Saccades orchestrate intraocular glucose dynamics to shape visual responses in birds

Domain: biology
Published: 2026-03-13
Editorial outcome: major_revision

Abstract
Birds exhibit remarkable vision despite lacking the classical retinal vasculature, raising an enigmatic question about how avascular retinas fulfill energy demands necessary for sight. Avian saccades couple rapid gaze shifts with cyclotorsional oscillations. In pigeons, we show that these saccades orchestrate intraocular metabolic dynamics and visual processing. Using combined measurements of eye movements, intraocular glucose, and neuronal activity in retinorecipient brain regions, we found that saccades induce intraocular glucose fluctuations that are closely linked to changes in visual responses over seconds to minutes. Pharmacologically manipulating glucose availability or eliminating saccadic oscillations produced corresponding changes in neuronal responses, demonstrating causality. Thus, stimulus-driven saccades not only serve visual exploration but also propel retinal metabolism, facilitating neuronal visual responses in the absence of retinal vasculature. These findings underscore an intrinsic interplay among eye movements, metabolic regulation, and visual function, offering insight into how oculomotor behavior supports retinal health and visual performance across species.

Introduction
Birds present a fascinating paradox in vertebrate evolution: despite exhibiting exceptionally high retinal metabolism among their brain regions 1,2, and possessing larger eyes 3,4, superior visual acuity 5-8, and thicker retinas than mammals 9-11, they entirely lack retinal blood vessels 12-14 that are essential for nutrient delivery in primates 15-17. This raises an intriguing enigma: how do avian eyes fulfill the intense metabolic activity of thick, high-acuity retinas in the absence of direct vascular support? Solving this puzzle requires uncovering the sophisticated strategies that birds have developed to sustain retinal health and visual performance without conventional vascularization. One potential mechanism centers on avian saccades, rapid, repetitive eye movements that combine a displacement of eye position and several cycles of cyclotorsional oscillation (Fig. 1a; Supplementary Fig. 1a) 18,19. Composing approximately 10% to 25% of total viewing time, these saccades have been recognized for orienting gaze and exploring the visual environment 20-28. Emerging evidence, however, suggests that avian saccades may also facilitate intraocular nutrient transport 29, by acting as mechanical “agitators”. Through repetitive motion, these saccades could propel glucose and other nutrients from the pecten oculi toward the retina, ensuring metabolic support to retinal neurons 4,30. The pecten oculi is a comb-like, pigmented vascular structure unique to birds (Supplementary Fig. 1b) and is considered homologous to the reptilian conus papillaris 31,32, possibly representing an ancient archosaur feature secondarily lost in mammals.

These striking properties of avian eyes led us to hypothesize that their interplay may create a dynamic system for visual processing, driven by three interconnected mechanisms. First, saccades are initiated for exploring visual information and sustaining retinal nutrient levels. Second, these eye movements facilitate the transport of metabolic substances, such as glucose 33, from the pecten oculi into the retina. Third, the resulting nutrient fluxes, especially the fluctuations in glucose levels, may dynamically modulate retinal function 34. Despite the compelling nature of these hypotheses, they have rarely been investigated as a cohesive entity, leaving critical gaps in our understanding of how avian eyes function without direct vascularization. Previous work has approached the avian saccade as an isolated, static event, by measuring the leakage of dye from the pecten following a single saccade 29. The challenge remains to establish cause-and-effect relationships among saccades, dynamics of intraocular metabolism, and changes in neuronal responses during viewing of visual stimuli.

Here, we address this challenge using an integrated series of experiments designed to test whether and how saccades drive fluctuations in intraocular glucose, and in turn, modulate visual responses in awake, head-fixed pigeons 35-37. We monitored eye movements and intraocular glucose levels while recording from neurons in three critical retinorecipient nuclei, key structures receiving direct input from retinal ganglion cells and essential for avian visual processing 22,38-45. We found that saccades induce fluctuations in intraocular glucose, and dynamically correlate with changes in neuronal responses over timescales. Furthermore, pharmacological manipulation of both intraocular glucose and saccades resulted in corresponding shifts in neuronal visual responses, establishing causal links. These findings provide direct causal evidence showing how saccades and glucose dynamics interact to shape visual processing in birds.

Results
Saccades elicited by visual inputs are linked to intraocular glucose perfusion
Birds, like primates, make saccades to explore and sample their visual environment 23-28. However, unlike primates, avian saccades are consistently accompanied by a burst of oscillations spanning tens to hundreds of milliseconds per event, with each oscillatory cycle lasting roughly 20 to 70 ms depending on species and individual variability (Fig. 1a and Supplementary Fig. 1c-h and Supplementary Videos 1-6). To examine how visual inputs influence avian saccades, we introduced four types of visual stimuli: light, dark, social mating videos and gray screens, and monitored eye movements (via electrooculography, EOG) in awake, head-fixed pigeons (Fig. 1b) 35-37. We quantified three frequency-related metrics: saccade frequency (saccade count per 1-minute window), oscillation frequency (the total number of oscillatory cycles across all saccades within the 1-min window), and intrasaccadic frequency (the reciprocal of an oscillatory cycle duration) (Fig. 1c). Across distinct visual conditions, both saccade and oscillation frequencies were significantly modulated (Fig. 1d, saccade: F = 45.26, p < 0.001; oscillation: F = 11.32, p < 0.001; one-way ANOVA), whereas intrasaccadic frequency remained unchanged (F = 0.79, p = 0.50). Post-hoc pairwise comparisons identified specific differences between visual conditions, and these findings were further validated using a linear mixed-effects model (LMM) to account for repeated measures from the same animal (Supplementary Fig. 2). Saccade and oscillation frequencies were overall positively correlated across animals and visual conditions (Fig. 1e, dark: r = 0.63, p < 0.001; light: r = 0.55, p < 0.001; gray: r = 0.63, p < 0.001; social video: r = 0.57, p < 0.001; Pearson correlation), yet their moment-to-moment variations were independent. Residual and cross-correlation analyses revealed no evidence of noise-driven coupling, indicating a shared trend with independent variability in response to visual inputs (all conditions: r ≈ 0, p = 1). Therefore, we focused on saccade and oscillation frequencies in the subsequent analyses.

Previous evidence suggests that such saccadic oscillations may facilitate intraocular glucose perfusion through mechanical agitation of ocular fluids 29. To investigate how visually-guided saccades influence metabolic regulation, we simultaneously monitored eye movements and intraocular glucose concentrations (via clinical-grade Continuous Glucose Monitoring, CGM) (Fig. 1b). The CGM system provided continuous, real-time glucose assessments (Supplementary Fig. 3a) with accuracy comparable to that of standard glucometers (Supplementary Fig. 3b), and did not significantly alter saccade (p = 0.63) or oscillation frequencies (p ≈ 1, n = 6; two-sided Wilcoxon signed-rank test; Supplementary Fig. 3c, d).

By employing 2-hour screen-brightness cycles (1 hour dark/1 hour light; Fig. 1f), we observed that pigeons adjusted their saccadic eye movements in response to luminance changes, accompanied by corresponding dynamic shifts in their intraocular glucose levels. After dark-to-light transitions, saccade frequency increased by 41.66 ± 6.95%, oscillation frequency by 25.62 ± 6.32%, and intraocular glucose levels by 3.35 ± 0.34% (p < 0.001, p < 0.001, p < 0.001, n = 6 pigeons; Fig. 1g-i). Conversely, light-to-dark transitions prompted reductions in saccade frequency (27.26 ± 3.14%), oscillation frequency (16.43 ± 2.60%), and intraocular glucose (2.88 ± 0.58%) (p < 0.001, p < 0.001, p < 0.001; Fig. 1k-n). Simultaneous interstitial glucose measurements ruled out systemic stress responses (dark-to-light: p = 0.08; light-to-dark: p = 0.41). A modified lag-correlation analysis revealed that intraocular glucose changes followed fluctuations in saccade and oscillation frequencies by approximately 3-4 minutes (Pearson correlation; Fig. 1j, o). To further validate this temporal relationship, we applied a standard-deviation (SD)-based threshold method to estimate response latency (see Methods for details). Consistent with the lag-correlation results, this analysis confirmed that glucose responses lagged behind saccade-related changes by roughly 3-4 minutes across animals (Supplementary Fig. 4).

To determine whether naturalistic visual conditions elicit comparable effects, pigeons were introduced with 30-minute conspecific social mating videos preceded by a randomly varying 30-40 minutes gray screen (Fig. 1p). Pigeons displayed robust increases in both saccadic eye movements and intraocular glucose levels. Across four individuals, saccade frequency, oscillation frequency, and intraocular glucose concentration increased by 7.70 ± 4.97%, 7.97 ± 4.90%, and 2.25 ± 2.80%, respectively, relative to the gray-screen baseline (p < 0.001, p < 0.001, p = 0.03). The modified correlation analysis revealed a similar temporal lag of approximately 3-4 minutes between glucose levels and both saccade and oscillation frequencies during the video-viewing condition (Fig. 1r). Together, these findings indicate that visually driven saccades in birds are associated with intraocular glucose mobilization that occurs with a temporal lag of several minutes, providing a delayed boost in nutrient supply during visual information gathering.

Saccades dynamically correlate with retinorecipient neuronal responses across timescales ranging from seconds to minutes
To explore whether saccades modulate neuronal visual responses, we targeted three crucial retinorecipient nuclei that receive direct retinal input and subserve distinct functions within the avian visual pathways. Using a large-field still grating that nearly filled the pigeons' right visual field, we simultaneously monitored eye movements and conducted extracellular recordings in these nuclei. To evoke robust visual responses, the grating periodically moved at 8°/s for 300 ms at random intervals (6 to 10 seconds), with motion direction optimized for each neuron’s preference (Fig. 2a; Supplementary Fig. 5). This stimulus design minimized interference from eye movements-related artifacts and well controlled retinal slip (Supplementary Fig. 6). In total, we recorded 127 neurons: 57 in the optic tectum (OT) of the tectofugal pathway, 20 in the nucleus opticus principalis thalami (nOPT) of the thalamofugal pathway, and 50 in the pretectal nucleus lentiformis mesencephali (nLM) of the accessory optic system (Fig. 2b). Notably, we analyzed only visual responses that recorded during stable fixation periods between saccades, when perisaccadic influences had fully subsided (see Methods for details). This criterion enabled systematic probing of visual responsiveness under steady fixation while eliminating perisaccadic modulations.

We next investigated how variability in visual responses of retinorecipient neurons related to preceding saccadic eye movements across timescales. Eye movements occurring over a series of analysis windows from 1 second to 8 minutes prior to grating motion onset were analyzed in 1-second increments (example neuron in Fig. 2a). Using a representative 3-minute analysis window as an example, both saccade and oscillation frequencies significantly correlated with neuronal visual responses (example neuron: saccade in Fig. 2c: r = 0.38, p < 0.001; oscillation in Fig. 2d: r = 0.22, p = 0.01; n = 120 trials, Pearson correlation). Because saccade and oscillation frequencies exhibited partially shared variability in response to visual stimuli (Fig. 1e), we utilized principal component analysis (PCA) to quantify their combined effects. PCA decomposed eye-movement metrics into principal components for each neuron (example neuron in Fig. 2e). Correlation analysis demonstrated a significant positive relationship between the first principal component (PC1) and corresponding neuronal visual responses (r = 0.33, p < 0.001; Fig. 2f). This indicates that higher saccade activity during the preceding period was associated with enhanced neuronal activity.

For population analyses, we focused on 54 out of 127 neurons that had at least 35 repetitions of the moving grating per analysis window, with window lengths extended up to 8 minutes. Across this population, retinorecipient neurons revealed correlations at both second- and minute-scale delays between their visual responses and preceding eye-movement dynamics, including saccade frequency, oscillation frequency (Supplementary Fig. 7) and the combined principal component of both measures (Fig. 2g, h). On a minute-scale, positive correlations emerged approximately 3-4 minutes before stimulus onset (p < 0.05; Supplementary Fig. 7a, c). Notably, this interval aligns with the delay observed between saccadic eye movements and intraocular glucose changes (Fig. 1). It suggests that saccades may modulate intraocular glucose delivery within this timeframe, thereby influencing retinal sensitivity and subsequent neuronal processing at higher visual regions. Conversely, on a second-scale, negative correlations appeared within the first 10 seconds, particularly pronounced during the initial 5-7 seconds (p < 0.01; Fig. 2g, i; Supplementary Fig. 7b, d), indicating a transient suppressive effect likely driven by one or two of the most recent saccades. Permutation tests confirmed significant correlations between neuronal responses and saccadic metrics, including their PC1, with both second- and minute-scale delays, indicating that saccade-related measures capture the most robust coupling across timescales (Supplementary Fig. 8).

To quantify these temporal dynamics in more details, we applied sliding-window analyses to compute time-lagged cross-correlations between saccade measures and visual responses (Supplementary Fig. 9). The resulting significance maps revealed that correlations for saccade frequency and the PCA-derived component were most pronounced within bands spanning around 0 s lags with ~150-250 s windows and extending to ~160 s lags with ~10-80 s windows (Supplementary Fig. 9a, e). Oscillation frequency showed weaker but comparable patterns, peaking around 0-20 s lags for 20-150 s windows (Supplementary Fig. 9c). These results indicate that neuronal responses are enhanced following approximately 3-minute periods of increased saccadic activity, corroborating the minute-scale coupling effect. While focusing on short time lags (0-7 s; Supplementary Fig. 9b, d, f), a transient negative correlation emerged near 0 s lag, consistent with the second-scale analyses. This effect rapidly diminished beyond ~1 s, reflecting the brief suppressive influence of the immediately preceding saccade.

To further dissect the second-scale effect, we examined how each neuron’s responses varied with the time elapsed since the latest saccade. As an example, one neuron’s visual responses were sorted by these intervals spanning up to 6 seconds and binned into three stages with equal ranges (Fig. 3a), revealing a linear increase in visual responses as a function of time since the prior saccade onset (r = 0.43, p < 0.001; Fig. 3b). Across stages, z-scored visual responses rose significantly (one-way ANOVA, F = 13.13, p < 0.001; Fig. 3c). Population-level analyses across the three visual nuclei consistently showed stronger visual responses in stage 3 than in stage 1 (OT: p < 0.001; nOPT: p = 5.1 × 10-3; nLM: p < 0.001; Fig. 3d-f), with the majority of neurons exhibiting this pattern (OT: 48/57, 84%; nOPT: 16/20, 80%; nLM: 36/50, 72%). Furthermore, visual responses were positively correlated with the time since the latest saccade (OT: r = 0.44, p < 0.001; nOPT: r = 0.47, p < 0.001; nLM: r = 0.36, p < 0.001; Pearson correlation; Fig. 3g-i). These effects persisted after regrouping intervals to ensure equal trial counts (Supplementary Fig. 10).

These second-scale effects may stem from the time required for glucose and other nutrients to diffuse across the retina after each saccade. If so, neurons whose receptive fields lie closer to the pecten should be modulated sooner, reflecting a gradient of glucose availability (Fig. 3j). To test this, we focused on OT neurons with receptive fields at different retinal locations. In the avian optic tectum or the mammalian superior colliculus, visual neurons receive topographically organized retinal projections 46,47. Recordings were obtained from an anterior region (A 5.00–6.00 mm; n = 33 neurons) with receptive fields near the pecten, and a posterior region (A 1.00–1.25 mm; n = 24 neurons) located farther away 40,47,48. Using a one-dimensional diffusion framework incorporating metabolic consumption and saccade-driven mechanical oscillation (see Methods for details), we estimated that glucose reaches anterior retinal regions more rapidly following saccades, generating distinct temporal profiles of nutrient availability across the retina. The model further predicted phase-dependent fluctuations in glucose concentration, producing asynchronous peaks between proximal and distal retinal zones (Fig. 3k). Indeed, visual responses in both anterior and posterior OT neurons tracked these estimated glucose dynamics (Fig. 3l, m). Compared with posterior OT neurons, anterior OT neurons exhibited significantly stronger visual responses across stages (48.04 ± 4.95 spikes/s vs. 24.24 ± 4.35 spikes/s, mean ± SEM, p < 0.05). These findings support a diffusion-based mechanism for the second-scale modulation of visual responses, governed in part by the spatial proximity of receptive fields to the pecten and saccade-induced perfusion dynamics.

Under the second-scale modulation, longer saccades encompassing more oscillatory cycles may induce stronger mechanical agitation, thereby enhancing visual responses. To test this possibility, we incorporated saccade duration and the time elapsed since the latest saccade into partial correlation and linear regression analyses across 54 neurons, controlling one variable while examining the other. Significant positive correlations persisted between visual responses and the time since the latest saccade when controlling for saccade duration or oscillatory cycle count (saccade duration: r = 0.05, p < 0.001; oscillatory cycle count: r = 0.05, p < 0.001; multiple linear regression; Supplementary Fig. 11a, b), and between visual responses and saccade duration or oscillatory cycle count when controlling for the time since the latest saccade (saccade duration: r = 0.37, p = 0.04; oscillatory count: r = 0.02, p = 0.04; multiple linear regression; Supplementary Fig. 11c, d). These findings indicate that saccade duration, or oscillatory cycle count, and the post-saccadic interval each independently contribute to the short-timescale modulation of visual responsiveness, supporting the interpretation that saccades could transiently enhance ocular glucose transport, thereby boosting retinal metabolic supply and visual signal transmission within seconds after saccades.

Intraocular glucose modulation alters visual responses
To investigate how intraocular glucose regulates neuronal visual responses, we locally manipulated intraocular glucose concentrations while simultaneously recording neuronal activity and monitoring eye movements in pigeons. Initially, intraocular glucose levels were elevated via intravitreal microinjections of D-glucose (1 mol/L, 2 μL; see Methods), raising intraocular glucose from 15.36 ± 0.40 to 17.69 ± 0.53 mmol/L (mean ± SEM, n = 9, p = 3.9 × 10-3). Due to spatial limitations in our recording setup, we focused on retinorecipient neurons in the nLM and measured visual responses to 300 ms grating-motion stimuli (Fig. 4a). In an example neuron, visual responses increased from 23.70 ± 13.22 to 70.23 ± 17.70 spikes/s within 2 minutes post-injection (mean ± SD, p < 0.001; Fig. 4c, d). Across all nine examined nLM neurons, D-glucose administration significantly increased visual responses (p = 7.8 × 10-3; Fig. 4g). The enhancement emerged within approximately 15 seconds (p < 0.05), suggesting a rapid increase in local glucose availability likely facilitated by several saccades occurring over tens of seconds that boosted visual activity. Control experiments with L-glucose and saline injections revealed no significant changes in visual responses on the example neuron (Fig. 4e, f) or across the population (L-glucose: p = 0.30, n = 9 neurons, Fig. 4h; saline: p = 0.58, n = 7 neurons, Fig. 4i). The order of these injections (D-glucose, L-glucose, and saline) was randomized for each examined neuron.

We next explored the effects of decreasing intraocular glucose by inhibiting glucose transporter 1 (GluT-1; Fig. 4b), a primary glucose transporter highly expressed in the avian pecten oculi 49,50. Intravitreal administration of the GluT-1 inhibitor Bay-876 (100 μmol/L, 1 μL) 51-53 effectively reduced glucose concentrations from 15.82 ± 0.34 to 11.52 ± 1.31 mmol/L (mean ± SEM, p = 2.0 × 10-3, n = 10 pigeons). This decrease led to reduced visual responses, as illustrated by an example neuron, whose firing dropped from 85.67 ± 24.88 to 47.10 ± 16.21 spikes/s (mean ± SD, p < 0.001, Fig. 4p, q). Population analyses of 10 neurons confirmed that lowering intraocular glucose significantly diminished visual responses (p = 9.8 × 10-3; Fig. 4r).

None of these microinjections affected saccade or oscillation frequencies (Fig. 4j-o, and Fig. 4s, t) or neuronal spontaneous firing rates (p > 0.05). These findings demonstrate that pharmacological manipulation of intraocular glucose, either elevating or reducing it, robustly influences neuronal visual responses. They underscore the critical role of glucose in retinal function and its potential as a key modulator of sensory processing in visual systems.

Muscimol-induced suppression of saccades unmasks glucose-driven modulation of visual responses
To determine how saccades modify neuronal visual responses through changes in glucose levels, we suppressed these eye movements by injecting the GABA receptor agonist muscimol into the raphe complex, key brainstem nuclei for saccadic oscillations in pigeons 20,22. Concurrently, we monitored intraocular glucose concentrations and neuronal activity in response to visual stimulation (Fig. 5a). After muscimol injection, both saccade and oscillation frequencies were significantly reduced (saccade frequency: pre-injection, 0.28 ± 0.04 Hz; post-injection, 0.02 ± 0.01 Hz, p < 0.001; oscillation frequency: pre-injection, 1.47 ± 0.27 Hz; post-injection, 0.04 ± 0.01 Hz, p < 0.001; mean ± SEM, n = 6 pigeons; Fig. 5b, c). These reductions were accompanied by a significant decrease in intraocular glucose, from 16.00 ± 2.01 to 11.45 ± 2.08 mmol/L (p < 0.001; Fig. 5d). Utilizing the modified lag correlation analysis, we observed that subsequent drops in intraocular glucose lagged behind the reduction in saccade frequency by 3.4 minutes and oscillation frequency by 3.7 minutes (Fig. 5e). Independent estimation of glucose latency using an SD-based threshold confirmed a ~3-minute delay of glucose changes relative to saccade activity (Supplementary Fig. 4f). These results closely match the temporal lag observed in earlier results (Figs. 1, 2).

Suppressing saccadic oscillations with muscimol significantly attenuated eye movements and visual responses, as demonstrated by an example neuron (Fig. 5f, g), and confirmed across all three examined nuclei (Fig. 5h-j). In the OT, neuronal activity to visual stimuli decreased from 44.10 ± 5.10 to 38.22 ± 4.81 spikes/s (mean ± SEM, p = 0.01, n = 14 neurons; Fig. 5h). Similarly, in the nOPT, visual responses dropped from 22.74 ± 4.54 to 12.59 ± 4.35 spikes/s (p = 6.8 × 10-3, n = 11 neurons; Fig. 5i), and in the nLM, declined from 77.89 ± 10.12 to 63.15 ± 8.40 spikes/s (p = 5.4 × 10-3, n = 15 neurons; Fig. 5j). In contrast, muscimol had no significant impact on spontaneous neuronal firing (p = 0.67, p = 0.08, p = 0.12). The reduction in visual responses began approximately 4.3 minutes after muscimol injection across 40 neurons (p < 0.001). Considering the 0.5-0.7-minute delay between injection and the suppression of saccades (Supplementary Fig. 4f), this corresponds to a comparable 3-minute lag between reduced saccade frequency and the onset of neuronal inhibition. Thus, within minutes after saccades cease, both glucose supply and neuronal activity decline almost simultaneously, linking saccade-driven perfusion to visual responsiveness. Together, these findings underscore the critical role of saccades in regulating intraocular glucose and modulating visual responses in retinorecipient neurons. This interplay between eye-movement dynamics and metabolic regulation provides mechanistic insight into how avian retinas sustain high metabolic demands to maintain optimal visual processing.

Methods
Animal preparation
We performed experiments on forty-seven awake, head-fixed 35-37 adult pigeons (Columba livia, body weight 300-500 g; both sexes; wild-type). All animals were purchased from local dealers and housed at the Institute of Biophysics, Chinese Academy of Sciences, under a 12 h-12 h light-dark cycle. Pigeons were randomly assigned to two sets of behavioral and physiological studies. In the first set, ten pigeons were utilized to investigate saccadic eye movements while viewing social mating videos or alternating light/dark screens; intraocular glucose levels were continuously monitored during these sessions. In the second set, the remaining thirty-seven were evaluated for neuronal visual responses to grating motion stimuli presented in a dimly lit room, associated with pharmacological manipulations. Among them, eighteen animals received muscimol injections targeting the raphe nucleus, nine animals were administrated intraocular injections of D-glucose, L-glucose and saline in a randomized order; and the remaining ten animals were given an intraocular injection of Bay-876, a potent and selective GluT-1 inhibitor. In the first set of experiments (Fig. 1 and Supplementary Fig. 1), we broadened the evolutionary scope by including four additional adult avian individuals from different species (all wild-type): a budgerigar (Melopsittacus undulatus), a chukar partridge (Alectoris chukar), a chicken (Gallus gallus domesticus), and a muscovy duck (Cairina moschata), which diverged tens of millions of years ago 58, to assess their saccadic eye movements. Sex was randomly selected and not included as a biological variable in the study design.

Each animal was anesthetized by injecting ketamine (30 mg/kg) and dexmedetomidine (0.125 mg/kg) into the pectoral muscles. The anesthetic status was monitored by examining their respiration patterns and reflex reactions to toe pinching 20,22,59. Pigeons were wrapped with soft straps and positioned into a stereotaxic apparatus. After a scalp incision, a lightweight steel head holder was affixed to the skull to ensure stable head restraint in experiments. Guided by stereotaxic coordinates of the pigeon brain 60, recording windows over the left tectum, the left telencephalon, as well as a pharmacological injection window over the cerebellum were surgically exposed by a dental drill. The overlying dura mater remained intact. We also gently drilled a little fragment of the orbital bone (1 × 1 mm) located on the top of the right eye, for the purpose of intraocular glucose examinations and intraocular injections. After surgery, the scalp was sutured and prescribed erythromycin ointment (BAIJINGYU Pharmaceutical, Nanjing, China). Pigeons were housed individually and received Meloxicam (0.5 mg/kg, PO, once or twice daily) to alleviate postoperative pain and ceftriaxone sodium (75 mg/kg, IM, every 6-8 hours) to prevent infection for 3-7 days. Within 12-24 hours, their locomotion, feeding, and hydration behaviors had resumed. On experimental days, pigeons received a light dose of ketamine (4 mg/kg), were coated in a soft bag, and situated on a foam couch. The head was secured to the stereotaxic apparatus via the implanted head holder. Wound margins and surrounding muscles were periodically infiltrated with lidocaine (Shanghaizhaohui Pharmaceutical, China). Experiments commenced at least 30 min after light ketamine sedation when the animals had recovered, consistent with a prior report 61. All neuronal recordings were performed in awake, head-fixed pigeons 35-37 with unrestricted eye movements. During neuronal recordings, the left eye was covered while the right eye was exposed to visual stimuli. For monitoring eye movements in the budgerigar, chukar partridge, chicken and duck, animals underwent the same anesthesia and head-fixation procedures. During the experiments, animals were not provided with food or water; however, they had free access to both in their home cages before and after each session.

All procedures in this study were in strict accordance with the guidelines for the care and use of animals established by the Society for Neuroscience. All of the animals were handled according to protocols approved by the Institutional Animal Administration Committee at the Institute of Biophysics, Chinese Academy of Sciences (IBP-P-001(21) and IBP-P-001(24)).

Visual conditions
The study introduced three types of visual stimuli to the animals, programmed using Psychtoolbox-3 (version 3.0.18) and executed within the MATLAB platform (R2016b, MathWorks Inc., USA). Mating videos and light/dark stimuli were presented on two 27-inch computer screens (Q27P2, AOC, China) positioned around the viewer’s head (Fig. 1b). In the video experiments, pigeons were first presented with a gray screen for a randomly varying period of 30-40 minutes, followed by a 30-minute presentation of mating videos (Fig. 1p). For light/dark stimuli, the screens alternated between white (RGB: 255, 255, 255) and black (0, 0, 0) at 60-minute intervals (Fig. 1f, k). During white-screen presentations, the luminance at the animal’s eye position was measured at 199.3 ± 0.7 lux, while black-screen presentations had a luminance of 3.0 ± 0.0 lux (LX-1330B lux meter, Tondaj, China). Each behavioral session lasted approximately 3-4 hours per experimental day.

To investigate neuronal visual responses, the visual stimuli used were full-field square wave gratings with a spatial frequency of 0.16 cycles per degree. The grating was displayed on a large screen (130º × 140º) positioned 50 cm from the viewer’s right eye using an EPSON CB-535W projector. The grating field’s horizontal and vertical meridians were rotated by 38° to match the pigeon’s natural viewing conditions 62,63. The luminance of black and white stripes of grating was 0.1 and 6.6 cd/m2, respectively. The orientation of each grating was orthogonal to its direction of motion. Gratings were presented for 300 ms, moving at 8°/s in one of four cardinal directions: temporal-to-nasal (T–N), nasal-to-temporal (N–T), dorsal-to-ventral (D–V), or ventral-to-dorsal (V–D) 20. For each neuron, the direction of motion was customized to evoke the strongest visual response (Supplementary Fig. 5). Grating motions lasting 300 ms were randomly introduced with inter-trial intervals ranging from 6 to 10 seconds. These motion stimuli were designed to probe neuronal responses while preventing the induction of saccadic or optokinetic nystagmus eye movements (Supplementary Fig. 6).

Pharmacological injections
To administer muscimol into the raphe complex, a Drummond™ PCR micropipette (1 to 10 µL, Thermo Fisher Scientific, USA) for microinjection using a PE-21 system (Narishige, Japan) and filled with muscimol (2%, PhytoLab, Germany). Accurate targeting of the raphe complex was achieved by initially inserting a glass-insulated tungsten microelectrode into the target area according to stereotaxic coordinates (from the interaural midpoint, P 0.25 mm, L 0.00 mm). The raphe complex was identified by the presence of well-characterized omnipause neurons, which exhibit a high tonic discharge rate that ceases during saccades in any direction 20,22,64. Next, the injection micropipette was connected to a gas-pressure microinjection system (Parker Picospritzer III, USA) and advanced into the raphe complex along the same trajectory as the microelectrode. Muscimol was administered in volumes of 20-50 nL. Following injections, pigeons exhibited cessation of saccadic oscillations within approximately 1 minute following the injection (Fig. 5f), with normal saccadic activity resuming after roughly 1.5 hours.

A custom-developed delivery system was used for the intraocular injections. This system included a sterilized 34-gauge needle connected to a microinjection pump (Zibo Guanjie Electronic Technology, China) via a 1-mL syringe. The microinjection pump was controlled automatically using Spike2 software (version 8.24, Cambridge Electronic Design Limited, UK), enabling precise programming of injection timing and speed. The needle was carefully inserted into the eyeball through a surgically drilled window on the orbital bone, ensuring its tip was positioned in the intraocular fluid. During distinct pharmacological experiments, the following substances were injected intraocularly: D-glucose (1 mol/L, 2 μL, Sinopharm Chemical, China), L-glucose (1 mol/L, 2 μL, Sigma-Aldrich, USA), saline (2 μL, CSPC PHARMA, China), and Bay-876 (100 μmol/L, 1 μL, MedChemExpress, USA). The insertion of the microinjection apparatus itself did not significantly alter saccadic eye movements (saccade: p = 0.84; oscillation: p ≈ 1, n = 6, two-sided Wilcoxon signed-rank test; Supplementary Fig. 3e, f).

Electrooculogram recording (EOG)
An electrooculogram system was employed to monitor eye movements 20,22,65,66. Two EOG electrodes were carefully implanted into the anterior and posterior regions of the orbital arch, while a third electrode was positioned on the occipital bone as a reference. The eye movement signals were band-pass filtered with a range of 0.1 to 500 Hz (A-M Systems, Microelectrode AC Amplifier Model 1800, USA). Subsequently, the signals were sampled at a rate of 1000 Hz and stored for offline analysis (CED Power1401, Cambridge Electronic Design Limited, UK).

Assessments of glucose levels
In this study, assessments of glucose levels involve measuring the concentration of glucose in intraocular fluid and interstitial fluid of pectoral muscles, via the Continuous Glucose Monitoring (CGM) systems (Freestyle Libre 2 CGM System, Abbott, USA; GS1 CGM System, SIBIONICS, China) and the glucometer (Accu-Chek Active, Roche, Switzerland). The CGM was conducted using commercially available glucose detection probes to monitor glucose concentrations in the eye and body over hours. The probe was inserted into the eyeball through the surgical window in the orbital bone, landing in the intraocular fluid. The transmitter was secured to the skull using a UV-curing adhesive (ergo 8500 Metal, Kisling AG, Switzerland). A small amount of medical petroleum jelly (Lircon, China) was used to seal the exposed area. A second glucose monitoring probe was inserted into the pectoral muscle and fastened by adhesive tape. These probes are equipped with flexible micro-detection heads that prevent damage to animals’ tissue and have a minimal impact on their eye mobility.

We assessed the temporal sensitivity and accuracy of CGM systems to detect changes in glucose concentration. To evaluate temporal sensitivity, a higher concentration glucose solution (1 mL, 15 mmol/L) was introduced into the testing glucose environment (20ml, 5mmol/L) 20 seconds prior to sampling. Subsequent samples were analyzed to determine the system’s capability to detect glucose fluctuations. The response was quantified using normalized readings, calculated as CGM change divided by actual concentration change (Supplementary Fig. 3a). For accuracy evaluation, CGM measurements were validated against reference values obtained from a glucometer across glucose concentrations ranging from 2.5 mmol/L to 20 mmol/L, in increments of 2.5 mmol/L (Supplementary Fig. 3b).

Extracellular recordings
The single-cell recordings utilized custom-made glass-insulated tungsten microelectrodes, which had impedances ranging from 1 to 3 MΩ. The microelectrodes were laterally inserted into the left tectum to specifically target the OT (A 5.00-6.00 mm) and the nLM (A 5.50-6.00 mm, L 4.00 mm; H 5.50-6.00 mm), and vertically introduced through the left telencephalon to target the nOPT (A 6.5-7.0 mm, L 2.8-3.2 mm, H 7.0 mm). The neuronal signals underwent filtration using a band-pass filter with a range of 300 to 5,000 Hz (A-M Systems, Microelectrode AC Amplifier Model 1800, USA). These signals were then sampled at a rate of 25 kHz (CED Power1401, Cambridge Electronic Design Limited, UK). The process of spike sorting was conducted offline using the Spike2 software (Cambridge Electronic Design Limited, UK).

A photocell (6.5 cm × 6.5 cm, Shenzhen Kobry Technology, China) was attached to the screen. It has the capability to directly capture the temporal information of visual stimuli displayed on the screen at a sampling rate of 1,000 Hz by Spike2 software. The signal was simultaneously recorded with both behavioral and neuronal responses, allowing for precise data analysis.

Saccade video recordings
Saccadic eye movements were assessed in five avian species: pigeon, budgerigar, chukar, chicken, and duck. Birds were observed while awake and head-fixed 35-37 under standard room-light conditions. Additionally, a separate video was recorded from a pigeon resting quietly with its head free to move. A high-speed camera (120Hz, HONOR Magic6 Pro, China) equipped with an adjustable macro lens (focal length: 30 to 70 mm, MECORIGHT, China) was used to record their saccadic eye movements.

Retinal tissue preparation and imaging
A pigeon and a chukar were randomly selected for postmortem retinal imaging. Animals were euthanized by the injection of sodium pentobarbital (200 mg/kg) into the pectoral muscles. After euthanasia, the eyes were enucleated using ophthalmic scissors. Then, a sagittal incision was made along the equator of each eyeball using a sterile scalpel blade to expose the retinal tissue. Images were acquired using the same HONOR Magic6 Pro camera under controlled illumination to minimize glare.

Quantification of saccadic eye movements
A typical saccadic eye movement in birds consists of several oscillations occurring at frequencies of approximately 18 to 44 Hz. Saccades were identified using a customized detection algorithm implemented in MATLAB. Raw EOG signals were band-pass filtered (5-60 Hz) to remove noise and drift, and peaks and troughs exceeding three times the baseline standard error were detected using MATLAB’s findpeaks function. Consecutive oscillatory cycles were merged to determine the onset and offset of each saccade. The algorithm was described previously 65 and is publicly available at: https://cdn.elifesciences.org/articles/78729/elife-78729-code1-v1.zip. All detections were subsequently manually verified to ensure accuracy.

To characterize the temporal organization of avian saccadic eye movements, three frequency-related metrics were defined (Fig. 1). Saccade frequency was calculated as the number of detected saccades within each analysis window, representing the rate of occurrence of oscillatory bursts. Oscillation frequency was defined as the total number of oscillatory cycles across all saccadic bursts within the analysis window, representing the overall temporal density of oscillatory activity. Intrasaccadic frequency was defined as the inverse of the duration of each oscillatory cycle, reflecting the temporal frequency of oscillations within individual saccade bursts. Data points lying more than two standard deviations from the mean were considered outliers and excluded.

To quantify the joint influence of saccade and oscillation frequencies, which exhibited partially shared variability, we performed a principal component analysis for each neuron. The first principal component (PC1), capturing the maximal shared variance, was used as a single index of overall saccadic activity. We then computed Pearson correlations between this PC1 and neuronal visual responses across preceding time windows, incrementally ranging from 1 s to 8 min (Fig. 2g, h). To assess the statistical significance of these correlations, we implemented two permutation-based validation procedures following established methods (Supplementary Fig. 8) 67. First, a within-session (“pseudosession”) test was conducted by generating surrogate time series of trial onsets for each neuron through random shuffling of the original inter-trial intervals. All correlations were recalculated using these surrogate event times (n = 100 shuffles) to derive a null distribution. Second, a between-session (“session permutation”) test was performed by randomly pairing the complete visual response time series from one neuron with the complete PC1 time series from another neuron (n = 1,000 permutations). The 95% confidence intervals of these null distributions served as thresholds for statistical significance. We further systematically examined the temporal relationship between saccadic metrics and neuronal responses by combining sliding-window analyses with time-lag assessments (Supplementary Fig. 9).

Glucose levels
The CGM system continuously monitored animals’ glucose concentrations. To align with the data of saccade and oscillation, glucose levels were interpolated using a linear method between two consecutive sampled data points. In the light/dark experiments, glucose levels were averaged across three or more repetitions per animal together with corresponding saccadic measures. In the video experiments, only the first transition from the gray screen to the social video was analyzed, when the animals were naïve to such stimuli. To ensure consistency, the values were normalized to the mean glucose level observed during the 30-minute baseline period preceding the video, transition of the screen or the administration of muscimol.

Correlation between saccades and intraocular glucose levels
To investigate the temporal relationship between eye movements and intraocular glucose dynamics, saccade frequency and oscillation frequency were computed within 1-min time windows with a step of 0.1 minute and normalized to baseline. To match this sampling rate, intraocular glucose concentrations were linearly interpolated for further analyses. Data were averaged across trials for each animal and subsequently pooled across individuals.

A lag correlation analysis was then performed to assess temporal coupling between eye movements and intraocular glucose levels. Because glucose fluctuations evolve on a slower timescale than saccadic eye movements, we applied a modified cross-correlation approach to minimize spurious correlations arising from slow drifts and strong autocorrelation. Both signals were z-score normalized to standardize amplitude, followed by linear detrending to suppress slow variations and reduce artificial correlations at negative lags. The cross-correlation function was computed with temporal resolution matched to the sampling interval used for cross-animal averaging. To determine statistical significance, a null distribution was generated by randomly shuffling one signal relative to the other 1,000 times. Observed correlation values at each lag were compared with the 95% confidence interval of the null distribution, and only values exceeding this range were considered significant. Positive lags indicate that glucose changes followed saccadic activity, whereas negative lags indicate the reverse.

To further validate the lag interval between intraocular glucose concentrations and saccades, we applied a standard-deviation (SD)-based threshold method (Supplementary Fig. 4). For each signal, a 30-min period preceding the state change (visual stimuli switch or muscimol injection) was defined as the baseline. The mean and standard deviation of the baseline period were calculated for each pigeon, and the response threshold was set at 2.5 times the SD beyond the baseline mean (Threshold = mean ± 2.5 × SD). Starting from the onset of the state change, we examined the subsequent time series and identified the first time point at which the signal exceeded or fell below this threshold. This time point was defined as the response latency for each individual, and the mean latency was then averaged across animals. Because visual scenes during the social-mating video sessions were highly variable and fragmented, eliciting enhanced yet dynamically fluctuating eye movements, the SD-based latency analysis was applied only to the light/dark screen and pharmacological conditions. Under these conditions, visual stimuli were consistent and uniform, producing stable and well-defined eye-movement patterns that allowed reliable latency estimation.

Neuronal visual responses
We successfully identified firing activity in 127 neurons from three retinorecipient structures in the visual pathways of birds. Neuronal firing rates were computed using established methods 68 and averaged in 50-ms bins. In order to eliminate the influence of perisaccadic modulations on visual response analyses, we first quantified the temporal extent of these modulations by aligning neuronal firing rates to the onset and offset of saccades for each neuron. The duration of perisaccadic modulation was determined using a standard-deviation-based criterion, identifying the time point where the firing rate consistently returned to the stable baseline within a specified statistical tolerance. All visual response data within these modulation time frames were excluded, thereby restricting analyses to stable fixation periods and isolating them from perisaccadic modulations.

In this study, a 300 ms grating motion served as a probe to assess neuronal visual responses. We calculated the average spontaneous firing rates during saccade intervals without probes, aligned to the onset of the prior saccade. For each probe, the mean spontaneous firing rate within a 300-ms window, aligned to the probe timing relative to the prior saccade, was used as the baseline. Visual responses to the grating motion were then defined by subtracting this baseline. To assess the relationship between visual responses and the prior saccade, we calculated the distribution of time intervals between the two events, excluded the 5% extreme values and divided the remaining intervals equally into three stages. We then compared neuronal visual responses across these stages (Fig. 3), as well as before and after micro-injections (Fig. 4). In the muscimol administration experiment, saccades were suppressed, and the average firing rates within a 100 ms time interval preceding the grating motion were calculated as a baseline for comparing neuronal visual responses before and after muscimol administration (Fig. 5). To assess post-injection latency, neuronal activity was aligned to injection onset and analyzed in 0.5-min windows. Neuronal visual responses were z-score normalized to the pre-injection baseline (excluding outliers exceeding 3 standardized MAD), and latency was defined as the first window showing a significant deviation from baseline.

Modeling of intraocular glucose dynamics
To approximate intraocular glucose perfusion in pigeons, we developed a simplified one-dimensional diffusion model incorporating both metabolic consumption and saccade-driven mechanical oscillations. This framework was inspired by Pettigrew’s classic study in the tawny frogmouth, which showed that fluorescein dye can be rapidly transported ~2-3 mm from the pecten within 2 s of a single saccade 29, whereas passive diffusion during fixation is negligible (< 0.5 mm even after 120 s). These findings suggest that saccade substantially enhances nutrient delivery from the pecten. Given the complex three-dimensional nature of ocular fluid flow and metabolism, we reduced the system to a one-dimensional diffusion domain extending from the pecten to the retinal periphery. The glucose concentration c(x, t) over space x and time t follows Fick’s second law with an additional consumption term R and an oscillatory flow component v(x, t):
𝜕𝑐/𝜕𝑡 = 𝐷(𝜕²𝑐/𝜕𝑥²) - 𝑅 + 𝑣(𝑥,𝑡)(𝜕𝑐/𝜕𝑥) (1)
where D is the diffusion coefficient, and v(x,t) models periodic fluid motion driven by saccadic oscillations. Under the small-amplitude approximation and Taylor dispersion theory, the concentration at positions a and b can be expressed as:
𝑐(𝑥,𝑡) = 𝑐₁ + (𝑐₂-𝑐₁)𝑥/𝐿 + 𝑅(𝑥²-𝐿𝑥)/2𝐷ₑ𝒻𝒻 + 𝐴𝜔𝑒⁻ᵅˣ [ (𝑐₂-𝑐₁)/𝐿 + 𝑅(𝑥-𝐿/2)/𝐷ₑ𝒻𝒻 ] cos(𝜔𝑡-𝑘𝑥+𝜙₀)/𝐷𝑘² + 𝐴𝜔²𝑒⁻ᵅˣ [ (𝑐₂-𝑐₁)/𝐿 + 𝑅(𝑥-𝐿/2)/𝐷ₑ𝒻𝒻 ] sin(𝜔𝑡-𝑘𝑥+𝜙₀)/𝐷𝑘³ (2)
The effective diffusion coefficient incorporating the oscillatory component is given by:
𝐷ₑ𝒻𝒻 = 𝐷 + (𝐴𝜔)² / [2𝐷𝑘²(1+(𝜔/(𝐷𝑘²))²)] (3)
Parameter values were based on published physiological data. The pigeon eye was modeled as a sphere of radius L = 7 mm (ocular diameter of ~13-14 mm) 69. Two retinal locations were examined: a proximal site 1 mm from the pecten (position a) and a distal site 4 mm away (position b). Blood glucose levels in birds typically range between 13-15 mmol/L; we therefore set c₁ = 15 mmol/L at the pecten and c₂ = 13 mmol/L at the retinal edge 70. The glucose consumption rate R was estimated from pigeon brain metabolism (27.29 ± 1.57 μmol glucose per 100 g per min ≈ 4.55×10⁻⁴ mmol·L⁻¹·s⁻¹) 2. The diffusion coefficient of glucose in water at 39 °C is 0.92×10⁻⁹ m²/s; corrected to 40 °C using the Stokes–Einstein relation and adjusted for vitreous medium (~twofold reduction), this yields D = 4.68×10⁻⁴ mm²/s 71,72. The oscillatory term was parameterized by the saccade frequency distribution. Using the 95% confidence bound of inter-saccade intervals (7.57 s in our data), the mechanical oscillation frequency was set to f = 0.13 Hz. Based on this frequency, the wave number (k = 12.81 rad/mm), angular frequency (ω = 0.83 rad/s) and phase (φ₀ = 0.97π rad) were used to determine the relative timing of proximal and distal glucose oscillations. The oscillation amplitude A was taken as one-tenth of the system length (0.7 mm), and the decay coefficient as α = 1×10⁻³ mm⁻¹.

Numerical simulations revealed temporal glucose concentration profiles at both retinal sites a and b. The saccade introduced phase-dependent fluctuations and concentrations, producing asynchronous glucose peaks between proximal and distal locations after a saccade (Fig. 3). These dynamics qualitatively match experimentally observed differences in neuronal responses within the optic tectum, supporting the hypothesis that saccades modulate retinal metabolism through pecten-driven perfusion. While the present model simplifies the three-dimensional fluid mechanics of the avian vitreous, it could provide the key temporal and spatial trends of glucose distribution within the eye. Estimated glucose signals were normalized by dividing each waveform by its peak-to-peak amplitude at the corresponding recording site.

Statistical analysis
A two-sided Wilcoxon signed-rank test was utilized to assess behavioral and neuronal visual responses for paired data, whereas the Wilcoxon rank-sum test was applied to independent observations. To examine the effect of visual conditions on eye movement metrics (Fig. 1), we performed one-way Analysis of Variance (ANOVA) followed by Tukey’s HSD post-hoc tests for pairwise comparisons. To further account for the non-independence of repeated measurements within animals, we fitted a linear mixed-effects model for each frequency metric, with the visual condition as a fixed effect and pigeon identity as a random intercept (Frequency ~ Condition + (1 | Pigeon ID)). Post-hoc comparisons for the LMM analysis were conducted using Wald tests.

Visual responses across the three stages following saccades were compared using a one-way analysis of variance (one-way ANOVA). Partial correlation (Supplementary Fig. 11) was used to assess the independent contribution of correlated variables (saccade duration, oscillatory cycle count, and time since last saccade) to visual responses. The Holm-Bonferroni correction was applied to control for multiple comparisons. Statistical analysis was conducted with a significance level of 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Xu et al. have submitted an excellent study on a core question on avian visual physiology. I want to congratulate the colleagues on their fine paper. I have only a couple of remarks and questions that might help to further increase the quality of their manuscript.\nIntroduction\nLines 38-39: Indeed, birds have higher metabolic demands, but their neurons run on “cheap fuel” since they require only ca. 1/3 glucose per neuron, compared to mammals (Von Eugen et al., Current Biol., 2022).\nAs far as I know, the reptilian equivalent to the avian pecten is the conus papillaris. It might be worth to alert the reader that this structure is possibly an ancient archosaur feature that might be secondarily lost in mammals.\nOverall\nIt is bit confusing that saccades to bring an interesting object into the projection of the retinal area of enhanced vision vs. oscillatory saccades with small cyclotorsional rotations are not properly differentiated. Do the authors imply that they occur in combination? Would cyclotorsional rotations not blur the image? Are the concomitant activity patterns in the visual nuclei a potential counterstrategy to increase temporal resolution to impede blurring?\nMethod\nWere the animals only slightly sedated during recordings or deeply anaesthetized?", "reviewer_2": "Reviewer #1 (Remarks to the Author):\nXu et al. have submitted an excellent study on a core question on avian visual physiology. I\nwant to congratulate the colleagues on their fine paper. I have only a couple of remarks and\nquestions that might help to further increase the quality of their manuscript.\nAuthors: We sincerely thank you for the positive and encouraging assessment of our work. We\nare delighted that you recognize our study as addressing a core question in avian visual\nphysiology. Your supportive comments greatly reinforce our confidence in the novelty and\nsignificance of our findings. We are also very grateful for your thoughtful remarks and\nconstructive suggestions", "reviewer_3": "Summary\nOscillatory saccades are high frequency (~10-40 Hz) eye movements that often accompany translational eye saccades in\nbirds. Xu et al. hypothesize that (1) oscillatory saccades are initiated during visual behaviors that require higher nutrient\nlevels in the retina; (2) these saccades distribute nutrients (specifically glucose) in the eye, and (3) this increased glucose\ndistribution improves retinal processing of images.\nIn support of (1), they show an immediate increase in saccade frequency and oscillation frequency (although these terms\nrequire clarification) upon salient changes in visual input. In support of (2), they find that intraocular glucose concentrations\nincrease ~3 minutes later and demonstrate causality by pharmacologically supressing saccades. In support of (3), they\nrecord increased neural responses to visual stimuli in retino-recipient nuclei also ~3 min after periods of increased\nsaccades. They again provide some evidence for causality through intraocular glucose injections which increase neural\nresponses, and suppression of saccades which decrease neural responses.\nThe neural data, intraocular glucose recording, and simultaneous causal manipulations are technically challenging and are\nof particular interest. The main issues revolve around better contextualizing the specific role of oscillatory saccades, and\nimproving the analysis and interpretation of temporal correlations between saccades, glucose concentrations, and neural\nactivity. Overall, the authors provide convincing evidence for a link between oscillatory saccades, glucose distribution, and\nvisual processing. The result is a fascinating example of how a neural system solves a challenging metabolic problem, and\nshould therefore be of broad interest.\nMajor points\n1. The manuscript depends on two key behavioral metrics: \"saccade frequency\" and \"oscillation frequency\". These metrics\nare poorly defined and the discussion around them is unclear.\n- The introduction lumps all avian saccades together. For example, Line 48 and 81-82- I don't believe anyone has suggested\nthe oscillatory component of avian saccades specifically is important for stabilizing the visual scene. How could ~30 Hz\ntorsional movements possibly be used for image stabilization? The connection between translational gaze shifting saccades\nvs. oscillatory saccades should be more clear, especially given this groups previous work on the topic. Do they always occur\ntogether? If not, which behaviors are more associated with one than the other? What is the impact of head fixation?\n- In the results, the measures of \"saccade frequency\" and \"oscillation frequency\" are never clearly defined. Does the former\nrefer to inter-saccade rate and the latter to intra-saccade oscillation rate? It is also unclear if the authors are detecting ALL\nsaccades (translational + oscillatory), or just oscillatory saccades, or if they are one and the same in head-fixed pigeons. A\nschematic showing the detection and calculation of these metrics in Fig 1 would go a long way (e.g. a modified version of\nFig S1a), and more concrete definition of the terms (\"all saccade rate or oscillatory saccade rate or oscillation frequency\",\ndepending on what is actually intended).\n- The \"customized MATLAB algorithm\" for detecting saccades should be described in methods.\n- The authors state that the glucose recording and injection apparati don't have a major impact on affect saccade frequency,\nbut this data should be presented.\n2. The analysis of the temporal relationship between oscillatory saccades and neural responses has several issues.\n- The entire analysis in Fig 2 is unusual. It would be helpful to see a more straightforward\napproach, e.g. apply a series of sliding average windows of increasing duration to both\nsaccade measures and neural response, then plot time-lagged cross-correlations. PCA also\nseems unnecessary here. It would be more informative to show the same cross correlation\nanalysis for each saccade measure individually.\n- Better controls are needed, e.g. a comparison to correlation coefficients from shuffled\nsession IDs. See Harris 2021 BioRXiv \"Nonsense Correlations in Neuroscience\". I am\nparticularly concerned that the authors may be treating each visual response as an independent\nsample, even through the preceding saccade histories overlap, and may be ignoring the\nobvious slow changes over time within the session seen in Fig 2c,d.\n3. The proposed mechanism for the two different timescales of the relationship between oscillatory saccades and neural\nresponse doesn't make sense.\n- The authors imply that both the positive correlation at ~3-4 minute timescale and the negative correlation at <6 s timescale\nare due to slow dynamics of glucose diffusion from the pectin oculi to retinal ganglion cells. However, these timescales differ\nby over an order of magnitude. Which is it? Does it takes seconds or minutes for glucose to diffuse and increase neural firing\nrates? Are there alternate explanations for the short + long time scale effects? E.g. since responses are measured\ndownstream in retino-recipient areas, could post-saccadic suppression play a role? Could mechanical disturbance affect\nRGC firing? Or, are there behavioral correlations that could explain this short timescale effect? E.g. are birds particularly\nlikely to make a saccade when glucose is already quite low, or are saccades regularly spaced in time? A more complete\ndescription of saccade statistics would likely help clarify this. Finally, could mechanical agitation both increase uptake of\nexisting glucose on a short timescale, and lead to increased glucose diffusion from the pectin over a longer timescale?\nIf the mechanism is totally unknown, that is fine, but the discussion should be more transparent.\n- If the short-timescale hypothesis is true (due to the time it takes for glucose to reach cells), then there should be a positive\ncorrelation between the duration and intra-saccade frequency of oscillations and the firing rate.\n- The authors don't mention oxygen diffusion at all, even though is is a previously proposed role for oscillatory saccades.\nCould other nutrients such as oxygen diffuse at different rates?\n- Can the causal manipulations provide some insight? What is the temporal relationship between the increase in intraocular\nglucose concentration caused by injection, and the increase in neural response? The Raphe inhibition experiments\ndemonstrate a 3-4 min lag between decreased saccades and decreased glucose concentration, but what is the time lag with\nthe neural response?\n4. Some conclusions are overstated\n- Lines 99-101. \"These findings indicate that attention-driven oscillatory saccades in birds enhance glucose mobilization\nfrom the pecten oculi to the retina, thereby boosting nutrient supply during social information gathering\" This conclusion is\noverstated at this point since causality has not yet been established. It is plausible at this point that viewing a salient\nstimulus causes other changes in attention and arousal that lead to both increased glucose circulation (with a delay) and\nincreased saccade rates. A similar comment applies to lines 113-116.\n- Line 284 - \"Meanwhile, avian visual responses are not only sustained, but dynamically optimized to efficiently process\nsalient and intricate visual stimuli by enhancing retinal energy availability\" What does this sentence mean? I don't believe\nthe authors distinguish between the effects of oscillatory saccades on sustained vs. transient neural responses. Similarly,\nwhat is the evidence for \"precise shaping of retinal function\" -- the authors do not demonstrate sharpening of receptive fields,\nfor example.\n- Line 292 \"Our findings provide novel insights into the critical role of avian oscillatory saccades and extend their\nsignificance beyond basic visual scanning to metabolic and functional optimization.\" Previous studies cited don't seem to\nsuggest that the oscillatory component of avian saccades is used for visual scanning/gaze orienting. How could a fast ~30\nHz oscillation possibly achieve scanning? \"Moreover, they raise broader implications regarding the role of eye movements\nin maintaining retinal health, enhancing attentional performance, and optimizing visual function across species.\" What are\nthese broader implications exactly?\nMinor comments\n1. Line 78 - \"unlike primates, birds produce oscillatory saccades spanning tens to hundreds of milliseconds per event\" - As\nwritten, the implication is that birds only display oscillatory saccades.\n2. 81 - \"These oscillatory saccades serve for efficient environmental scanning and attention-driven visual processing 36,40.\"\n- What is meant by this sentence? Has a causal link been previously demonstrated between attentional behavior and\noscillatory saccades specifically? Ensure references support the statement for oscillatory saccades specifically. E.g. Ref 36\nmentions oscillatory saccades but doesn't consider further. Similarly, line 84 - \"visually-guided saccades\".\n3. Rates of saccades and oscillation frequency. It would be informative to give baseline rates of saccade\nfrequency/oscillation frequency/glucose levels when these results are first presented, and their new values upon viewing the\nmating scene. Conversely, exact values for an example bird are unnecessary. Later in the text (lines 217-220), when\nbaseline saccade and oscillation rates are given, the values are surprisingly low compared to previous reports (e.g.\nNye 1969). It is understandable if this is due to the invasive nature of glucose monitoring, but if so, it should be acknowledged. I\nalso question whether the oscillation frequency reported after muscimol injection (0.04 Hz = 25 s period) is meaningful.\n4. Values are given to unnecessarily high precision, see standards for significant figure reporting.\n5. Fig 1c - why does intraocular glucose increase before the video onset? What is the smoothing window?\n6. Fig 1d - What do individual data points represent and how are they calculated (e.g. over what time period is the response\nand baseline calculated)?\n7. Fig. 3j - What is the model?\n8. Fig. 4c - What is the color scale? How can neurons have a response of -30 sp/s? Also please add start & ending trial\nnumbers.\n9. Line 171 - What does \"A 5.00-6.00\" refer to?\n10. What is the effect of muscimol injections in the Raphe nucleus on both regular saccades and oscillatory saccades? It is\nunclear if effects are due to suppression of the oscillatory component specifically.\n11. Discussion - Studies have demonstrated dramatic differences in eye movements in head fixed vs. unrestrained birds.\nSee Land 2015 and Gioanni 1988. Are oscillations equally important when the head is free to move? Have they been\ndemonstrated in both head fixation and free motion?\n12. Line 274 - Reference 50 doesn't contain any mention of the Raphe complex. It would be helpful to clarify the proposed\nrole of the Raphe complex in generation of regular saccades vs. oscillatory saccades.\n13. Line 321-322. I believe the authors mean \"intraocular\", not \"interocular\"\n14. Methods: How many individuals of the non-pigeon species were used? Were the surgical procedures the same for these\nanimals (the methods refer to pigeons when describing most procedures)?\n15. Methods, \"Visual conditions\" - How many 30 or 60 min visual presentations were delivered in a single session? Were\nanimals given food and water during sessions?\n16. Line 480 - \"saccades\" - typo\n17. Figures are very low resolution", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Authors: We sincerely thank you for the positive and encouraging assessment of our work. We\nare delighted that you recognize our study as addressing a core question in avian visual\nphysiology. Your supportive comments greatly reinforce our confidence in the novelty and\nsignificance of our findings. We are also very grateful for your thoughtful remarks and\nconstructive suggestions, which have helped us further improve the quality and clarity of the\nmanuscript.\nIntroduction\nLines 38-39: Indeed, birds have higher metabolic demands, but their neurons run on “cheap\nfuel” since they require only ca. 1/3 glucose per neuron, compared to mammals (Von Eugen et\nal., Current Biol., 2022).\nAuthors: Thank you for recommending the excellent study by Von Eugen et al. (2022). This\nwork, based on rigorous experimentation, quantifies cerebral metabolic rate of glucose\nconsumption in bird brains (examined in pigeons). In particular, Figure 2A provided clear\nevidence that regional glucose uptake differs across the avian brain, with the retina and\nforebrain are among the most metabolically active regions, suggesting that they consume\nglucose at above-average levels. This observation reinforces our conclusion that glucose\nutilization plays a critical role in supporting the substantial metabolic activity of the avian retina.\nThis highlights a highly efficient energy utilization strategy in the avian nervous system, which\ncomplements the mechanisms discussed in our manuscript. We have incorporated this\nimportant information into the revised manuscript (Lines 38–43).\n“Birds present a fascinating paradox in vertebrate evolution: despite exhibiting exceptionally\nhigh retinal metabolism among their brain regions (Nagy et al., 1999; Von Eugen et al., 2022),\nand possessing larger eyes (Baden et al., 2020; Meyer, 1977), superior visual acuity (Caves et\nal., 2018; Evans, 2016; Martin, 2022; Shimizu & Watanabe, 2012), and thicker retinas than\nmammals (Hughes et al., 1972; McKibbin et al., 2014; Reiner et al., 2018), they entirely lack\nretinal blood vessels (Dubielzig et al., 2010; Ruggeri et al., 2010; Williams, 2023) that are\nessential for nutrient delivery in primates (Adams & Horton, 2003; Catita et al., 2015; Provis,\n2001). This raises an intriguing enigma: how do avian eyes fulfill the intense metabolic activity\nof thick, high-acuity retinas in the absence of direct vascular support?”\nAs far as I know, the reptilian equivalent to the avian pecten is the conus papillaris. It might be\nworth to alert the reader that this structure is possibly an ancient archosaur feature that might\nbe secondarily lost in mammals.\nAuthors: Thank you so much for this insightful comment. As you pointed out, the conus\npapillaris in reptiles is widely regarded as the counterpart to the avian pecten. We agree that\nhighlighting its possible origin as an ancient archosaur feature, secondarily lost in mammals,\nprovides valuable evolutionary context to the reader. We have revised the Introduction\naccordingly to clarify this point (Line 50-54).\n“Through repetitive motion, these saccades could propel glucose and other nutrients from the\npecten oculi toward the retina, ensuring metabolic support to retinal neurons (Meyer, 1977;\nWood, 1917). The pecten oculi is a comb-like, pigmented vascular structure unique of birds\n(Supplementary Fig. 1b) and homologous to the reptilian conus papillaris (Ringvold, 2023),\npossibly representing an ancient archosaur feature secondarily lost in mammals.”\nOverall\nIt is bit confusing that saccades to bring an interesting object into the projection of the retinal\narea of enhanced vision vs. oscillatory saccades with small cyclotorsional rotations are not\nproperly differentiated. Do the authors imply that they occur in combination? Would\ncyclotorsional rotations not blur the image? Are the concomitant activity patterns in the visual\nnuclei a potential counterstrategy to increase temporal resolution to impede blurring？\nAuthors: We sincerely thank you for these insightful questions regarding the relationship\nbetween saccadic eye movements and their possible visual consequences, that are indeed\ncritical to understanding avian vision.\nFirst, we would like to clarify that “saccades” and “oscillatory saccades” are not differentiated\neye movement types in birds. In pigeons, each saccade comprises both a displacement in eye\nposition and several cycles of oscillation, as previously reported and confirmed by our long-\nterm studies. Whether during spontaneous saccades (Pettigrew et al., 1990; Wallman & Letelier,\n1993; Yang Yang et al., 2008) or the quick phase of optokinetic nystagmus (Xiao et al., 2023;\nYan Yang et al., 2008; Yang Yang et al., 2008), oscillations consistently appear as an intrinsic\ncomponent of every saccade rather than a separate movement category. We apologize for the\nmisleading terminology in the previous submission. In the revised manuscript, we now use\n“saccade” when referring to the overall saccadic eye movement and “saccadic oscillation” or\n“oscillation” when emphasizing its oscillatory component. Figure 1 has been updated\naccordingly to illustrate these features of avian saccades more clearly.\nSecond, we agree with you that these oscillations could blur the retinal image during saccadic\neye movements. However, our previous work demonstrated that saccades in pigeons induce\nperisaccadic inhibition of neuronal firing in the brain visual regions during saccades, mediated\nby corollary discharge signals from brainstem neurons (Yan Yang et al., 2008). This suppression\nmechanism likely contributes to visual stabilization by transiently suppressing blurred visual\ninputs during rapid eye movements.\nFinally, the present study focuses exclusively on visual responses during fixation periods, about\none to six seconds after a saccade, when perisaccadic modulations have subsided. As detailed\nin the revised Methods (Line 596-603), all visual response trials evoked by grating motion that\noccurred during periods of perisaccadic modulation were excluded to eliminate perisaccadic\neffects. Therefore, the neuronal activity analyzed here reflects influences from factors like\nglucose concentration changes over time, not from corollary discharge or other oculomotor-\nrelated signals.\n“In order to eliminate the influence of perisaccadic modulations on visual response analyses,\nwe first quantified the temporal extent of these modulations by aligning neuronal firing rates to\nthe onset and offset of saccades for each neuron. The duration of perisaccadic modulation was\ndetermined using a standard-deviation-based criterion, identifying the time point where the\nfiring rate consistently returned to the stable baseline within a specified statistical tolerance. All\nvisual response data within these modulation time frames were excluded, thereby restricting\nanalyses to stable fixation periods and isolating them from perisaccadic modulations.”\nMethod\nWere the animals only slightly sedated during recordings or deeply anaesthetized?\nAuthors: We appreciate you for raising this important point. In our study, animals were initially\nanesthetized with ketamine (30 mg/kg) and dexmedetomidine (0.125 mg/kg) for surgical\npreparation. The anesthetic depth was carefully monitored by respiration patterns and toe-pinch\nreflexes. After surgery, animals were allowed several days to recover fully. On experimental\ndays, a light supplemental dose of ketamine (4 mg/kg) was administered solely for brief\nhandling. Recordings began at least 30-40 minutes later, when the animals were fully awake\nand behaviorally recovered, consistent with established procedures (Azizpour & Hassani, 2012).\nThus, all neuronal recordings were conducted in awake, head-fixed birds under stable and\nreproducible conditions.\nAs you suggested, we have clarified these details in the revised methods (Line: 383-411):\n“Each animal was anesthetized by injecting ketamine (30 mg/kg) and dexmedetomidine (0.125\nmg/kg) into the pectoral muscles. The anesthetic status was monitored by examining their\nrespiration patterns and reflex reactions to toe pinching (Yan Yang et al., 2008; Yang et al., 2017;\n2008). Pigeons were wrapped with soft straps and positioned into a stereotaxic apparatus. After\na scalp incision, a lightweight steel head holder was affixed to the skull to ensure stable head\nrestraint in experiments. Guided by stereotaxic coordinates of the pigeon brain (Karten &\nHodos, 1967), recording windows over the left tectum, the left telencephalon, as well as a\npharmacological injection window over the cerebellum were surgically exposed by a dental\ndrill. The overlying dura mater remained intact. We also gently drilled a little fragment of the\norbital bone (1 × 1 mm) located on the top of the right eye, for the purpose of intraocular glucose\nexaminations and intraocular injections. After surgery, the scalp was sutured and prescribed\nerythromycin ointment (BAIJINGYU Pharmaceutical, Nanjing, China). Pigeons were housed\nindividually and received Meloxicam (0.5 mg/kg, PO, once or twice daily) to alleviate\npostoperative pain and ceftriaxone sodium (75 mg/kg, IM, every 6-8 hours) to prevent infection\nfor 3-7 days. Within 12-24 hours, their locomotion, feeding, and hydration behaviors had\nresumed.\nOn experimental days, pigeons received a light dose of ketamine (4 mg/kg), were coated in a\nsoft bag, and situated on a foam couch. The head was secured to the stereotaxic apparatus via\nthe implanted head holder. Wound margins and surrounding muscles were periodically\ninfiltrated with lidocaine (Shanghaizhaohui Pharmaceutical, China). Experiments commenced\nat least 30 min after light ketamine sedation when the animals had recovered, consistent with a\nprior report (Azizpour & Hassani, 2012). All neuronal recordings were performed in awake,\nhead-fixed pigeons (Mahajan & Mysore, 2022; Neuenschwander et al., 1996; Xiao et al., 2024)\nwith unrestricted eye movements. During neuronal recordings, the left eye was covered while\nthe right eye was exposed to visual stimuli. For monitoring eye movements in the budgerigar,\nchukar partridge, chicken and duck, animals underwent the same anesthesia and head-fixation\nprocedures. During the experiments, animals were not provided with food or water; however,\nthey had free access to both in their home cages before and after each session.\nAll procedures in this study were in strict accordance with the guidelines for the care and use\nof animals established by the Society for Neuroscience. All of the animals were handled\naccording to protocols approved by the Institutional Animal Administration Committee at the\nInstitute of Biophysics, Chinese Academy of Sciences (IBP-P-001(21) and IBP-P-001(24)).”\nReviewer #2 (Remarks to the Author):\nSummary:\nIn this study, the authors investigated how oscillatory saccades (OS) modulate retinal glucose\ndynamics and visual neuronal responses in awake, passively viewing pigeons. They monitored\neye movements and intraocular glucose levels, in some cases interstitial glucose, and also\nrecorded single-unit activity in three retinorecipient brain regions: the optic tectum (OT), the\nnucleus opticus principalis thalami (nOPT), and the pretectal nucleus lentiformis mesencephali\n(nLM) while birds viewed visual stimuli. Visual stimuli included naturalistic social videos of\npigeons, light/dark screen cycles, and large-field drifting gratings.\nThere are several key findings in the study. The authors demonstrate strong correlations\nbetween visual stimulation and subsequent OS as well as between visual stimuli triggered OS\nand subsequent increases in intraocular glucose levels and visual responses. Interestingly,\npharmacologically increasing retinal intraocular glucose via D-glucose injections enhanced\nvisual responses, while GluT-1 inhibition diminished these responses, causally linking\nintraocular glucose levels to visual responses. Finally, suppressing OS via muscimol OS via muscimol injections\nin the brainstem raphe complex diminished both intraocular glucose levels and visual responses,\nthereby establishing a causal link from oscillatory saccades to visual responses through\nmodulation of intraocular glucose levels.\nThe strengths of this study are its rare combination of oculomotor, metabolic, and\nneurophysiological measurements in awake birds, its well-controlled pharmacological\nmanipulations that establish causal links, and its demonstration that these effects occur across\nmultiple visual nuclei. However, while the causal role of oscillatory saccades in visual\nprocessing is convincingly demonstrated, the attention-driven claims are unsupported by the\ndata. These should either be removed or require additional experiments to justify the link\nbetween attention and oscillatory saccades.\nAuthors: We sincerely thank you for your thoughtful summary of our work and constructive\nfeedback. We appreciate the recognition of the strengths of our study, particularly the\nintegration of oculomotor, metabolic, and neurophysiological measurements in awake birds,\nand the pharmacological manipulations that helped establish causal links between oscillatory\nsaccades, intraocular glucose dynamics, and visual neuronal responses.\nWe also fully understand and appreciate your concern regarding the attention-related\ninterpretation. In the revised manuscript, we have carefully revised our discussion to avoid\noverstating this aspect. Specifically, we now describe saccades as stimulus-driven movements\nthat dynamically modulate retinal glucose supply and neuronal responsiveness, rather than\nattributing them to attentional processes. We have removed all statements implying a link\nbetween saccades and attention, and instead focus on their mechanistic role in coupling sensory-\ndriven eye dynamics with local metabolic regulation and neuronal activity.\nMajor Comments:\nSpecific conceptual critiques\n1. Re: “which birds actively evoked saccades during attention-driven information gathering”\nWhile here we have taken an example from a line from the abstract this is a recurring theme in\nthe manuscript and comes across as one of the major claims of the paper (which it should not\nbe). The evidence provided in the current manuscript does not demonstrate a direct (or indirect)\nlink between attention and OS and we disagree with the usage of this language in the manuscript,\nincluding in the abstract, introduction and discussion.\nAuthors: Thank you so much for pointing out this important issue. Regarding the comment on\n“attention”: we agree that our dataset in the prior submission does not directly measure\nattentional states. We acknowledge that in the absence of explicit behavioral or task-based\nmeasures of attention in our study, the initial phrasing in the prior submission could overstate\nthe evidence. We have corrected the related statements based on the ground truth, and now\nemphasize the mechanistic role in linking visually driven eye movements to intraocular\nmetabolic regulation and neuronal responsiveness in the revised manuscript.\na) A ~3-4 min lag between oscillatory saccades (OSs) and response facilitation is incompatible\nwith moment-to-moment attentional modulation. These results are more consistent with a\n“retinal readiness” hypothesis where scene changes trigger oscillatory saccades, elevating\nretinal metabolic support for visual processing. the authors should reframe their interpretation\naround metabolic maintenance rather than attention unless rapid, stimulus-locked effects are\ndemonstrated.\nAuthors: We greatly appreciate the insightful comment and agree that a ~3–4 min lag between\nsaccades and response facilitation is inconsistent with rapid, moment-to-moment attentional\nmodulation. In response, we have revised the manuscript to reframe our interpretation around\na mechanistic coupling between eye movement dynamics, local metabolic regulation, and\nneuronal responsiveness, rather than attention. Specifically, we now characterize saccades as\nvisual stimulus-driven eye movements that modulate intraocular glucose levels and visual\nneuronal activity, without inferring attentional involvement. We believe these revisions clarify\nthe scope of our conclusions and strengthen the mechanistic coherence and interpretive rigor of\nthe study.\nb) Fig 1 j-n and Fig 1 o-s are strong evidence that it is changes in a visual scene that are\nmodulating oscillatory saccades rather than a more active and voluntary process as the authors\nclaim. Further, from Fig. 1 j-n, it seems to be just the presence of light (or a change from less\nto more illumination) which might be driving increases in OS and subsequent glucose, visual\nresponses etc. In contrast a change from high luminance to dark as shown in Fig.1. o-s\ndiminishes OS. These pieces of evidence seem to suggest that this is a ‘reflexive’ mechanism\nfor priming the visual processing right from the retina which is the earliest node in the visual\nprocessing hierarchy, rather than an active form of behavioral response, as claimed by the\nauthors.\nAuthors: Thank you again for this thoughtful and perceptive comment. As noted, Fig. 1j-s (now\nupdated as Fig. 1f-o) clearly demonstrate that changes in the visual scene, particularly\ntransitions between light and dark, strongly modulate both saccade frequency (Fig. 1g, l) and\noscillation frequency (Fig. 1h, m). These findings highlight a robust component of the\noculomotor response and support the view that saccades are triggered by visual scene changes.\nIndeed, the enhancement of eye movements following illumination and their suppression in\ndarkness could suggest a reflexive mechanism that modulates visual processing, the earliest\nstage in the visual hierarchy, by elevating local metabolic readiness. The visually driven\nsaccades observed during naturalistic social video viewing (Fig. 1p-r) further indicate that such\neye movements can be dynamically modulated under diverse visual conditions, extending\nbeyond simple luminance changes.\nIn the revised manuscript, we have accordingly reworded the Abstract, Results, and Discussion\nto replace descriptions of saccades as “active” or “voluntary” with “stimulus-driven” or\n“visually driven”, emphasizing their reflexive, stimulus-related nature. We believe these\nrevisions more accurately capture the underlying mechanism, align our interpretation with the\npresented evidence, and strengthen the physiological coherence of the study.\nc) The animal subjects in this study are passively viewing birds and are not actively behaving.\nAll experiments in this study have been conducted in passively viewing birds and not in\nbehaving birds performing a task. The authors use the terminology ‘ behaving birds’ at several\nplaces in the manuscript (including methods) and these should be corrected using the\nappropriate terminology in the field, for example ‘ passively viewing birds’, ‘ awake, head-\nfixed’, etc (Neuenschwander et al, 1996, Mahajan and Mysore, 2022, Xiao et al, 2024).\nAuthors: We thank you for highlighting this important point. We agree that our experiments\nwere conducted in awake, head-fixed pigeons that were passively viewing visual stimuli, and\nnot in birds performing active behavior tasks. Our use of the phrase “behaving birds” was\nimprecise and could be misleading in this context. In line with your suggestion, we have\nsystematically revised the manuscript to use terminology consistent with the field awake, head-\nfixed pigeons, including in the Methods and all other sections. We have also included relevant\nprior work (Mahajan & Mysore, 2022; Neuenschwander et al., 1996; Xiao et al., 2024) to align\nour terminology with established standards.\n2. Correlation of OS with visually evoked neural responses\nFrom the Fig1 data it appears that changes in visual scene trigger OS. This is also apparent in\nthe correlation plots Fig2c and Fig2d.\na) Fig. 2c-d. It seems that there is no neuronal data in Fig.2 c and d. These are only ‘ saccade’\nand ‘oscillation’ frequency. Which means the figure caption (lines 699-700) and lines\n134 (main manuscript) are incorrect.\nOverall, we have found the writing and explanations in the text as well as axes labels and\ncolor schemes not to be at the standards that are expected from a manuscript submitted to\na top journal (see minor comments).\nAuthors: We thank you for this careful reading and constructive feedback. You are correct that\nFig. 2c-d of the previous submission only included saccade frequency and oscillation frequency\nrather than correlations with neuronal data. We apologize for the earlier mislabeling and have\nreplotted figures and revised the related text in the main manuscript to accurately describe these\ndata. The revised caption now specifies that Fig. 2c-d illustrate correlations between\nfrequencies of saccadic eye movements with neuronal activity.\nWe have also thoroughly revised the figure layout, axis labels, and color schemes throughout\nthe manuscript to ensure visual clarity and consistency with the standards of the top journal.\nFurthermore, explanatory descriptions in the Results section have been rewritten for greater\nprecision and readability, ensuring that all figures are clearly interpreted and aligned with the\ndata they present.\nb) For Fig. 2 c-d, each row going from bottom to top, the previous stimulus’s row should\nappear shifted by the ISI. Although this is visible, it isn’t consistent throughout the plot.\nSome diagonal streaks are visible, but they do not follow a consistent ISI-sized right shifted\npattern. Might this be obscured by smoothing artefacts due to a plot function? Please clarify.\nAuthors: We sincerely thank you for the careful observation and insightful question regarding\nthe details of Fig. 2c–d. In the previous version, Fig. 2c–f presented data from an example\nneuron recorded simultaneously with eye movements. Specifically, Fig. 2c and Fig. 2d depict\nthe calculated saccade frequency and oscillation frequency, respectively, within time windows\nranging from 1 s to 8 min preceding each probe stimulus, across the 1st to 149th probe trials\n(ordered from bottom to top along the y-axis). Your description is entirely accurate, and the\napparent irregularity you noted can be explained by key aspects of our experimental design\nrather than by any plotting or smoothing artefacts.\nFirst, as described in the Methods section, the inter-stimulus interval (ISI) between the 300-ms\ngrating motion trials was randomly varied between 6 and 10 seconds, rather than fixed. Because\nof this randomization, the stacked trial heatmaps in Fig. 2c–d do not produce perfectly aligned\nor consistent ISI-dependent diagonal patterns. The diagonal traces visible in the plots thus\nreflect the genuine temporal variability in the eye movement data under randomized ISI\nconditions, not artefacts of the plotting process.\nSecond, each 300-ms grating stimulus could occasionally coincide with spontaneous saccadic\neye movements. In our analyses, visual response trials evoked by grating motion that occurred\nduring periods of perisaccadic modulation were excluded to eliminate perisaccadic effects. This\nremoval of occasional trials created small discontinuities within the data series, which can\nappear as interrupted or segmented diagonal streaks in the plots.\nWe have added a clarification in the Methods sections explicitly describing the categories of\nneuronal visual response trials, ensuring that the observed results are properly understood as a\nconsequence of the experimental design rather than a visualization artefact.\nc) Fig. 2g. PCA - Do oscillation frequencies per saccade increase with saccade frequency?\nIf not, the way oscillation frequency is computed, a higher frequency could simply be a\nconsequence of more saccades in the window. Irrespective, it would be beneficial for the\nauthors to include plots for mean correlation coefficient versus saccade frequency and\noscillation frequency separately in the extended figures.\nAuthors: Thank you so much for raising this insightful question regarding the relationship\nbetween saccade frequency and oscillation frequency, as well as for the valuable suggestion to\nconduct independent correlation analyses.\nBy combining your and other reviewers’ valuable suggestions, we have substantially revised\nthe manuscript to improve both conceptual clarity and methodological transparency.\nSpecifically, we introduced a new Figure 1 that systematically defines and contextualizes the\nthree behavioral metrics: saccade frequency, oscillation frequency, and intrasaccadic frequency,\nwithin the established framework of avian saccadic eye movements. Saccade frequency was\ndefined as the number of saccades per analysis window. Oscillation frequency was the total\nnumber of oscillatory cycles across all bursts within the window, reflecting the overall density\nof oscillatory activity. Intrasaccadic frequency was the inverse of each oscillatory cycle’s\nduration within a saccadic burst. This figure also illustrates how these parameters vary across\nvisual conditions (dark, light, gray screens and viewing social videos). The new analyses\ndemonstrate that only saccade frequency and oscillation frequency change significantly across\nconditions, whereas intrasaccadic frequency remain stable, thereby providing a clearer\nconceptual and quantitative foundation for subsequent neuronal and behavioral interpretations.\nAlthough saccade and oscillation frequencies are overall positively correlated, their moment-\nto-moment fluctuations are largely independent. Residual cross-correlation analyses confirmed\nno evidence of noise-driven coupling, suggesting that they share a general trend but vary\nindependently in response to visual inputs. To clarify this relationship, we have included a new\nplot in Figure 1e illustrating their correlation, which should help readers better interpret the\ndynamics of avian saccadic eye movements.\nGiven the relationship between saccade and oscillation frequencies, we performed a principal\ncomponent analysis (PCA) to extract a unified component (PC1) capturing overall saccadic\nactivity strength. This approach could reduce collinearity between the two metrics and provided\na single, integrated variable for use in subsequent regression analyses. Nonetheless, we fully\nagree that examining neuronal correlations with saccade frequency and oscillation frequency\nseparately provides complementary insight. Following this recommendation, we have now\nincluded the new results in the revised supplementary figures (Supplementary Fig.6a-d).\nConsistent with their shared partial correlation, the new results revealed distinct yet comparable\ntemporal structures, with both second- and minute-scale patterns evident in neuronal\ncorrelations with saccade and oscillation frequencies. We believe these additions substantially\nenhance the clarity, completeness, and analytical rigor of the manuscript, and we are grateful\nfor this constructive suggestion.\nd) Fig. 2g - From the data, as mentioned earlier, it seems like the visual response enhancement\nand OS frequency increase seem like an effect of increased alertness/arousal once stimulus\npresentation begins (i.e. at the very start of the recording session). If that is the case, then\nit’s likely that the early trials drive the effects seen in 2g, and for later trials, the same 8-\nminute correlation curve could be more flattened. It would be good to see if the same plot\nas in Fig. 2g, but with trials partitioned from 1-50, 51-100 and so on. If the above is true,\nthe correlation plot for the trials from 51-100 would be more flattened.\nAuthors: We appreciate this thoughtful and important comment. We carefully considered the\npossibility that the observed enhancement in visual responses and increases in oscillation\nfrequency could reflect an early-session arousal or alertness effect, and we conducted additional\nanalyses to address this concern. The results indicate that such factors are unlikely to account\nfor the findings in Fig. 2g, for the following reasons.\nFirst, the dataset comprised 127 neurons recorded from 37 individuals (on average 3-4 neurons\nper animal). Before formal data collection, each neuron was tested using brief (300-ms)\ndrifting gratings in multiple directions to determine its preferred direction of motion. Thus, during the\nformal recordings, the stimuli were neither novel to the neurons nor to the animals on that\nexperimental day. Moreover, these gratings did not evoke any detectable eye movements in the\noculomotor data (Supplementary Fig.5). When we compared the data across trial partitions (1–\n50, 51–100, and the last 50 trials), neither saccade nor oscillation frequencies showed\nsignificant differences (all p > 0.05; one-way ANOVA). These results suggest that the effects\nin Fig. 2g are not attributable to transient increases in alertness or arousal at the start of the\nsession.\nSecond, to further evaluate this possibility, we explicitly examined whether neuronal visual\nresponses or their variability changed systematically over the course of the recording session.\nTrials were divided into three segments (1–50, 51–100, and the last 50 trials), and the\ncorresponding neuronal responses were compared. If early-session arousal had contributed to\nthe observed effects, we would expect a progressive decline in visual response strength or\nmodulation depth across partitions. However, neither the neuronal visual responses nor their\nvariability differed significantly across three partitions (one-way ANOVA, F = 0.04, p =\n(2,159)\n0.96 for visual responses; F = 0.11, p = 0.89 for variability; Fig. R1a, b). In addition, the\n(2,159)\ntiming of the grating motion relative to the latest saccade did not differ significantly across\npartitions (F = 0.12, p = 0.89; Fig. R1c). Together, these results suggest that the correlation\n(2,159)\npatterns reported in Fig. 2g primarily reflect relationships between saccadic activity and\nvisually evoked neuronal responses, rather than transient effects driven by early-session\nalertness or adaptation.\nFigure R1. Control analyses showing minimal influence of recording sessions on neuronal\nvisual responses. Group-level statistics (n = 54 neurons) for three metrics: neuronal visual\nresponses (a), variability of visual responses (b) and time of grating motion since last saccade\nonset (c). Each measure was compared across three segments within recording session for each\nneuron: trials 1–50, trials 51–100, and the last 50 trial. Error bars indicate SEM. One-way\nANOVA showed no significant differences across three segments for any measure (all p > 0.05).\nn.s., not significant, two-sided Wilcoxon signed-rank test.\ne) Line 143 - “ ..short- and longer-term correlations..”. Are these short/long-term correlations\nor correlations in short/long delays? The latter is more appropriate for the analysis in Fig.\n2g, while the former seems more suitable for correlations that last short or long periods for\na fixed-width sliding window analysis.\nAuthors: We thank you for this helpful comment. In Fig. 2g, the analysis specifically examines\ncorrelations between neuronal responses to grating motion and saccade activity across a series\nof progressively extended time windows preceding grating onset. For example, the 5-s and 3-\nmin analysis windows represent correlations between neuronal responses and eye movements\ncalculated over short and long temporal windows, corresponding to short and long delays,\nrespectively. Accordingly, we have revised the text to use the more accurate phrasing\n“correlations at short and long delays” instead of “short- and longer-term correlations.” This\nclarification more precisely reflects the temporal structure of our analysis and avoids potential\nmisinterpretation.\nOther major comments\n3. In Fig. 1i, why do the correlation coefficients increase in the pre-stimulus period? While\nthis is clear from the data in the example session shown in Fig. 1c, however, it seems that if\nthe effect is visible in the cumulative data in Fig. 1i, Fig. 1n and Fig. 1s, then this might\nhappen across sessions. Could this be due to an experimental artefact or due to predictability\nin the 30 min stimulus transitions? It would be beneficial if the authors could give their\nperspective on this.\nAuthors: Thank you for this insightful question. We agree that the pre-zero lag correlation\nwarrants careful examination and have performed additional analyses to address it. In our\nopinion, the apparent rise in correlation coefficients before zero lag might be caused by the\nintrinsic temporal structure of the signals being compared. The intraocular glucose fluctuation\nvaries smoothly over time and therefore exhibit strong autocorrelation. When such temporally\ncorrelated signals are cross-correlated, the overlap between the slow, gradual changes in one\nsignal (e.g., glucose) and the faster changes in the other (e.g., saccade frequency or oscillation\nfrequency) can produce elevated correlation values at neighboring negative lags. In other words,\nbecause the slow signal already shows an upward trend before the rapid change, partial\nalignment occurs even when the lag is slightly negative. Therefore, the increase in correlation\ncoefficients before zero lag might reflect the autocorrelation and gradual trends of the signals,\nrather than a causal influence of eye movements occurring before time zero. To address this,\nwe implemented a two-part analysis. First, we applied a modified cross-correlation analysis\nusing detrended and z-scored signals, with statistical significance assessed against a shuffle-\nbased null distribution. Second, to corroborate these findings with a method less susceptible to\nautocorrelation, we applied an independent, standard-deviation (SD)-based threshold method\nto estimate response latency. Reassuringly, both analyses yielded highly consistent results,\nconfirming that glucose responses lag behind saccade-related changes by approximately 3-4\nminutes (Supplementary Fig. 3). We have updated the Methods section with a detailed\ndescription of these approaches (Line 567-592).\n4. The authors should include summary metrics for receptive field mapping and grating\norientation tuning data of recorded units in the supplementary material.\nAuthors: We appreciate your suggestion to include summary metrics for receptive field\nmapping and grating orientation tuning. Although pigeons were head-fixed during\nelectrophysiological recordings, they were not trained to maintain visual fixation and freely\nmade saccades to orient their gaze. Therefore, it was not feasible to systematically map classical\nreceptive fields in the same way as in primate studies where gaze position can be precisely\ncontrolled.\nTo assess visual responsiveness, we presented full-field square-wave gratings on a large screen\n(130º × 140º) positioned 50 cm from the right eye, using an EPSON CB-535W projector. The\nhorizontal and vertical meridians of the grating field were rotated by 38° to match the pigeon’s\nnatural viewing geometry (Britto et al., 1990; Fu et al., 1998), following the same visual\nstimulation protocol described in our previous studies of awake head-fixed pigeons. The\norientation of each grating was orthogonal to its direction of motion. Gratings were presented\nfor 300 ms, moving at 8°/s in one of four cardinal directions: temporal-to-nasal (T–N),\nnasal-to-temporal (N–T), dorsal-to-ventral (D–V), or ventral-to-dorsal (V–D). For each neuron,\nthe direction of motion was customized to evoke the strongest visual response.\nAlthough we did not perform precise receptive field mapping or restrict stimuli to localized\nregions, the 300-ms large-field motion stimuli reliably evoked strong visual responses during\ninter-saccadic periods. We agree that the quantifying direction tuning is fundamental for\nunderstanding neuronal response properties. Accordingly, we have now included detailed\ndescriptions of the visual stimulation protocol in the Methods section and provided the\ncorresponding summary data in Supplementary Fig. 4, which add an overview of the tuning\nproperties of recorded units.\n5. Line 243-244 - The authors should clarify what is meant by \"...complexity of the\nrelationship between eye movements and visual processing\", and what role does this\ncomplexity serve? Similarly, in line 264 - “This short-term, second-scale modulation likely\nserved as an instant mechanism enabling…”. Why distinguish between second-scale and\nminute-scale effects when the data suggests a monotonic increase in correlation? Does the\ndata support two mechanistically distinct processes?\nAuthors: We really appreciate your insightful question. By referring to the “complexity of the\nrelationship between eye movements and visual processing” we intended to highlight that\nsaccadic eye movements in birds influence visual responses across multiple temporal scales. At\nthe minute-scale, our data showed that saccades were followed by gradual increases in\nintraocular glucose and enhanced neuronal responses, with a lag of ~3–4 minutes (Figs. 1, 2).\nThis effect likely reflects the cumulative impact of saccade-driven nutrient fluctuations over\nminutes, supporting retinal responsiveness in the absence of retinal vasculature. At the second-\nscale, neuronal responses increased transiently following individual saccades (Fig. 2g, i; Fig.\n3), accompanied by spatially graded improvements in receptive fields near versus far from the\npecten (Fig. 3j–m). Inspired by reviewers’ comments, our additional analyses in the revised\nmanuscript further revealed that both saccade duration and the interval since the preceding\nsaccade correlated with visual response strength. Longer saccades likely induced stronger\nmechanical agitation of intraocular fluid, whereas longer inter-saccade intervals allowed more\ntime for glucose diffusion to retinal cells (Supplementary Fig. 9). These findings support a rapid,\nsaccade-driven mechanism that transiently enhances nutrient delivery and visual\nresponsiveness.\nAlthough the overall correlation profiles in Fig. 2 may appear monotonic, they encompass both\ngradual, lagged effects over minutes and rapid, short-latency effects within seconds. We use the\nterms “minute-scale” and “second-scale” to highlight these different temporal dynamics, which\nlikely reflect complementary aspects of the saccade-metabolism-response coupling in the avian\nretina. Per your comment, we recognized that the term “complexity” was too general and\ninsufficiently specify. We have therefore replaced it with the phrase “multi-timescale coupling”,\nto more accurately convey that avian saccades modulate visual processing through temporally\nlayered mechanisms, producing both immediate and cumulative effects that together optimize\nretinal metabolic support and visual processing during natural viewing.\n6. Would it be possible to compute the latency between the intraocular glucose peak and the\ncorrelation peak (or the first instance of significant positive correlation) between OS and FR?\nAuthors: We greatly appreciate this insightful suggestion. In the study, saccades were recorded\nsimultaneously with either neuronal firing rates or continuous intraocular glucose (CGM),\nbut not both, as space limitations in the pigeon’s head precluded tri-modal recordings. During\npharmacological manipulations, intraocular glucose was monitored with a handheld glucometer,\nenabling causal assessment of glucose changes but not continuous temporal alignment.\nTherefore, a direct within-session latency between glucose peaks and saccade–FR correlations\ncannot be computed.\nTo approximate temporal relationships across modalities, we implemented a cross-session\nlatency analysis aligned to saccade onset. (1) For saccade-glucose coupling (CGM sessions),\nwe computed lagged cross-correlations between saccade events and glucose signals and\nidentified the first time point exceeding a baseline threshold (mean ± 2.5 SD). (2) For saccade-\nneuronal coupling (neurophysiology sessions), latency was defined as the delay corresponding\nto the peak saccade-visual-response correlation (Fig. 2; Supplementary Figs. 6-8). (3) For\nindirect glucose-visual-response coupling, we compared the mean latencies of these measures\nunder comparable behavioral conditions to approximate the glucose-neuron relationship. Both\nanalyses revealed consistent delays of about 3-4 minutes after changes in saccadic activity for\n(1) alterations in intraocular glucose levels and (2) the peak of correlations between saccadic\nactivity and neuronal visual responses.\nApplying these analyses to the muscimol experiments, we found that glucose changes lagged\nbehind reduced saccades by ~3-4 min (Fig. 5e), while neuronal suppression followed muscimol\ninjection by ~4.3 min, accounting for a 0.5-0.7 min pharmacological delay in saccade\nsuppression (Supplementary Fig. 3f). Thus, the effective delay between reduced saccades and\ndiminished neuronal responses was ~3.6-3.8 minutes, closely matching the saccade-glucose lag.\nThese results suggest that within roughly 3-4 minutes after saccades cease, both glucose supply\ndisruption and neuronal activity decline occur nearly simultaneously.\nWhile this cross-session triangulation cannot replace a tri-modal simultaneous recording, the\nconvergent latencies strongly support the interpretation that saccade-driven metabolic\ndynamics and neuronal responsiveness are temporally coupled on the minute scale. These\nprocedures and summary statistics have been added to the revised manuscript, and we note as\na priority for future work the development of a miniaturized tri-modal setup to obtain within-\nanimal, simultaneous latency estimates.\n7. In Fig. 3l-m. Why do anterior (“red”; pecten-proximal) and posterior (“blue”; distal)\nlocations show different peak glucose levels and firing rates, with max posterior/distal > max\nanterior/proximal? If at all a difference is expected, shouldn’t it be the other way around -\nanterior/proximal > posterior/distal? The authors should analyze smoother time courses with\noverlapping sliding windows to check if activity in the anterior region (red) has a higher peak\nat a time point between stages 2 and 3, potentially exceeding the peak of activity in the\nposterior region. \nAuthors: We greatly appreciate this insightful comment and helpful suggestion. In the previous\nversion, Fig. 3l–m plotted normalized visual responses (each neuron scaled to its own mean\nvisual response). This normalization inadvertently emphasized on variance differences within\nthe anterior (pecten-proximal) and posterior (pecten-distal) optic tectum (OT) neurons. Because\nposterior OT neurons exhibited larger trial-over-trial variability in visual responses across the\nelapsed-since-saccade time (CV: anterior neurons, 0.59; posterior neurons, 0.88), normalization\ncould give the misleading impression that max > max .\nposterior/distal anterior/proximal\nIn the revision, we re-analyzed the data by comparing raw stimulus-evoked visual responses\n(firing rate during the 300 ms stimulus period minus spontaneous baseline). The results show\nthat anterior neurons (mean ± SEM: 48.04 ± 4.95 spikes/s, n = 33 neurons) exhibited\nsignificantly higher visual responses than posterior neurons (24.24 ± 4.35 spikes/s, n = 24\nneurons). To further verify the temporal profiles of these responses, we conducted the suggested\noverlapping sliding-window analysis to obtain smoother time courses (1-stage analysis window\nwith a 0.5-stage step). With overlapping windows, the anterior trace peaks earlier, with a\nmaximum between stages 2 and 3, and exceeds the posterior peak in the corresponding interval\n(Fig. 3l, m). This analysis confirmed that the temporal dynamics of both anterior and posterior\nOT neurons remained well aligned with the predicted pecten-proximal > pecten-distal\nmetabolic gradient.\nWe are particularly encouraged by these new results for two reasons. First, they demonstrate\nthat anterior (pecten-proximal) neurons exhibit stronger visual responses than posterior (pecten-\ndistal) neurons, consistent with the estimated glucose concentration gradient and with our\nglucose simulations at two locations (Fig. R2). Second, the temporal patterns of neuronal\nactivity and glucose dynamics remained tightly correlated (Fig. R2). Nonetheless, we\nacknowledge that retinal anatomical factors, such as regional ganglion-cell density or sensitivity,\nmay also contribute. We have now clarified these points and emphasized the temporal dynamics\nin the revised text to present the results as objectively as possible.\nFigure R2. Convergence relationship between estimated intraocular glucose diffusion and\nvisual response. a, Estimated glucose dynamics following a saccade differ between anterior\nOT (a-OT, red) and posterior OT (p-OT, blue) neurons due to the proximity of their visual\nreceptive fields to the pecten (Hamdi & Whitteridge, 1954; McGill et al., 1966; Wylie et al.,\n2009). Red and blue dots indicate the recording sites and their respective receptive fields. Scale\nbar: 2 mm. b, Temporal glucose diffusion patterns reveal a delay between a-OT (red) and p-OT\n(blue) neurons. c, Estimated glucose concentration and visual response in the a-OT and p-OT\nregions. d-e, Estimated glucose dynamics (triangles, dashed lines) have similar trends to the\nvisual response (circles, solid lines) in the a-OT (d) and p-OT (e) across stages.\nAdditionally, how are glucose levels at the two retinal locations estimated? This methodology\nneeds to be explicitly described since it's currently unclear how these curves were generated.\nAuthors: We appreciate the comment highlighting the need to clarify the methodology used to\nestimate glucose levels. Pettigrew’s pioneering work in the tawny frogmouth demonstrated that\nfluorescein dye can be rapidly propelled ~2–3 mm from the avian pecten within 2 seconds of a\nsingle saccade, whereas during prolonged fixation dye diffusion was minimal (<0.5 mm even\nafter 120 s). This underscores the inefficiency of passive diffusion mechanisms and highlights\nthe essential role of saccades in nutrient delivery.\nIn our study, we aimed to approximate intraocular glucose dynamics in pigeons. Because\noutward nutrient transport from the pecten involves a complex three-dimensional process of\ndiffusion, fluid motion, and metabolic consumption, we simplified the problem into a one-\ndimensional diffusion model. In the revised Methods (Line 620-663), we have provided a full\ndescription of this approach. Briefly, glucose dynamics in the vitreous were modeled according\nto Fick’s second law, extended with a consumption term and a periodic mechanical component\nrepresenting saccadic oscillation. The model was parameterized using published values for\npigeon eye size, blood glucose concentrations, metabolic consumption rates, and diffusion\ncoefficients (Braun & Sweazea, 2008; Del Amo et al., 2017; Donovan, 1978; Koirala et al.,\n2022; Von Eugen et al., 2022).\nFor simulation, we set the ocular radius to 7 mm (diameter ~13–14 mm) and considered two\nretinal sites: a proximal site (1 mm from the pecten) and a distal site (4 mm from the pecten).\nPhysiological glucose concentrations were set to c1 = 15 mmol/L at the pecten and c2 = 13\nmmol/L distally (Braun & Sweazea, 2008). The metabolic consumption rate was estimated from\npigeon brain tissue (27.29 ± 1.57 μmol/100 g/min ≈ 4.55×10−4 mmol/L/s(Von Eugen et al.,\n2022), and the diffusion coefficient was derived from corrected water values at 40 °C, reduced\nby half to approximate vitreous properties (≈ 4.68 × 10⁻⁴ mm²/s(Koirala et al., 2022)). A\nperiodic oscillatory term was included based on measured saccade intervals and amplitudes.\nThis model allowed us to generate time-dependent glucose concentration curves for proximal\nand distal retinal locations, capturing the general trends of glucose diffusion influenced by\nsaccades. While these parameters cannot fully replicate the complex physicochemical\nenvironment of the avian vitreous, the model provides a reasonable theoretical framework to\nestimate how glucose availability differs across retinal regions (pecten-proximal vs. pecten-\ndistal). We also noted in the revised Methods that more precise modeling will require further\nfluid-mechanics characterization of the avian vitreous.\nFinally, are the other two nuclei, nOPT and nLM, topographically organized?\nAuthors: Thank you for raising this important point regarding the topographic organization of\nthe nOPT and nLM. In the case of the nOPT, as noted by Remy and Güntürkün (1991), the\nretinal projections are relatively sparse, and retrograde tracing did not reveal a clear or\nsystematic retinotopic organization. This diffuse and weakly topographic projection pattern\nprevents us from reliably inferring receptive field origins from electrode positions, unlike in the\nOT where a robust retinotopic map exists. For the nLM, the situation differs somewhat.\nBodnarenko et al. (1988) demonstrated the presence of a retinotopic map in this nucleus.\nHowever, the retinal ganglion cells projecting to the nLM are concentrated in a horizontal band\nnear the retinal equator, while input from central visual field regions is sparse. These precludes\nreliable group comparisons of proximal versus distal representations analogous to those we\nconducted in the OT. Taken together, while both nOPT and nLM play important roles in avian\nvisual processing, the specific anatomical and projection features of OT make it more amenable\nto the type of spatially structured analyses we pursued here.\n8. It is clear that muscimol injection in the Raphe complex abolishes OSs, but does it also\nsuppress glucose release from the pecten? If not, one would still expect positive correlations to\nemerge, although weaker, with an additional delay. It would be useful to include the same\ncorrelation analyses as in Fig. 1i and Fig. 2g for the muscimol data in Fig. 5. The data in Fig.\n5g already suggest this effect may be present, where the later trials in the muscimol condition\nhave higher firing rates.\nAuthors: Thank you for raising the important and thoughtful question. We agree that determining\nwhether muscimol injection into the Raphe complex affects glucose release from the pecten,\nindependent of saccades, is highly relevant.\nFirst, to our knowledge, muscimol in the Raphe complex, a GABA receptor agonist, does not\nA\ndirectly alter glucose transporter activity in the pecten. Our data indicate that its principal effect\nis to abolish normal saccadic eye movements (Fig. 5b and c), accompanied by a significant\ndecrease in intraocular glucose levels, from 16.00 ± 2.01 to 11.45 ± 2.08 mmol/L (p < 0.001;\nnormalized data in Fig. 5d).\nSecond, if glucose delivery were driven effectively by passive diffusion from the pecten,\none would indeed expect delayed, weaker correlations to persist. However, classic work by\nPettigrew et al. (1990) demonstrated that fluorescein dye can be actively propelled 2–3 mm\nfrom the avian pecten within ~2 s following a saccade, whereas diffusion during fixation\nremains minimal (<0.5 mm even after 120 s). This observation indicates that passive diffusion\nalone cannot effectively transport nutrients within the retina.\nThird, additional analyses of the muscimol data provided further supporting evidence. We\nappreciate your suggestion to include correlation analyses similar to Fig. 2g. However, due to\nspatial constraints in the pigeons’ heads, it was not feasible to record saccades, neuronal firing,\nand intraocular glucose continuously and simultaneously within the same sessions, which\nprecluded meaningful correlation analyses with firing rates. Nevertheless, we performed\ncomplementary analyses showing that saccadic eye movements and neuronal visual responses\nwere significantly reduced within 0.5-0.7 min (Fig. 5f; Supplementary Fig. 3f) and ~4.3 min (p\n< 0.001) after muscimol injection, respectively. This ~3-4-minute lag between the reduction in\nsaccade frequency and the onset of neuronal inhibition closely matched the 3–4-minute delay\nbetween muscimol-induced suppression of saccades and the subsequent decline in intraocular\nglucose (Fig. 5e). Together, these results further support the interpretation that disrupting the\nsaccade-driven pumping mechanism impairs glucose distribution and visual function.\nTo clarify these points, we have reorganized and revised text accordingly. We also corrected\nFig. 5g by labeling trial numbers for both before and muscimol conditions, making it clear that\nneuronal visual responses decreased progressively following muscimol injection.\nMinor comments:\n1. The authors should replace \"awake, behaving pigeons\" with \"awake, passively viewing\npigeons\" throughout the manuscript, given that the birds were head-restrained and\npassively viewed visual stimuli.\nAuthors: Thank you for this correction. We agree that the phrase “awake, behaving pigeons”\nwas inaccurate, as the pigeons were head-fixed and passively viewed the stimuli. We have\nrevised the text throughout the manuscript to use the more accurate wording “awake, head-\nfixed pigeons.”\n2. Fig. 1c uses the same color scheme for kissing/mounting as for the saccade frequency,\noscillation frequency, and intraocular glucose color bars. The authors could remap one of\nthese to avoid confusion\nAuthors: Thank you for the helpful suggestion. We agree that the color scheme in Fig. 1c could\ncause confusion. In the revised figure, we have simplified the color palette to use a single color\nto present the social video as one category of visual scene, (as suggested both in your earlier\ncomment and by Reviewer #3). The updated plots are now visually distinct from the color bars\nrepresenting saccade frequency, oscillation frequency, and intraocular glucose. We believe this\nchange will improve clarity and removes potential ambiguity.\n3. It would be better if the authors could clarify the visual stimulus protocol for mating videos.\nAre videos 30 minutes long, or are they 30 minutes of gray screen with intermittent\nkissing/mounting clips? What do the dark-gray periods indicate?\nAuthors: Thank you for the comment. We acknowledge that the original description of the video\nstimulation protocol lacked clarity. In the revised Methods, we have clarified that each\nexperiment consisted of a continuously played 30-minute social video depicting pigeon mating,\nwhich was preceded by a gray screen lasting a randomly varying period of 30-40 minutes. As\nnoted in our response to the previous comment, the updated plot now uses a gray-purple color\nbar, with purple indicating the 30-minute social video and gray indicating the preceding 30-\nminute gray-screen period.\n4. In all relevant figures, the authors should replace “normalization” with normalized <variable-\nname>. Specify what the variable is normalized to, in short, when possible, in the figure\ndescription, even if the methods section consists of a detailed explanation.\nAuthors: Thank you for this valuable suggestion. In the revised manuscript, we have\nsystematically updated all figure axis labels to replace the general term “Normalization” with\nspecific expressions (e.g., “normalized saccade frequency” or “normalized oscillation\nfrequency”). In addition, we now specify in each figure legend what the variable was\nnormalized to, while retaining the detailed explanation of the normalization procedure in the\nMethods. We believe these changes improve both precision and readability of our data.\n5. Line 65. ‘natural viewing behavior’ – remove. This is not natural viewing behavior but in\none case viewing naturalistic stimuli in passive head-fixed birds. a. Similarly - Line 244\n“natural viewing conditions”\nAuthors: Thank you so much for this suggestion. We agree that the terms “natural viewing\nbehavior” and “natural viewing conditions” were inaccurate given that the birds were head-\nfixed. In this study, pigeons were head-fixed to ensure stable neuronal recordings. As you\nsuggested, we have replaced these phrases with more precise wording in the revised manuscript,\nstating that they were head-fixed and passively viewed naturalistic visual stimuli without the\nneed for trained behavior, discrimination, or reward-related tasks.\nConcluding remark:\nThe study is technically impressive, but the current framing around attention overreaches the\ndata. Addressing the analytical and interpretative issues above is essential to give an accurate\nperspective to readers.\nAuthors: We sincerely thank you for the valuable suggestions. As your comments pointed out,\nour original framing have overreached the role of attention, and we have carefully revised the\nmanuscript to avoid overinterpretation. Specifically, we now describe our findings in terms of\nthe observed coupling between stimulus-driven saccades and intraocular glucose dynamics. We\nbelieve these revisions provide a more accurate and balanced interpretation of the data,\nconsistent with the evidence available.\nReviewer #3 (Remarks to the Author):\nSummary\nOscillatory saccades are high frequency (~10-40 Hz) eye movements that often accompany\ntranslational eye saccades in birds. Xu et al. hypothesize that (1) oscillatory saccades are\ninitiated during visual behaviors that require higher nutrient levels in the retina; (2) these\nsaccades distribute nutrients (specifically glucose) in the eye, and (3) this increased glucose\ndistribution improves retinal processing of images.\nIn support of (1), they show an immediate increase in saccade frequency and oscillation\nfrequency (although these terms require clarification) upon salient changes in visual input. In\nsupport of (2), they find that intraocular glucose concentrations increase ~3 minutes later and\ndemonstrate causality by pharmacologically supressing saccades. In support of (3), they record\nincreased neural responses to visual stimuli in retino-recipient nuclei also ~3 min after periods\nof increased saccades. They again provide some evidence for causality through intraocular\nglucose injections which increase neural responses, and suppression of saccades which decrease\nneural responses.\nThe neural data, intraocular glucose recording, and simultaneous causal manipulations are\ntechnically challenging and are of particular interest. The main issues revolve around better\ncontextualizing the specific role of oscillatory saccades, and improving the analysis and\ninterpretation of temporal correlations between saccades, glucose concentrations, and neural\nactivity. Overall, the authors provide convincing evidence for a link between oscillatory\nsaccades, glucose distribution, and visual processing. The result is a fascinating example of\nhow a neural system solves a challenging metabolic problem, and should therefore be of broad\ninterest.\nAuthors: We sincerely thank you for the careful and generous summary of our study, and for\nrecognizing both its technical contributions and broad conceptual interest. We particularly\nappreciate the characterization of the work as “a fascinating example of how a neuronal system\nsolves a challenging metabolic problem.” Following your suggestions, we have clarified three\nsaccade-related metrics, including saccade frequency, oscillation frequency and intrasaccadic\nfrequency, and incorporated additional analyses and interpretations of the temporal correlations\namong saccades, intraocular glucose concentrations, and neuronal activity. These revisions\nsubstantially improve clarity and strengthen the mechanistic narrative of the manuscript.\nMajor points\n1. The manuscript depends on two key behavioral metrics: \"saccade frequency\" and \"oscillation\nfrequency\". These metrics are poorly defined and the discussion around them is unclear.\nAuthors: We appreciate your insightful feedback regarding the definition of avian behavioral\nmetrics. As correctly noted, “saccade frequency” and “oscillation frequency” are central to our\nstudy but were insufficiently clarified in the original submission. In the revised manuscript, we\nhave added new plots in Figure 1 that systematically defines three behavioral metrics: saccade\nfrequency (number of saccades per analysis window), oscillation frequency (total number of\noscillatory cycles across all saccadic bursts within the window), and intrasaccadic frequency\n(the reciprocal of each oscillatory cycle duration, reflecting the oscillation rate within a saccadic\nburst). These behavioral metrics are now contextualized within the established framework of\navian saccadic eye movements described in previous literature. This figure also illustrates how\nthese features vary across different visual conditions in pigeons, including light, dark, and gray\nscreens as well as social video viewing. The new results demonstrate that only saccade\nfrequency and oscillation frequency change significantly across visual conditions, thereby\nproviding a clearer conceptual and quantitative basis for subsequent analyses and\ninterpretations. Accordingly, we have revised the corresponding text in the Results (Lines 85–\n97), Methods (Lines 530-537) sections and rewritten the related parts of the Discussion. We\nbelieve that these revisions enhance the clarity of the presentation and strengthen the overall\ncoherence of the manuscript.\n- The introduction lumps all avian saccades together. For example, Line 48 and 81-82- I don't\nbelieve anyone has suggested the oscillatory component of avian saccades specifically is\nimportant for stabilizing the visual scene. How could ~30 Hz torsional movements possibly be\nused for image stabilization? The connection between translational gaze shifting saccades vs.\noscillatory saccades should be more clear, especially given this groups previous work on the\ntopic. Do they always occur together? If not, which behaviors are more associated with one\nthan the other? What is the impact of head fixation?\nAuthors: We sincerely thank you for this insightful comment and for highlighting the need to\nclarify the relationship between translational (gaze-shifting) saccades and their oscillatory\ncomponents. In pigeons and chickens, it has been well established that each saccade inherently\nincludes oscillations (Wallman & Letelier, 1993). Previous studies have consistently\ndemonstrated that avian saccades producing gaze shifts are accompanied by rapid oscillations,\nwhich are not artifacts of head fixation. For instance, Nye (1969) reported oscillatory eye\nmovements in freely moving pigeons, Pratt (1982) identified ~25 Hz intrasaccadic oscillations\nas reliable markers of saccades in walking chickens, and Schwarz et al. (2013) employed a\nmagnetic tracking system to quantify oscillatory saccades in unrestrained chickens. In our\nrecordings, the supplementary videos demonstrate that these oscillations persist during\nsaccades, even when the birds are freely moving their heads, confirming that the oscillations\nare intrinsic features of avian saccades.\nIn the previous version, the term “oscillatory saccades” was not intended to imply a separate\nbehavioral category, but rather to differentiate avian saccades—which inherently contain\noscillations—from the smooth, non-oscillatory saccades typical of primates. We apologize for\nthe confusion caused by the previous terminology and have corrected it into “saccade”. The\ncorresponding text and figure have been updated to clearly reflect this distinction, supported by\nour data and citations to relevant studies (Nye, 1969; Pratt, 1982; Schwarz et al., 2013; Wallman\n& Letelier, 1993).\n- In the results, the measures of \"saccade frequency\" and \"oscillation frequency\" are never\nclearly defined. Does the former refer to inter-saccade rate and the latter to intra-saccade\noscillation rate? It is also unclear if the authors are detecting ALL saccades (translational +\noscillatory), or just oscillatory saccades, or if they are one and the same in head-fixed pigeons.\nA schematic showing the detection and calculation of these metrics in Fig 1 would go a long\nway (e.g. a modified version of Fig S1a), and more concrete definition of the terms (\"all saccade\nrate or oscillatory saccade rate or oscillation frequency\", depending on what is actually\nintended).\nAuthors: We thank you for this insightful comment and for highlighting the need to clarify the\ndefinitions and relationship between “saccade frequency” and “oscillation frequency.” As noted\nabove, we now define three frequency-related metrics of avian saccades: saccade frequency\n(number of saccades per analysis window), oscillation frequency (total number of oscillatory\ncycles across all bursts within the window), and intrasaccadic frequency (the reciprocal of each\noscillatory cycle duration, reflecting the oscillation rate within a saccadic burst). Notably,\nalthough oscillation frequency was partially correlated with saccade frequency, it exhibited\nindependent variability, as shown in the updated Figure 1.\nWe also appreciate your point regarding the nature of avian saccades. In pigeons, virtually all\nsaccades contain oscillatory components (Wallman & Letelier, 1993), even when the head is\nfree to move (see new Supplementary video 6). Thus, our previous use of the term “oscillatory\nsaccades” was not intended to denote a distinct eye movement class, but rather to distinguish\nthem from the smooth, non-oscillatory saccades typical of primates. We recognize that this\nterminology may have caused confusion and have clarified it throughout the revised manuscript,\nnow referring simply to “saccades” when describing avian saccadic eye movements.\nFinally, following your helpful suggestion, we have included new data in the revised Figure 1\nthat schematically illustrates how saccade, oscillation, and intrasaccadic frequencies are defined\nand computed. This figure also summarizes how these measures vary across visual conditions,\nthereby providing a clearer conceptual and methodological foundation for subsequent analyses.\n- The \"customized MATLAB algorithm\" for detecting saccades should be described in\nmethods.\nAuthors: We greatly appreciate you for this suggestion. As recommended, we have added a\ndetailed description of the customized MATLAB algorithm in the revised Methods section\n(Line 522-529). The algorithm employs a peak–trough detection strategy using MATLAB’s\nfindpeaks function to identify oscillatory saccades. Raw EOG signals were band-pass filtered\n(5–60 Hz) to reduce noise and drift, and peaks and troughs exceeding three times the baseline\nstandard error were used to detect candidate segments and capture oscillatory cycles.\nConsecutive oscillatory cycles were combined to define the onset and offset of each saccade\nevent. This customized algorithm was reported in our previous study (Xiao et al., 2023) and is\npublicly available at: https://cdn.elifesciences.org/articles/78729/elife-78729-code1-v1.zip.\n- The authors state that the glucose recording and injection apparati don't have a major impact\non affect saccade frequency, but this data should be presented.\nAuthors: We agree with you that it is important to demonstrate how the glucose recording and\ninjection impact eye movements with further details. As you suggested, we have included these\ndata in the revised manuscript. Specifically, we assessed saccadic eye movements (saccade\nfrequency and oscillation frequency) during 10 minutes before and 30-40 minutes after\nimplanted the CGM sensor (saccade frequency: p = 0.63; oscillation frequency: p ≈ 1; n = 6;\ntwo-sided Wilcoxon signed-rank, Fig. R3a, b) and the microinjection apparatus (saccade\nfrequency: p = 0.84; oscillation frequency: p ≈ 1; n = 6; two-sided Wilcoxon signed-rank,\nFig. R3c, d). The results indicate that eye movement activity had recovered to pre-insertion\nlevels following either CGM sensor or injection apparatus implantation.\nFigure R3. Influence of CGM sensor and microinjection apparatus implantation on saccadic\neye movements. a, b, Saccade frequency (a) and oscillation frequency (b) measured before\n(black) and after implantation (gray) of the CGM sensor in six pigeons. Each gray line connects\nbefore and after implantation values for a single pigeon. Saccade frequency: p = 0.63;\noscillation frequency: p ≈ 1; n = 6; two-sided Wilcoxon signed-rank. c, d, Saccade frequency\n(c) and oscillation frequency (d) measured before (black) and after implantation (gray) of the\nmicroinjection apparatus in six pigeons. Each gray line connects before and after implantation\nvalues for a single pigeon. Saccade frequency: p = 0.84; oscillation frequency: p ≈ 1; n = 6;\ntwo-sided Wilcoxon signed-rank. n.s., not significant, two-sided Wilcoxon signed-rank test.\n2. The analysis of the temporal relationship between oscillatory saccades and neural responses\nhas several issues.\n- The entire analysis in Fig 2 is unusual. It would be helpful to see a more straightforward\napproach, e.g. apply a series of sliding average windows of increasing duration to both\nsaccade measures and neural response, then plot time-lagged cross-correlations. PCA also\nseems unnecessary here. It would be more informative to show the same cross correlation\nanalysis for each saccade measure individually.\nAuthors: Thank you very much for the valuable comments regarding the analysis of the\ntemporal relationship between saccades and neuronal responses. Following your suggestion,\nwe performed time-lagged cross-correlation analyses using a series of sliding average windows\nof increasing duration applied to both saccade measures and neuronal responses. We then\ncomputed the time-lagged correlations between visual responses and eye-movement metrics:\nsaccade frequency, and oscillation frequency. Since the new analyses indicated that these two\nfrequencies shared a common trend but exhibited independent variability in response to visual\ninputs, we additionally included the PCA-derived combined frequency component (PC1) to\ncapture their shared variance. As examples, four representative analysis window durations, each\napplied with a sliding step of 1 s, are shown: 5 s, 30 s, 60 s, and 180 s (Fig. R4). The results did\nnot reveal a consistent or robust temporal relationship across the behavioral metrics. We venture\na guess that this outcome likely reflects the rhythmic nature of our experimental paradigm, in\nwhich visual stimuli are presented repeatedly at approximately 6-10-second intervals.\nMeantime, visual response trials evoked by grating motion that occurred during periods of\nperisaccadic modulation were excluded to eliminate perisaccadic effects. This removal of\noccasional trials in our analyses created discontinuities within the data series. Thus, this\napproach might obscure trial-specific coupling between eye movements and individual visual\nevents, thereby reducing sensitivity to immediate, event-locked neuronal–behavioral\nrelationships.\nFigure R4 Time-lagged cross-correlation between eye movement metrics and visual responses\nacross four analysis window durations. a-c, Correlation coefficients as a function of time lag\nfor a 5-s analysis window, between saccade frequency and visual responses (a), oscillation\nfrequency and visual responses (b), and PC1 and visual responses (c). d-f, The same\ncomparisons using a 30-s analysis window. g–i, The same comparisons using a 60-s analysis\nwindow. j–l, The same comparisons using a 180-s analysis window.\nInspired by the suggestion, we updated the approach used in the previous submission by\napplying sliding-window averaging with durations ranging from 1 to 480 s to both saccade\nmetrics and neuronal responses, and computed time-lagged cross-correlations across lags from\n1 to 360 s. The analyses were performed separately for saccade frequency and oscillation\nfrequency, as well as for a PCA-derived component combining both metrics, to visualize their\ndistinct temporal profiles. As shown in Fig. R5, the cross-correlation maps reveal broad regions\nof positive correlation across lag ranges comparable to those observed in the original analysis,\nindicating a delayed yet temporally distributed modulation of neuronal activity following\nincreased saccadic behavior. The corresponding significance maps for saccade frequency and\nPCA show that these effects are most pronounced at ~150-s lags when using 20-80-s windows,\nand at ~1–30-s lags when using 150–220-s windows. The oscillation-frequency panel exhibits\nweaker significance overall but a similar tendency, with effects most evident at ~1-30-s lags\nwhen applying 20-150-s windows. These data suggest that enhanced neuronal responses follow\n~3 min periods of heightened saccadic activity Fig. R5a, c, e.\nFurthermore, when we zoom in on the time lag range of 0–10 s, Fig. R5b, d, f, we observe that\nthe negative correlation present around time lag = 0, which is consistent with our previous\nresults at the second-scale resolution. However, this negative correlation disappears for time\nlags greater than 1 s. This is because the negative correlation primarily arises from the influence\nof the preceding saccade; as the time lag increases, it effectively skips over part of the interval\nbetween the visual stimulus and the prior saccade, making it difficult to detect significant\nnegative correlations at later time lags.\nWe retain the PCA in our analysis because it captures the shared variance between saccade\nfrequency and oscillation frequency, two highly correlated yet distinct behavioral measures (see\nnew Fig. 1). Importantly, the PCA-derived results converge with those obtained from individual\nmetrics, reinforcing the robustness of the observed temporal relationships. We have included\nthese complementary analyses in the revised manuscript to provide a clearer and statistically\nvalidated picture of how neuronal responses are modulated following periods of increased\nsaccadic activity.\nFigure R5 Time-lagged cross-correlation between eye movement metrics and visual responses\nat fine temporal resolution. a-f, Population correlations between neuronal visual responses and\nthree distinct eye movement metrics across 54 neurons: (a, b) saccade frequency, (c, d)\noscillation frequency, and (e, f) PCA-derived component. Correlations were computed as a\nfunction of the duration of the preceding analysis window (1 second to 8 minutes) and the time\nlag relative to visual stimulus onset (0 second to 6 minutes). Left panels display mean\ncorrelation coefficients (r), and right panels show corresponding significance values (−log₁₀(p))\nobtained from a two-sided Wilcoxon signed-rank test. Heatmaps in (a, c, e) illustrate results\nover the full time-lag range (0 to 6 min, step = 0.5 s), whereas (b, d, f) provide magnified views\nof the 0 to10 s range. White outlines indicate time-lag and analysis window combinations where\ncorrelation coefficients were significantly different from zero at the population level.\n- Better controls are needed, e.g. a comparison to correlation coefficients from shuffled\nsession IDs. See Harris 2021 BioRXiv \"Nonsense Correlations in Neuroscience\". I am\nparticularly concerned that the authors may be treating each visual response as an independent\nsample, even through the preceding saccade histories overlap, and may be ignoring the\nobvious slow changes over time within the session seen in Fig 2c,d.\nAuthors: Thank you so much for raising this critical point and for suggesting the inspiring\nreference (Harris, 2021). We fully agree that, given slow temporal dynamics, it is essential to\nrule out “nonsense correlations” arising from sample non-independence and shared drifts.\nFollowing the suggestion, we implemented two shuffling-based control analyses to statistically\nvalidate the temporal coupling between saccadic and neuronal measures.\nFirst, we employed the Pseudosession Method, which tests whether the observed correlations\nare genuinely locked to stimulus timing or could emerge from chance alignments of slow\nfluctuations in eye movements and neuronal firing. For each neuron, we generated a surrogate\ntime series of stimulus onsets by randomly shuffling the inter-trial intervals (ITIs) from the\noriginal session, thereby preserving the ITI distribution while disrupting the true temporal\nalignment between saccadic history and visual stimuli. As shown in the left panel of\nSupplementary Fig. 7, the mean correlation coefficients from the real data (orange line)\nsystematically deviated from the null distribution (gray shaded area) obtained from the shuffled\nevent times, indicating that the correlations were specifically time-locked to the actual visual\nstimuli.\nSecond, we applied the Session Permutation Method, which assesses whether the correlations\nare specific to matched neuronal and saccadic metrics from the same recording session, rather\nthan reflecting shared slow drifts across sessions or animals. Each neuron’s recording (n = 54)\nwas treated as an independent session. We generated a null distribution by pairing the visual-\nresponse time series from one neuron with the saccadic-metric series from a different, randomly\nselected neuron. As shown in the right panel of Supplementary Fig. 7, the mean correlation\ncoefficients from the true pairings diverged from those in the shuffled dataset. Notably,\nsignificant negative correlations were observed at the second-scale and significant positive\ncorrelations at the minute-scale, confirming that the temporal relationships we observed are not\nartifacts of overlapping saccade histories or slow session-level drifts.\nWe have incorporated these two shuffling-based control analyses into the revised manuscript,\nwhich we believe provide additional support for our findings.\n3. The proposed mechanism for the two different timescales of the relationship between\noscillatory saccades and neural response doesn't make sense.\n- The authors imply that both the positive correlation at ~3-4 minute timescale and the negative\ncorrelation at <6 s timescale are due to slow dynamics of glucose diffusion from the pectin oculi\nto retinal ganglion cells. However, these timescales differ by over an order of magnitude.\nWhich is it? Does it takes seconds or minutes for glucose to diffuse and increase neural firing\nrates?\nAuthors: Thank you so much for this important comment. We have clarified the distinction\nbetween the two timescales in the revised manuscript.\nThe second-scale negative correlation (< 6 s) is directly supported by both the neuronal\nresponse data and our biophysical simulation (Fig. 3). Neuronal visual responses increased\nprogressively with longer stimulus-saccade intervals after each saccade (Fig. 3). Additional\nanalyses of the latest saccade duration further revealed that the longer saccades significantly\nenhanced subsequent neuronal visual responses, as your following comments suggested (see\ndetails later). These findings support a local convective–diffusive relaxation process triggered\nby fluid perturbations induced by a single saccade. Consistent with this interpretation, our\nsimulation based on Fick’s second law predicted that intraocular glucose concentration re-\nequilibrates within a few seconds over inner-retinal distances, matching the timescale of the\nobserved neuronal response dynamics. Thus, the second-scale modulation likely reflects a brief\nadjustment of nutrient gradients following eye motion.\nIn contrast, the minute-scale positive correlation (~3-4 min) represents a slow cumulative effect\nin which saccades, through fluid-dynamic agitation, enhance glucose diffusion relative to its\nmetabolic consumption, gradually improving nutrient availability in the avascular avian retina.\nRepetitive saccadic eye movements increase the effective diffusivity of glucose and promote\nmild convection, producing a slowly accumulating imbalance between metabolic consumptions\nand nutrient delivery, and consequently a gradual rise in intraocular glucose concentration. This\nlong-term process aligns closely with the delayed enhancement of intraocular glucose levels\nand neuronal responsiveness observed in the study.\nTogether, these findings support a dual-timescale mechanism: a rapid convective–diffusive\nrecovery that occurs within seconds after each saccade, and a slow cumulative enhancement of\nmetabolic support that builds up over minutes through repeated saccades. We have now clarified\nthat the slow correlation most likely reflects a cumulative metabolic process, whereas the fast\ncorrelation reflects a transient, mechanically induced modulation, indicating that distinct\nmechanisms rather than a single diffusion process spanning both timescales.\nAre there alternate explanations for the short + long time scale effects? E.g. since responses are\nmeasured downstream in retino-recipient areas, could post-saccadic suppression play a role?\nCould mechanical disturbance affect RGC firing?\nAuthors: Thank you for this insightful comment. We fully agree that post-saccadic modulation\nrepresents a critical mechanism that can influence neuronal activity following saccades. Indeed,\nour earlier study (Yan Yang et al., 2008) demonstrated robust peri-saccadic modulation in the\npigeon visual system, including transient suppression during saccades followed by a post-\nsaccadic facilitation phase (Fig. R6, adapted from Yan Yang et al. (2008)). In that work,\nneuronal activity in both the nucleus opticus principalis thalami (nOPT) and nucleus lentiformis\nmesencephali (nLM) exhibited a characteristic pattern of pre-saccadic inhibition followed by\npost-saccadic enhancement lasting serval hundred milliseconds. This temporal profile contrasts\nsharply with the present findings, in which neuronal visual responses exhibited gradual, multi-\nsecond modulations following the latest saccade onset.\nFigure R6 Perisaccadic modulation in visual neurons in pigeons (Yan Yang et al., 2008).\nNotably, to minimize potential contamination from peri-saccadic modulations, we implemented\nexclusion criteria in the present analyses. As detailed in the Methods, we first quantified the\nduration of peri-saccadic modulations by aligning neuronal firing rates to both the onset and\noffset of saccades. This duration was objectively determined using an SD-based criterion,\nidentifying the time point at which firing rate consistently returned to baseline stability. All\nvisual response data failing within these modulation windows were subsequently excluded,\nensuring that the analysis focused exclusively on stable fixation periods. Visual response to the\n300-ms grating probe were calculated by subtracting a baseline defined as the mean\nspontaneous firing rate during a 300-ms window aligned to the probe timing relative to the\npreceding saccade. Thus, we are confident that the second-scale effects analyzed in this study,\nsuch as the gradual increase process lasting up to ~6 s (Fig. 3), extend well beyond the temporal\nrange of the classic, sub-second peri-saccadic modulations.\nRegarding the possibility that mechanical perturbations of the eye might influence retinal\nganglion cell (RGC) activity, we agree that this is an important consideration. Prior work\n(Marrese et al., 2019) showed that sustained mechanical indentation ( 10 μm displacement for\n60 second) can modulate RGC firing rates. Although saccades in our dataset last only a few\n∼\nhundred milliseconds, and all data during saccades were excluded from the analysis, we\nacknowledge that subtle mechanical effects cannot be entirely ruled out. We have added this\npoint to the Discussion to provide a more comprehensive and balanced interpretation of the\nresults.\nOr, are there behavioral correlations that could explain this short timescale effect? E.g. are birds\nparticularly likely to make a saccade when glucose is already quite low, or are saccades\nregularly spaced in time? A more complete description of saccade statistics would likely help\nclarify this. Finally, could mechanical agitation both increase uptake of existing glucose on a\nshort timescale, and lead to increased glucose diffusion from the pectin over a longer timescale?\nIf the mechanism is totally unknown, that is fine, but the discussion should be more transparent.\nAuthors: We thank you for these constructive suggestions. We agree that potential behavioral\ncorrelates of the short timescale effects warrant careful examination. To address this, we\nanalyzed the temporal statistics of saccades. Our data show that saccades were not regularly\nspaced in time but instead occurred with variable inter-saccade intervals, following a right-\nskewed distribution (mean ± SD: 2.73 ± 1.93 s; Fig. R7). Moreover, bidirectional manipulations\nof intraocular glucose levels robustly modulated visual responses, but did not alter saccadic eye\nmovements, indicating that the observed short-timescale modulation of visual activity is\nunlikely to arise from regular saccade timing or preferential saccades triggered under low-\nglucose conditions.\nFigure R7. Distribution of inter-saccade intervals. Histogram showing the distribution of\ninter-saccade intervals across animals. The dashed line indicates the 95% distribution interval.\nRegarding the possibility that mechanical agitation enhances both glucose uptake and diffusion\nacross multiple timescales, direct physiological evidence is currently lacking. Nevertheless, this\nmechanism aligns well with the coexistence of both second- and minute-scale correlations\nobserved in our data. Specifically, transient mechanical agitation during individual saccades\ncould rapidly promote glucose diffusion and utilization that facilitate neuronal responses over\na few seconds, whereas cumulative increases in saccadic eye movements could gradually create\nimbalance between metabolic consumption and nutrient delivery, leading to a gradual rise in\nintraocular glucose concentration over minutes. We have revised the Discussion to clarify this\ninterpretation and to explicitly acknowledge the uncertainty surrounding the precise underlying\nmechanism. We appreciate your emphasis on transparency and have expanded the revised text\nto distinguish established physiological observations from more speculative hypotheses.\n- If the short-timescale hypothesis is true (due to the time it takes for glucose to reach cells),\nthen there should be a positive correlation between the duration and intra-saccade frequency of\noscillations and the firing rate.\nAuthors: We sincerely appreciate for this constructive comment. We agree that, under the short-\ntimescale hypothesis, if the observed modulation reflects the time required for glucose to\ndiffuse and reach retinal cells, both saccade duration and the interval from the latest saccade\nshould influence neuronal responses.\nTo disentangle the effects of these correlated factors, we performed correlation analyses across\n54 neurons (Fig. 2), applying linear regression models to control for one parameter while\nexamining the other. The population analyses still revealed significant positive correlations\n(saccade duration: r = 0.05, p < 0.001; oscillatory cycle count: r = 0.05, p < 0.001; multiple\nlinear regression; Fig. R8a, b) between the time from the latest saccade and the corresponding\nvisual responses when controlling for saccade duration and oscillatory cycle count, respectively.\nSimilarly, significant positive correlations (saccade duration: r = 0.37, p = 0.04; oscillatory\ncount: r = 0.02, p = 0.04; multiple linear regression; Fig. R8 c, d) were observed between\nsaccade duration, oscillatory cycle count and visual responses when controlling for the time\nfrom the latest saccade.\nFig. R8. Partial correlation between normalized visual response and features of the last\nsaccade.\na, Relationship between normalized visual response and time since the last saccade, controlling\nfor saccade duration. Residuals of visual response (after removing variance explained by\nsaccade duration) are plotted against residuals of time since the last saccade. r = 0.05, p < 0.001,\nmultiple linear regression. b, Relationship between normalized visual response and time since\nthe last saccade, controlling for the oscillatory cycle count of the last saccade. r = 0.05, p <\n0.001, multiple linear regression. c, d, Relationship between normalized visual response and\nsaccade duration (c, r = 0.37, p = 0.04, multiple linear regression), and oscillatory cycle count\n(d, r = 0.02, p = 0.04, multiple linear regression), controlling for time since the last saccade.\nRegarding the intrasaccadic frequency of oscillations, we note that this measure corresponds\nmore closely to the intrasaccadic frequency as defined in the revised Figure 1. The intrasaccadic\nfrequency reflects the oscillatory cycle frequency within individual saccade bursts and\nremained stable across different visual contexts, suggesting it may represent an intrinsic\nproperty of saccades rather than a modifiable factor. Therefore, it was not included in the\npresent correlation analysis.\nThese findings support the interpretation that the short-timescale effects arise from oscillatory\nmechanical agitation that transiently facilitates rapid glucose delivery to metabolically active\nretinal regions. We have incorporated these new results into the revised manuscript, which we\nbelieve provide additional support for the short-timescale hypothesis.\n- The authors don't mention oxygen diffusion at all, even though is is a previously proposed role\nfor oscillatory saccades. Could other nutrients such as oxygen diffuse at different rates?\nAuthors: Thank you very much for highlighting the potential role of oxygen diffusion. While\nour study primarily measured glucose dynamics, we agree that other nutrients, particularly\noxygen, could contribute to the functional role of saccades. Oxygen diffusion and blood flow\nare indeed essential for retinal metabolism. Wingstrand et al. reported the crucial role of the\npecten in maintaining intraocular oxygen supply (Wingstrand & Munk, 1965). In anesthetized\npigeons, they observed an oxygen partial pressure gradient from the pecten to the retinal\nperiphery, and vessel occlusion caused a sharp drop in oxygen tension accompanied by retinal\ndegeneration. Wallman et al. further proposed that saccadic oscillations may facilitate the\ndiffusion of oxygen and nutrients from the avian pecten to the retina. However, direct\nexperimental evidence supporting this hypothesis remains limited.\nIn the present study, we directly monitored intraocular glucose dynamics using a continuous\nglucose monitoring (CGM) system and found a temporal correlation between glucose\nfluctuations and saccade activity. We have now added a statement in the Discussion\nacknowledging that oxygen (and potentially other metabolic substrates) may also play a role\nand that differences in their diffusion and transport properties could modulate their\nphysiological relevance. We explicitly state that our conclusions are based on glucose-\ndependent evidence and do not exclude possible contributions from oxygen or other nutrients.\nFuture work may test whether saccades similarly modulate intraocular oxygen levels and visual\nsensitivity.\n- Can the causal manipulations provide some insight? What is the temporal relationship between\nthe increase in intraocular glucose concentration caused by injection, and the increase in neural\nresponse? The Raphe inhibition experiments demonstrate a 3-4 min lag between decreased\nsaccades and decreased glucose concentration, but what is the time lag with the neural response?\nAuthors: We sincerely thank you for this valuable suggestion, which has helped us improve\nboth the precision and the mechanistic clarity of the revised manuscript.\nFollowing the direct D-glucose injection, our analysis showed that the enhancement of neuronal\nresponses was very rapid, with a latency of only 0.25 minutes (15 seconds, p < 0.05).\nConversely, inhibiting glucose transport with Bay-876 resulted in a decrease in neuronal\nresponses with a latency of 1.25 minutes (p < 0.05). These findings confirm that neuronal\nactivity is indeed highly sensitive to local glucose availability. We have clarified the\nmethodology for this analysis in the revised Methods section:\n\"To assess post-injection dynamics of neuronal activity, data were aligned to the injection time\nand analyzed in 0.5-min windows. Activity in each window was z-score normalized to the pre-\ninjection baseline (outliers >3 standardized MAD excluded). The latency of significant change\nwas defined as the median time of the first window differing from baseline.\"\nRegarding the Raphe complex inhibition experiment, our data demonstrate a 3-4 minute lag\nfrom the reduction in saccadic metrics to the decline in intraocular glucose concentrations (Fig.\n5e) . To address the specific question about the lag with the neuronal response, our new analyses\nrevealed a consistent delay of about 4.3 minutes from the muscimol injection to the attenuation\nof neuronal responses. Crucially, we also determined there was a 0.5-0.7 min delay between the\ninjection and the actual suppression of eye movements (Supplementary Fig. 3f). Therefore,\nwhen accounting for this initial pharmacological delay on eye movements, the resulting lag\nbetween the cessation of saccades and the subsequent decline in neuronal responses is\napproximately 3.6-3.8 minutes. The close correspondence between these two key time lags: the\n3.6–3.8-minute delay from reduced saccades to neuronal responses inhibition and the 3-4-\nminute delay from reduced saccades to decreased intraocular glucose concentrations (Fig. 5e),\nsuggest that within approximately 3-4 minutes after saccades cease, both glucose supply\ndisruption and neuronal activity decline occur nearly simultaneously.\n4. Some conclusions are overstated\n- Lines 99-101. \"These findings indicate that attention-driven oscillatory saccades in birds\nenhance glucose mobilization from the pecten oculi to the retina, thereby boosting nutrient\nsupply during social information gathering\" This conclusion is overstated at this point since\ncausality has not yet been established. It is plausible at this point that viewing a salient stimulus\ncauses other changes in attention and arousal that lead to both increased glucose circulation\n(with a delay) and increased saccade rates. A similar comment applies to lines 113-116.\nAuthors: We thank you so much for this important comment. We agree that the original wording\noverstated the causal link between oscillatory saccades and glucose mobilization prior to the\nresults of pharmacological manipulations. At this part, our data demonstrate a temporal\nassociation between saccade rates and intraocular glucose fluctuations, but do not yet establish\na direct causal mechanism. Accordingly, we have revised the text at both locations to present\nour interpretation more cautiously, emphasizing that saccades are correlated with, rather than\ndirectly responsible for, enhanced glucose mobilization during salient visual events.\n- Line 284 - \"Meanwhile, avian visual responses are not only sustained, but dynamically\noptimized to efficiently process salient and intricate visual stimuli by enhancing retinal energy\navailability\" What does this sentence mean? I don't believe the authors distinguish between the\neffects of oscillatory saccades on sustained vs. transient neural responses. Similarly, what is the\nevidence for \"precise shaping of retinal function\" -- the authors do not demonstrate sharpening\nof receptive fields, for example.\nAuthors: Thank you so much for the valuable comment. We apologize that the original wording\n(“sustained” and “precise shaping”) was misleading and did not accurately reflect our results.\nWe do not distinguish between sustained and transient neuronal responses, nor do we provide\nevidence for receptive-field sharpening. Our findings instead show that avian saccades are\nclosely coupled with fluctuations in intraocular glucose levels, suggesting a mechanism that\nhelps maintain retinal energy availability during visual processing. Accordingly, we have\nrevised the statement to read more conservatively, noting that saccades may support retinal\nglucose supply, thereby facilitating visual processing.\n- Line 292 \"Our findings provide novel insights into the critical role of avian oscillatory\nsaccades and extend their significance beyond basic visual scanning to metabolic and functional\noptimization.\" Previous studies cited don't seem to suggest that the oscillatory component of\navian saccades is used for visual scanning/gaze orienting. How could a fast ~30 Hz oscillation\npossibly achieve scanning? \"Moreover, they raise broader implications regarding the role of\neye movements in maintaining retinal health, enhancing attentional performance, and\noptimizing visual function across species.\" What are these broader implications exactly?\nAuthors: We fully understand your concerns and appreciate the opportunity to clarify this point.\nFirst, in pigeons, all saccades are inherently oscillatory(Wallman & Letelier, 1993). The\noscillatory component is an intrinsic feature of each saccade rather than a distinct type of eye\nmovement. Each saccadic eye movement therefore combines a positional shift with embedded\nhigh-frequency (~30Hz) oscillations in pigeons (Nye, 1969). As in mammals, one function of\na saccade in birds is to reorient gaze toward a new location of interest. We apologize that the\nterm “oscillatory saccades” in the previous version may have been misleading. In the revised\nmanuscript, we have clarified three properties of avian saccades in Figure 1. Among them, the\n~30 Hz oscillatory frequency itself, as correctly note, is too fast to serve a visual scanning or\ngaze orientation, which are accomplished by the overall saccadic displacement.\nSecond, during saccade, visual input is likely blurred, akin to viewing a scene through a rapidly\nshaking camera. However, these blurred images are not normally processed by the brain\nbecause of saccadic visual suppression. Previous studies have demonstrated that, in pigeons,\nactivity along the primary visual pathways, from the nOPT to higher visual centers, is strongly\nsuppressed during saccades by corollary discharge signals from the Raphe complex (Yan Yang\net al., 2008), paralleling the phenomenon of saccadic suppression in humans that prevents\nperception of motion blur during rapid eye movements.\nTaken together, these observations suggest that saccadic eye movements in pigeons may serve\ndual roles: beyond their gaze orienting function, they may also contribute to maintaining retinal\nhealth and metabolic homeostasis through movement-coupled perfusion and nutrient-delivery\nmechanisms. We have revised the corresponding text to clarify these distinctions and to better\narticulate the broader physiological implications.\nMinor comments\n1. Line 78 - \"unlike primates, birds produce oscillatory saccades spanning tens to hundreds of\nmilliseconds per event\" - As written, the implication is that birds only display oscillatory\nsaccades.\nAuthors: We appreciate your careful reading. To clarify, birds indeed produce saccades that\ninherently contain oscillatory components, rather than distinct “oscillatory” and “non-\noscillatory” saccade types. This has been well documented in the literature. For example,\nWallman and Letelier (1993) noted that “in chickens and pigeons it is clear that all saccades\ninvolve oscillations.” Consistent with this, as described in our Methods (Data Analysis section),\n“a typical saccadic eye movement consists of several oscillatory cycles occurring at a frequency\nof approximately 18–44 Hz.” We have carefully revised the manuscript, especially in the\nIntroduction and Results sections to make this point explicit and to avoid unintended\nimplication that only certain saccades are oscillatory.\n2. 81 - \"These oscillatory saccades serve for efficient environmental scanning and attention-\ndriven visual processing 36,40.\" - What is meant by this sentence? Has a causal link been\npreviously demonstrated between attentional behavior and oscillatory saccades specifically?\nEnsure references support the statement for oscillatory saccades specifically. E.g. Ref 36\nmentions oscillatory saccades but doesn't consider further. Similarly, line 84 - \"visually-guided\nsaccades\".\nAuthors: Thank you so much for this insightful comment. We agree that our original phrasing\nmay have implied a causal relationship between saccades and attentional processes, which was\nnot directly demonstrated in the study. Accordingly, we have revised the sentence to clarify that\nour data were obtained under awake, head-fixed condition, where saccades were examined\nacross different visual contexts rather than in explicit attention tasks. We re-examined the cited\nreferences and confirmed that Ref. (36) describe the presence and potential functional relevance\nof saccades during visual exploration, but not establish a direct causal link with attention. To\navoid overinterpretation, we have removed the phrase “attention-driven visual processing” and\nensured that all remaining references accurately support the revised statement (Bloch et al.,\n1984; Lemeignan et al., 1992; Pratt, 1982; Tyrrell et al., 2015; Voss & Bischof, 2009; Yorzinski\n& Platt, 2014).\n3. Rates of saccades and oscillation frequency. It would be informative to give baseline rates\nof saccade frequency/oscillation frequency/glucose levels when these results are first\npresented, and their new values upon viewing the mating scene. Conversely, exact values for\nan example bird are unnecessary. Later in the text (lines 217-220), when baseline saccade and\noscillation rates are given, the values are surprisingly low compared to previous reports (e.g.\nNye 1969). It is understandable if this is due to the invasive nature of glucose monitoring, but\nif so, it should be acknowledged. I also question whether the oscillation frequency reported\nafter muscimol injection (0.04 Hz = 25 s period) is meaningful.\nAuthors: We appreciate your insightful feedback on the definition and interpretation of our\nbehavioral metrics. As noted, “saccade frequency” (or rates of saccades) and “oscillation\nfrequency” are central to our study but were not clearly defined in the original submission.\nSaccades are rapid, oscillatory bursts of eye movements at periodic intervals in pigeons (Nye,\n1969; Wallman & Letelier, 1993). To capture their temporal organization, we included a\ndetailed definition in the revised manuscript: 1) Saccade frequency: the number of detected\nsaccades per analysis window, representing the rate of oscillatory episodes occurred. 2)\nOscillation frequency: the total number of oscillatory cycles embedded within all saccades in\nan analysis window, representing the overall temporal density of oscillatory activity. 3)\nIntrasaccadic frequency: the reciprocal of each oscillatory cycle duration, reflecting the\noscillation rate within a saccadic burst (typically 20–30 Hz in our data, consistent with Nye\n(1969). The revised Fig. 1c now systematically illustrated theses three frequency-related\nmetrics, saccade frequency (F ), oscillation frequency (F ), and intrasaccadic frequency\nsac osci\n(F ) along with their relation to the established characteristics of avian saccadic eye\nintra\nmovements in the literature. The updated figure also shows how these metrics vary across\ndifferent visual conditions in pigeons (Fig. 1d, e). These new results demonstrate that only\nsaccade frequency and oscillation frequency changed significantly across viewing conditions,\nthereby providing a clearer foundation for subsequent analyses and interpretation.\nRegarding the lower baseline frequencies, the higher “oscillation frequencies” (~20-30 hz)\nreported in the earlier studies correspond to intrasaccadic frequency, not to the oscillation\nfrequency as defined in this study. Our intrasaccadic frequency (20–30 Hz) is fully consistent\nwith prior findings (Letelier et al., 1999; Nye, 1969; Yang Yang et al., 2008), while the saccade\nfrequency values closely match those in previous reports (Yan Yang et al., 2008). Finally, the\n0.04 Hz value reported after muscimol injection represents the oscillation frequency, not the\nintrasaccadic frequency within a burst. This low value reflects the elimination of saccadic\noscillation events following Raphe inactivation. We have clarified this distinction in the revised\nResults section.\n4. Values are given to unnecessarily high precision, see standards for significant figure\nreporting.\nAuthors: Thank you for the important suggestion. We agree that some values were reported\nwith unnecessarily high precision. In the revised manuscript, we have rounded all numerical\nresults to an appropriate number of significant figures, following the standards for significant\nreporting, ensuring clarity and readability without overstating precision.\n5. Fig 1c - why does intraocular glucose increase before the video onset? What is the\nsmoothing window?\nAuthors: Thank you so much for the comment. In Fig. 1c, the apparent increase in intraocular\nglucose prior to video onset in the single-animal example likely reflects spontaneous\nphysiological fluctuations. Details regarding the analysis window are provided in our response\nto the following comment. To ensure greater representativeness, we reanalyzed the data using\nthe averaged responses from four animals instead of a single example. To avoid potential\nadaptation, we only analyzed the data from the first trial each animal viewed the social video,\nwhen they were naive to the specific visual stimuli. The pre-onset increase was no longer\nobserved across populations in the updated version.\nThis change in analysis also updated the overall observed increases. When using all trials, the\nincreases were 16.08 ± 8.59% for saccade frequency (p < 0.001), 6.74 ± 2.30% for oscillation\nfrequency (p < 0.001), and 4.94 ± 1.55% for intraocular glucose (p < 0.001). The revised\nanalysis using only the first trial yielded increases of 7.70 ± 4.97% for saccade frequency (p <\n0.001), 7.97 ± 4.90% for oscillation frequency (p < 0.001), and 2.25 ± 2.80% for intraocular\nglucose (p = 0.03).\n6. Fig 1d - What do individual data points represent and how are they calculated (e.g. over\nwhat time period is the response and baseline calculated)?\nAuthors: Thank you for your attention to the details of our data representation. In the previous\nversion, Fig. 1d showed data from an example animal. Each data point represented the\nnormalized saccade frequency (x-axis) and oscillation frequency (y-axis) within a 1-minute\ntime window. Frequencies were calculated using a moving-window approach, in which a 1-\nminute window was slid across the entire experimental timeline in 0.1-minute (6-second) steps,\nand frequency values were determined by counting the number of events occurring within the\n1-minute window centered on each time point (30 seconds before and 30 seconds after). All\nfrequency values were normalized relative to a baseline period defined as the 30-minute gray-\nscreen phase preceding social video playback, with the mean baseline frequency used as the\nreference.\nPer your and other reviewers’ constructive comments, we have clarified the quantification of\navian saccadic metrics across distinct visual conditions and compared the relationships between\nsaccade frequency and oscillation frequency in the revised version. Each data point now\nrepresents the normalized saccade frequency (x-axis) and oscillation frequency (y-axis)\ncalculated within discrete, non-overlapping 1-minute time windows (Fig. 1e). To illustrate how\nsaccades were evoked by visual stimuli, we reordered the presentation of results to begin with\n“simple” luminance transitions between light and dark screens, followed by the viewing of\nsocial mating videos. Accordingly, the population responses of saccadic activity and intraocular\nglucose fluctuations during social video viewing were averaged across four animals and\nnormalized to the preceding 30-minute gray-screen phase (Fig. 1q).\n7. Fig. 3j - What is the model?\nAuthors: Thank you for your question concerning the model, which is important in the study.\nThe model implanted in Fig. 3j is a conceptual diffusion-consumption framework designed to\nsimulate intraocular glucose concentration dynamics under the influence of saccades. We\nadopted this simplified approach to estimate how saccadic oscillations might modulate glucose\naccessibility at different retinal loci.\nA full description of the model is now included in the revised Methods (Lines 620–663). The\nmodel is based on Fick’s second law of diffusion, extended to include a glucose consumption\nterm and a periodic mechanical perturbation term to represent saccadic effects. Anatomical and\nphysiological parameters (e.g. ocular radius, baseline glucose concentrations, metabolic\nconsumption rates, and diffusion coefficients; (Braun & Sweazea, 2008; Del Amo et al., 2017;\nDonovan, 1978; Koirala et al., 2022; Von Eugen et al., 2022)) were drawn from published data\nor experimentally measured values. Two retinal distances were modeled: one proximal (~1 mm\nfrom the pecten) and one more distal (~4 mm from the pecten). The simulation yields time-\ndependent glucose concentration curves at each location, illustrating the relative effect of\nsaccadic activity on nutrient distribution and spatial gradients. While the model does not capture\nthe full complexity of three-dimensional vitreous fluid flow and biochemical interactions, it\nprovides a quantitative framework for comparing glucose dynamics between regions and\nassessing the potential contribution of saccadic eye movements to nutrient delivery. We have\nupdated the legend of Fig. 3 and added a detailed description in the Methods to clarify the\nmodel’s construction and underlying assumptions, which we believe will enhance clarity and\ninterpretability for readers.\n8. Fig. 4c - What is the color scale? How can neurons have a response of -30 sp/s? Also please\nadd start & ending trial numbers.\nAuthors: The color scale in Fig. 4C represents baseline-corrected neuronal firing rates (spikes/s).\nIn this study, a 300-ms grating motion served as a probe to assess neuronal visual responses,\nand firing rates were computed in 50-ms bins. Baselines were defined as average spontaneous\nfiring rates during saccade intervals without probes, aligned to the time point of prior probe.\nVisual responses to the grating motion were obtained by subtracting this baseline from the\nprobe-evoked firing rate. Accordingly, negative values indicate suppression below baseline\nfiring, whereas positive values indicate excitatory responses to the grating motion. In\naccordance with your suggestion, we have added annotations for the starting and ending trial\n(probe) numbers in the revised figure.\n9. Line 171 - What does \"A 5.00-6.00\" refer to?\nAuthors: Thank you for raising this point. \"A 5.00–6.00\" refers to stereotaxic coordinates of the\npigeon brain. Our coordinate system follows the standards established by Karten and Hodos\n(1967), which remains the reference for avian brain anatomy. Here, \"A\" stands for anterior,\nindicating a position along the anterior–posterior axis relative to the reference point (bregma).\nThe values 5.00–6.00 denote the distance in millimeters (mm) from bregma. In avian\nneuroanatomy, bregma (the intersection of the sagittal suture and the mid-plane of the ear canals)\nis conventionally used as the origin for stereotaxic coordinates, from which anterior (A),\nposterior (P), dorsal (D), ventral (V), medial (M), and lateral (L) directions are defined. We\napologize for the lack of explicit unit labeling in the manuscript. In the revision, we have added\n“mm” when these coordinates first appear, included a brief explanation of the notation, and\ncited the stereotaxic atlas for clarity.\n10. What is the effect of muscimol injections in the Raphe nucleus on both regular saccades\nand oscillatory saccades? It is unclear if effects are due to suppression of the oscillatory\ncomponent specifically.\nAuthors: We appreciate for the important question. In pigeons, each saccade comprises of a\nburst of oscillatory cycles. The Raphe complex is critical for generating the oscillatory\ncomponent. Muscimol injections into the Raphe complex selectively suppressed the oscillatory\nactivity. Thus, Raphe complex inactivation does specifically disrupt oscillatory dynamics,\nsupported by our previous lesion studies (Yan Yang et al., 2008; Yang Yang et al., 2008). We\nhave clarified this point in the revised manuscript.\n11. Discussion - Studies have demonstrated dramatic differences in eye movements in head\nfixed vs. unrestrained birds. See Land 2015 and Gioanni 1988. Are oscillations equally\nimportant when the head is free to move? Have they been demonstrated in both head fixation\nand free motion?\nAuthors: We appreciate you for raising this important point and for highlighting the relevant\nreferences. Eye movements during optokinetic nystagmus (OKN) can indeed differ between\nhead-fixed and unrestrained conditions, especially in the gain relationship between the slow\nphase of OKN and the stimulus velocity. Nevertheless, the oscillatory component of saccades\nhas been shown to be a robust and consistent feature across behavioral conditions. Multiple\nstudies employing high-temporal resolution methods, including search coil system and EOG\nrecordings, have demonstrated that oscillations occur during both spontaneous saccades and the\nquick phases of OKN in head-free as well as head-fixed birds (Gioanni, 1988; Land, 2015;\nWallman & Letelier, 1993; Xiao et al., 2023; Yan Yang et al., 2008; Yang Yang et al., 2008). To\nfurther illustrate this point, we have added a supplementary video (Supplementary Video 6)\nshowing saccades with oscillatory components in a head-free pigeon, observed both when the\nhead was still and when it was moving. We also compared stimulus-driven saccades during\nviewing of light, dark, and gray screens, as well as during presentation of social mating videos\n(updated Fig. 1). In the present study, head fixation was applied to ensure stable recordings of\nwell-isolated single-neuron activity. These methodological considerations have been clarified\nin the revised Methods section.\n12. Line 274 - Reference 50 doesn't contain any mention of the Raphe complex. It would be\nhelpful to clarify the proposed role of the Raphe complex in generation of regular saccades vs.\noscillatory saccades.\nAuthors: We thank you for the careful observation. You are correct that Reference 50 does not\nexplicitly mention the raphe complex. Instead, it provides a broad review of brainstem\nmechanisms underlying saccade control, particularly the role of omnipause neurons (OPNs).\nThe Raphe complex, especially the nucleus raphe interpositus (RIP), contains OPNs that fire\ntonically during fixation but pause during saccades in all directions, thereby gating saccade.\nIn birds, virtually all saccades contain oscillatory components (Wallman & Letelier, 1993),\neven when the head is free to move (see new Supplementary Video 6). Our previous lesion studies\nhave shown that disruption of the Raphe complex eliminate saccadic oscillations dramatically\n(Yan Yang et al., 2008). Thus, the Raphe complex plays a key role in generating avian saccades.\nIn light of this clarification, we have removed the original citation and revised the text to clearly\ndescribe the specific features of avian saccades and the role of the Raphe complex.\n13. Line 321-322. I believe the authors mean \"intraocular\", not \"interocular\".\nAuthors: Thank you so much for catching this mistake. We have corrected interocular to\nintraocular in the revised manuscript.\n14. Methods: How many individuals of the non-pigeon species were used? Were the surgical\nprocedures the same for these animals (the methods refer to pigeons when describing most\nprocedures)?\nAuthors: We thank you for this important question regarding animal use and procedures. As\nshown in Figure 1, we included four additional bird species besides pigeons, with one individual\nfrom each species. Eye movements in these birds were recorded using electrooculography\n(EOG), following the same surgical and recording procedures described for pigeons. Among\nthem, only one chukar partridge was euthanized for eyeball collection after the procedure\n(Supplementary Fig. 1c). All animals were housed and cared for in accordance with institutional\nanimal care guidelines and approved protocols, and with full consideration for animal welfare.\n15. Methods, \"Visual conditions\" - How many 30 or 60 min visual presentations were delivered\nin a single session? Were animals given food and water during sessions?\nAuthors: Thanks for this question. In the video presentation experiments, pigeons were exposed\nto a gray screen for 30-40 minutes, followed by a 30-minute presentation of mating videos (Fig.\n1p in the revised version). For the light/dark stimulation experiments, the screens alternated\nbetween white (RGB: 255, 255, 255) and black (RGB: 0, 0, 0) at 60-minute intervals (Fig. 1f,\nk in the revised version). Each behavioral session lasted approximately 3-4 hours. Animals were\nnot provided with food or water during the sessions but had free access to both before and after\nthe experiments when in their home cages. We have added these details to the Methods section.\n16. Line 480 - \"saccades\" – typo\nAuthors: Thank you for catching this typo. We have corrected the sentence in the revised\nmanuscript.\n17. Figures are very low resolution\nAuthors: We thank you for pointing this out. All figures have been updated with high-resolution\nversions to improve clarity and readability. We believe these revisions will help readers better\ndiscern the details of the data presentation.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nAuthors: We greatly appreciate your remarks and your contribution to the review of our\nmanuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have properly responded to my questions. This is a fine paper now.", "reviewer_2": "The authors have provided all requested clarifications and addressed all of our questions from the previous round of review.\nThe adjustment of how the findings were framed, methodological clarifications, addition of support analyses, and\nimprovements to figures and descriptions have strengthened the authors’ work.\nMajor Comments:\nFigure 1d:\n1. While the one-way ANOVA shows a significant effect, it does not indicate which of the L, D, V, and G groups differ\npairwise—only that at least one pair differs. Post-hoc pairwise comparisons (such as Tukey's HSD or corrections for multiple\ncomparisons) are needed to identify which specific conditions differ.\n2. Additionally, ~770 time windows are treated as independent observations (df = 771, 768), but these are repeated\nmeasurements from continuous recordings within the same pigeons. Time windows from the same animal and the same\nrecording are not independent. They share individual-level variance and are temporally correlated. We suggest using a\nrepeated-measures ANOVA, or a linear mixed-effects model with pigeon identity as a random effect to properly account for\nnon-independence, where possible.\nOverall, the study is technically impressive and will be of broad interest. We do not need to review the revised manuscript\nagain, provided that the Nature Communications editorial team ensures that the authors include the required methodological\ndetails. Once the comments shared in this round are incorporated, the manuscript will be ready for publication.", "reviewer_3": "The authors have thoroughly addressed my main concerns with the original manuscript. My only remaining comment is\nregarding my original major point 2: Thank you for including this more systematic analysis of the temporal relationship\nbetween neural activity and saccade frequency. The analysis is quite helpful, but I am concerned by the diagonal structure in\nSupplementary Fig 8. Are the authors sure that this is not an artifact of how the sliding windows are centered in the analysis?\nIt seems odd that the optimal time lag should shift so systematically with the window width. It is possible that a\nmethodological issue is obscuring a more consistent result across window widths, compatible with the ~3 min delays\nreported elsewhere in the paper. (There is also an issue with the tick marks in FigS8e, right panel.) I urge them to take a\nlook, but overall recommend the article for publication.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Authors: We appreciate the time and effort you dedicated to reviewing our work. Your\nconstructive comments in the previous round have substantially improved the quality of this\nmanuscript.\nAuthors: We sincerely thank you for the favorable assessment of our revised manuscript and\nfor the helpful recommendations regarding the statistical methodology of Fig. 1d. We fully\nagree that 1) post-hoc tests are needed to identify which conditions differ after a significant\nomnibus ANOVA, and 2) treating time windows as independent observations can inflate the\ndegrees of freedom because repeated measurements from the same pigeon are not independent.\nPer your suggestions, we have added multiple-comparison-corrected post-hoc pairwise\ncomparisons and re-analysed the data using a linear mixed-effects model (LMM), with pigeon\nidentity included as a random effect to account for repeated measurements.\n1) Post-hoc pairwise comparisons following the one-way ANOVA\nAs you noted, a one-way ANOVA indicates that at least one group differs but does not identify\nwhich pairs of conditions differ (Light, Dark, Video, and Gray). We therefore have added\nTukey’s HSD post-hoc tests following each ANOVA to perform pairwise comparisons with\nappropriate control for multiple testing.\ni. Saccade frequency (Fig. R1a): the dark condition was significantly lower than the light,\nvideo, and gray conditions (all p < 0.001). Additionally, light was higher than gray (p =\n0.02), and video was higher than the gray condition (p < 0.001).\nii. Oscillation frequency (Fig. R1b): the dark condition was significantly lower than the\nvideo condition (p < 0.001), and the video condition was significantly higher than the\ngray condition (p < 0.001).\niii. Intrasaccadic frequency (Fig. R1c): no significant pairwise differences were observed\namong conditions (all Tukey-adjusted p > 0.05).\nFig. R1 | Pairwise comparisons of eye movement metrics across visual conditions.\na-c, Pairwise comparison matrices for saccade frequency (a), oscillation frequency (b), and\nintrasaccadic frequency (c) derived from one-way ANOVA followed by Tukey’s HSD post-hoc\ntests. d-f, Pairwise comparison matrices for the same metrics derived from a linear mixed-\neffects model followed by Wald tests, with Pigeon ID included as a random effect to account\nfor repeated measurements. Colors and numbers indicate the significance level (p values) of\ndifferences between conditions (Dark, Light, Gray, and Video). * p < 0.05, ** p < 0.01, *** p\n< 0.001.\n2) Accounting for non-independence across time windows (repeated measurements)\nWe agree with you that, in the previous version, the non-overlapping 1-minute time windows\nsampled from the same pigeons were not independent. To address this, we re-analyzed each\nsaccade metric using a linear mixed-effects model, with “Condition” as a fixed effect and\n“Pigeon ID” as a random intercept: Frequency ~ Condition + (1 | Pigeon ID). This approach\naccounts for between-subject variability and avoids inflating degrees of freedom due to within-\nsubject repeated sampling. We also conducted post-hoc pairwise comparisons within the LMM\nframework (estimated marginal means with Wald tests and multiple-comparison correction).\nThe LMM results confirm and strengthen our conclusions.:\ni. Saccade frequency (Fig. R1d): The fixed effect of condition remained highly significant,\nF(3, 771) = 52.29, p < 0.001. Post-hoc tests indicated significant differences for most\ncondition pairs, except light vs. video.\nii. Oscillation frequency (Fig. R1e): The fixed effect of condition was also significant, F(3,\n768) = 16.63, p < 0.001. Post-hoc tests showed significant differences for most pairs,\nexcept gray vs. dark and gray vs. light.\niii. Intrasaccadic frequency (Fig. R1f): no effect of condition was observed (F(3, 765) =\n0.29, p = 0.83), and no significant pairwise differences were detected.\nWe have updated the manuscript to include these additional statistical procedures in detail and\nadded a new supplementary figure (Supplementary Fig. 2) summarizing the full pairwise\ncomparison matrices for both the ANOVA with Tukey’s HSD and the LMM-based post-hoc\ntests (Wald tests).\nAuthors: Thank you so much for your careful reading and the positive assessment of the\nrevision. We also appreciate for raising this important methodological question regarding the\ndiagonal structure in the supplementary Fig. 8 (now updated as Supplementary Fig. 9).\nTo assess whether the apparent systematic shift in optimal lag with window width could be an\nartifact of how the sliding windows are referenced/centered, we re-ran the analysis using\nalternative definitions of the time lag. Specifically, we defined the lag relative to (i) the end of\nthe window (trailing-edge; Supplementary Fig. 9), (ii) the midpoint of the window (centered,\nFig. R2a, c, e), and (iii) the onset of the window (leading-edge, Fig. R2b, d, f). Across all three\ndefinitions, the resulting correlation maps were qualitatively similar and consistently exhibited\na clear diagonal ridge (Fig. R2a-f), indicating that the structure is not driven by window\ncentering/alignment choices or by the particular reference point used to define the lag.\nWe therefore interpret the diagonal feature as a consequence of evaluating correlations across\ncombinations of window widths and lags when the relationship is dominated by eye-movement\nactivity within a relatively fixed, minute-scale pre-stimulus interval (~3 min). Under this\nscenario, elevated correlations arise for any window/lag pair that substantially overlaps with\nthe same informative interval; as window width increases, the lag that best positions the\nwindow over that interval shifts systematically, producing a diagonal ridge. Consistent with this\naccount, the significance maps show that correlations for saccade frequency and the PCA-\nderived component are most pronounced within bands spanning around 0 s lags with ~150-250\ns windows and extending to ~160 s lags with ~10-80 s windows (Supplementary Fig. 9a, e).\nOscillation frequency exhibits weaker but similar patterns, peaking around 0-20 s lags for 20-\n150 s windows (Supplementary Fig. 9c). Together, these results indicate that neuronal responses\nare enhanced following approximately 3-min periods of increased saccadic activity,\ncorroborating the reported minute-scale coupling effect.\nWe appreciate for noting the formatting issue in Supplementary Fig. 9e (right panel). We have\ncorrected the tick marks in the revised figure. In addition, to make the statistical interpretation\nmore direct, we revised the right panels to display only significance values (−log10(p);\nthreshold p < 0.05). Together, these additional analyses and figure revisions support the\nrobustness of the minute-scale effect.\nFig. R2 | Time-resolved correlations between saccadic metrics and neuronal visual\nresponses across time lag definitions.\na-f, Correlations were recomputed using two additional definitions of the time lag. in (a, c, e),\nthe lag is defined as the interval between the midpoint of the analysis window and visual\nstimulus onset; whereas in (b, d, f) the lag is defined as the interval between the onset of the\nanalysis window and grating motion onset. Results are shown for saccade frequency (a, b),\noscillation frequency (c, d), and the PCA-derived component (PC1) (e, f). In each panel, the\nleft heatmap displays mean correlation coefficients (r) between neuronal visual responses and\nthe corresponding eye-movement metric across neurons, and the right heatmap shows the\nassociated significance values (-log₁₀(p); two-sided Wilcoxon signed-rank test). Heatmaps span\ntime lags from 0 to 6 min (step = 0.5 s) and analysis-window durations from 1 s to 8 min. White\nregions indicate window-lag combinations that cannot be evaluated under the corresponding\ntime-lag definition.\nAuthors: We appreciate the time and effort you dedicated to evaluating our work."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The reviewers have addressed my comments."}, "author_response": ""}]

Decision letter