Selective control of prefrontal neural timescales by parietal cortex

Domain: chemistry
Published: 2026-03-09
Editorial outcome: minor_revision

Abstract
The frontal eye field (FEF) and posterior parietal cortex (PPC) are key hubs of the dorsal attention network, yet how parietal inputs shape prefrontal circuit temporal dynamics and attentional computations remains largely unknown. We measured intrinsic timescales of FEF neurons in male rhesus macaques and examined their changes during PPC inactivation. We observed two distinct classes of FEF neurons based on their intrinsic timescales: short-timescale neurons (~25 ms) and long-timescale neurons (~100 ms). Short-timescale neurons showed stronger transient visual responses, suggesting a role in rapid visual processing. In contrast, long-timescale neurons exhibited stronger sustained salience representation, suggesting a role in spatiotemporal integration to maintain stimulus-driven attention. During PPC inactivation, intrinsic timescales increased in both neuron types, with a substantially larger effect in short-timescale neurons. In addition, PPC inactivation selectively disrupted salience computation, particularly in long-timescale neurons. These findings provide causal evidence linking intrinsic local neural timescales to long-range inter-area communication and indicate the presence of at least two distinct network motifs that support different neuronal dynamics and functional computations within the FEF.

Introduction
Intrinsic neural timescales measure the duration over which spontaneous neural activity remains temporally correlated with its prior states, independent of external stimuli or specific behavioral tasks1–3. These timescales define the temporal window in which previous neural activity can influence current neural dynamics, thereby shaping how information is processed and integrated within neural circuits4. Across cortical regions, intrinsic timescales exhibit systematic variations that reflect functional specialization. For instance, along the visual hierarchy, early visual areas exhibit shorter intrinsic timescales, whereas higher-order areas demonstrate longer timescales2;5. This gradient aligns with functional hierarchies in the brain, where shorter timescales are associated with regions involved in transient information processing, and longer timescales support sustained cognitive functions, such as working memory and decision-making6–9. Recent studies further suggest that intrinsic timescales also vary among individual neurons within a single brain region, corresponding to the functional heterogeneity at the neuronal level1;10;11. Taken together, this diversity may critically enable the brain’s dynamic balance between integrating and segregating information over multiple temporal scales1;3;4;12;13.

In the primate brain, the frontal eye field (FEF) and the posterior parietal cortex (PPC) are key nodes in the frontoparietal attention network, with the PPC located earlier in the visual hierarchy14–16. Both areas are involved in stimulus-driven and goal-directed visual attention17;18. Consistent with this network organization, our recent research demonstrated a causal role of the PPC in regulating stimulus-driven attentional representations in the FEF and attention-driven behavior19. Anatomically, the FEF and oculomotor areas within the PPC, such as the lateral intraparietal area (LIP), exhibit extensive reciprocal connectivity20. These interactions between FEF and PPC are thought to play a crucial role in supporting various cognitive functions, including visual attention21–24. Moreover, the FEF has direct anatomical connections with most visual cortical areas, both receiving input and sending feedback projections across the visual cortex25;26. Therefore, FEF is considered a proxy for prefrontal cortex communication with visual areas through these anatomical connections27. Consequently, intrinsic timescales within FEF likely play an important role in shaping attentional control across prefrontal and visual cortices.

The mechanisms underlying the variability in intrinsic neural timescales are thought to involve both inter- and intra-area interactions28. Previous studies have primarily emphasized the role of local and regional factors within the brain, such as dendritic spine density in pyramidal neurons29–31, the expression levels of NMDA and GABA receptor genes32–34, and the extent of structural and functional connectivity22;35. Complementing these empirical studies, computational modeling work has further shown that differences in intrinsic timescales across cortical regions can emerge naturally from variations in both local excitatory-inhibitory interactions and long-range connectivity patterns5;28;36;37. These findings suggest that intrinsic timescales arise from a combination of diverse excitatory and inhibitory connection strengths within regions and long-range connectivity patterns, indicating that neither factor alone is sufficient to predict a region’s timescales5;36;38. However, despite the modeling work, a direct causal link between inter-area communication and intrinsic neural timescales remains untested. Specifically, it is entirely unknown whether the intrinsic timescales of FEF neurons depend on PPC input.

In this study, we examined the diversity of intrinsic neural timescales among FEF neurons, assessed their relationship to neuronal functional specialization during visual processing and salience representation, and investigated their dependence on PPC inputs. We found a bimodal distribution of intrinsic timescales, identifying distinct FEF neuronal groups with either fast (short-τ) or slow (long-τ) timescales. Moreover, these timescales measured during the baseline period correlated with the neurons’ functional properties during the task: short-τ neurons were more involved in processing transient visual input, while long-τ neurons exhibited stronger sustained salience representation. Finally, PPC inactivation selectively increased the intrinsic timescales of short-τ neurons while having a larger impact on salience modulation of long-τ neurons. Together, these findings provide direct causal evidence that PPC inputs regulate intrinsic dynamics and attentional modulation within FEF, suggesting a distributed computation mechanism underlying the frontoparietal attention network.

Results
We recorded spiking activity from the right FEF of two behaving monkeys (J and Q) using multichannel microelectrodes (Fig. 1A; see Methods). These neuronal responses were measured during the presentation of two types of visual stimuli: single visual probes and popout arrays containing a unique stimulus (Fig. 1B). To assess the impact of PPC input on FEF activity, we reversibly inactivated the PPC using cryoloops chronically implanted within the right intraparietal sulcus (IPS). We then compared FEF spiking responses between PPC inactivation and control conditions. In total, we recorded 400 single- and multi-unit responses from 192 FEF recording sites across 11 experimental sessions.

Two distinct neural timescales in FEF neurons
To characterize the diversity of intrinsic neuronal dynamics within the FEF, we first analyzed neural timescales in two monkeys under the control condition (no PPC inactivation). Intrinsic neural timescales were quantified by calculating the spike-count autocorrelation for each neuron based on spontaneous activity in the baseline epoch before stimulus presentation (Fig. 1C). Autocorrelation functions were fit using a single exponential decay model to estimate intrinsic neural timescales (Fig. 1D)2. R2 values were then used to quantify the goodness of fit between our model and the observed data (Fig. S1A). The exponential function was closely aligned with the autocorrelation profiles of each neuron (mean R2 = 0.78; R2 for Monkey J = 0.73; R2 for Monkey Q = 0.83). Neurons with poor fits (R2 < 0.3, n = 20) were excluded, leaving 380 neurons for analysis. For this final set of neurons, the precision of the fits was high with a median coefficient of variation (CV) of 0.22. Furthermore, a split-half analysis confirmed the stability of the measurements, showing strong positive correlations between the first and second halves of each recording session (Spearman’s ρ = 0.72, p < 2.2 × 10−16; Fig. S2).

The distribution of intrinsic timescales among FEF neurons exhibited clear bimodality (Fig. 2A), confirmed by several statistical tests (Table S1). This bimodality was observed across both monkeys combined (Excess mass test: p < 2.2 × 10−16) and within each monkey individually (Monkey Q: p < 2.2 × 10−16; Monkey J: p = 3 × 10−2)39. Neurons were divided into short-τ and long-τ populations using the global minimum (60.95 ms) from the kernel density estimate of the combined distribution, which was consistent across individual monkeys (Monkey J = 60.51 ms; Monkey Q = 59.33 ms) (Fig. 2B). This division yielded a short-τ group (N = 168) with a mean timescale of 27.34 ± 16.50 ms and a long-τ group (N = 212) with a mean timescale of 102.64 ± 23.31 ms (Fig. 2C–D). Similar mean timescales were observed for both Monkey J and Q (short-τ neurons: Monkey J = 28.17 ms; Monkey Q = 26.71 ms; long-τ neurons: Monkey J = 104.78 ms; Monkey Q = 100.73 ms). In addition, we observed a significant depth-dependent separation between the two neuronal classes. Short-τ neurons were recorded at more superficial sites (N = 151; median depth = 901 µm), whereas long-τ neurons were found deeper (N = 199; median depth = 1351 µm; Wilcoxon rank-sum test, p = 7.68 × 10−5; Fig. 2E). The likelihood of encountering a long-τ neuron increased at deeper recording depths in the FEF (channels 8–16; 1050–2250 µm; Wilcoxon signed-rank test, p = 3.59 × 10−2; Fig. S3). Although absolute recording depth does not directly correspond to cortical layers due to the cortical fold and relative penetration angle, these results reveal a systematic depth-dependent organization of neurons with distinct intrinsic timescales.

This bimodal timescale distribution was evident within both the multi-unit (N = 300; Excess mass test: p < 2.2 × 10−16) and single-unit (N = 80; p = 2 × 10−3) populations (Fig. S4). Consistent with this finding, the distinction between timescale groups was not explained by unit type (Chi-squared test of independence: χ2(1) = 1.38, p = 0.24) or by differences in the precision of the timescale estimates (Wilcoxon rank-sum test: p = 0.56; median CV = 0.21 for short-τ vs. 0.24 for long-τ).

In contrast to the bimodal distribution of timescales, firing rate distributions were consistently unimodal for both monkeys (Excess mass test: Monkey Q, p = 0.43; Monkey J, p = 0.79) (Fig. S5). However, the two groups differed in their intrinsic firing patterns. Short-τ neurons exhibited significantly higher temporal variability (Wilcoxon rank-sum test: p = 4.6 × 10−5; Fig. S6A) but similar trial-to-trial variability (p = 0.31; Fig. S6B) in their firing rate compared to long-τ neurons. This suggests that a short-τ signifies more transient activity characterized by a dynamic baseline firing pattern, whereas a long-τ indicates sustained activity with a stable baseline firing pattern. Together, these results suggest the existence of at least two distinct neural circuit motifs within the FEF characterized by different temporal dynamics.

Functional relevance of FEF intrinsic neural timescales
Next, we asked whether the intrinsic neural timescales measured during the task-free baseline period correlate with the neurons’ functional properties during the task. We used single and popout indices to quantify neuronal visual responses and salience-driven modulation. Specifically, the single index was calculated by comparing the activity evoked by a single stimulus presented inside versus outside the receptive field (RF). This index captures the spatial selectivity of visual responses, reflecting the ability of neurons to distinguish stimuli within their RFs. Similarly, a popout index was calculated by comparing neuronal responses to unique stimuli inside versus outside the RF. This index quantifies the neural representation of stimulus salience, reflecting the ability of neurons to distinguish visually salient stimuli. To further characterize the temporal dynamics of these responses, we divided the neuronal activity into transient (50–200 ms) and sustained (200–500 ms) periods following stimulus onset. This temporal segmentation enabled us to calculate transient and sustained versions of each index, distinguishing short-lived neural responses to stimulus onset from long-term changes in activity associated with sustained stimulus processing (see Methods).

To isolate the relationship between intrinsic timescales and neuronal function, we used multiple linear regression to relate timescales to the single and popout indices from both transient and sustained epochs, while controlling for other predictors (Fig. 3A). For visual responses, we found a significant negative association between timescales and transient single indices (Permutation test: p = 2 × 10−4, β = −73.98). That is, neurons with faster intrinsic timescales showed stronger responses to single visual stimuli within their receptive fields than those with slower timescales. For salience processing, we found a significant negative relationship between timescales and transient popout indices (Permutation test: p = 2.05 × 10−2, β = −53.98), and a significant positive relationship with sustained popout indices (p = 9 × 10−4, β = 77.18). In other words, neurons with slower intrinsic timescales exhibited stronger sustained responses to popout stimuli, while neurons with faster timescales showed greater transient activity. These findings demonstrate a robust link between individual FEF neurons’ intrinsic timescales and their role in visual processing and salience representation.

Motivated by this functional relevance, we next compared visual responses and salience processing between short-τ and long-τ FEF neurons, defined by their intrinsic timescales during the task-free epoch (Fig. 3B). Short-τ neurons exhibited significantly greater transient single indices (Crame´r–von Mises test: p < 3 × 10−3; median = 0.41 for short-τ vs. 0.35 for long-τ) and transient popout indices (p < 1.3 × 10−2; median = 0.12 for short-τ vs. 0.09 for long-τ) compared to long-τ neurons. In contrast, sustained indices were not significantly different for either single (p = 0.16) or popout (p = 0.96) conditions at the individual level (Fig. 3C). Additionally, the visual onsets for the two groups did not significantly differ for either single (Crame´r–von Mises test: p = 0.2) or popout stimuli (p = 0.08) (Fig. S7). These findings align with the regression analysis, demonstrating that neurons with shorter timescales exhibit stronger, but not faster, transient responses to both single and popout stimuli.

Next, we assessed the visual and salience information encoded at the population level by short-τ and long-τ neurons. We performed two complementary pseudo-population decoding analyses. The first was a binary stimulus detectability task, in which we predicted whether a stimulus location was inside or outside the receptive field. The second was a multi-class stimulus discriminability task, where we predicted the specific location of the stimulus out of 24 possibilities. We then generated neuron-dropping curves (NDCs) from neuronal subsets of each group to examine decoding accuracy across varying population sizes (Fig. S9 and Fig. 3D–E). To quantify the performance of each population, we calculated the area under these curves (AUC) that exceeded the chance level.

In the detectability task, we found a dissociation between the two populations (Fig. S9). For single-stimulus detection, short-τ neurons excelled during the transient period (Wilcoxon rank-sum test: p = 2.41 × 10−13, effect size = 0.60; median = 0.989 for short-τ vs. 0.984 for long-τ), whereas long-τ neurons performed better during the sustained period (p = 7.71 × 10−3, effect size = 0.22; median = 0.953 for long-τ vs. 0.947 for short-τ). For popout detection, long-τ neurons consistently showed stronger performance in both transient (p = 3.94 × 10−4, effect size = 0.29; median = 0.64 for long-τ vs. 0.62 for short-τ) and sustained periods (p = 1.88 × 10−7, effect size = 0.43; median = 0.76 for long-τ vs. 0.73 for short-τ).

This pattern of specialization was even more evident in the discriminability task (Fig. 3F–G). The single-stimulus discrimination results mirrored the detectability results: short-τ neurons again had better performance in the transient period (p = 2.56 × 10−34, effect size = 1.00; median = 0.31 for short-τ vs. 0.27 for long-τ), while long-τ neurons were better during the sustained period (p = 4.88 × 10−15, effect size = 0.64; median = 0.18 for long-τ vs. 0.17 for short-τ). This advantage for long-τ neurons was most apparent for popout discrimination, where they demonstrated better performance across both transient (p = 1.44 × 10−16, effect size = 0.68; median = 0.058 for long-τ vs. 0.054 for short-τ) and sustained periods (p = 2.56 × 10−34, effect size = 1.00; median = 0.09 for long-τ vs. 0.06 for short-τ). The differences between the groups were most pronounced for single-stimulus discrimination in the transient period and popout stimulus discrimination in the sustained period.

In summary, intrinsic neural timescales in the FEF are closely linked to visual responses and salience processing at both the individual and population levels, with short-τ neurons excelling in transient visual processing and long-τ neurons playing a greater role in sustained salience representation.

Selective increase of FEF intrinsic neural timescales during PPC inactivation
We next investigated the effects of PPC inactivation on the FEF intrinsic timescales to understand how PPC regulates local neuronal dynamics within the FEF. PPC inactivation was achieved using cryoloops implanted in the IPS, which suppressed neuronal activity by lowering the temperature of the cortical tissue. The effectiveness of PPC inactivation was confirmed by robust behavioral effects in both the free viewing task and the double-target choice task, as reported by Chen et al.19 Similar to the control condition, the exponential function showed a good fit to each neuron’s autocorrelation profiles during inactivation (mean R2 = 0.8; R2 for Monkey J = 0.75; R2 for Monkey Q = 0.84; Fig. S1B). The precision of the fits was also high (median CV = 0.24). Fig. 4A shows six representative neurons, illustrating the changes in their timescales during PPC inactivation. These neurons exhibited an increase in their intrinsic timescales, reflecting slower dynamics and a more stable firing pattern during the baseline period. Consistent with this, we found a significant increase in intrinsic timescales across the entire population of FEF neurons following PPC inactivation (Wilcoxon signed-rank test: p = 5.09 × 10−13, effect size = 0.43, mean = 84.43 ms, median = 94.92 ms for inactivation vs. mean = 69.89 ms, median = 73.14 ms for control) (Fig. 4B). This increase in intrinsic neural timescales was significant for both monkeys (p = 3.02 × 10−2, effect size = 0.19 for Monkey J; p = 4 × 10−15, effect size = 0.63 for Monkey Q) (Fig. S10). Thus, PPC inactivation robustly slowed neural dynamics across FEF neurons.

We further explored the changes in intrinsic timescales separately for the two distinct populations in FEF: short-τ and long-τ neurons. Similar to control conditions, we found that the probability density of FEF timescales exhibited a bimodal distribution during PPC inactivation (Excess mass test: p < 2.2 × 10−16). However, PPC inactivation led to a substantial increase (29.52%) in the number of long-τ neurons (N = 210; N = 272) and a corresponding decrease (38.27%) in the number of short-τ neurons (N = 162; N = 100). This redistribution highlights a shift in the population dynamics towards lower timescales (Fig. 4C).

We then examined the magnitude of timescale changes separately for short-τ and long-τ neurons (Fig. 4D). We found that there was a significant increase in timescales for both short-τ neurons (Wilcoxon signed-rank test: p = 1.02 × 10−13, effect size = 0.67) and long-τ neurons (p = 7.79 × 10−3, effect size = 0.21). However, the changes observed in short-τ neurons were considerably larger than those of long-τ neurons. On average, short-τ neurons exhibited a 15-fold greater increase in timescales compared to long-τ neurons (Wilcoxon rank-sum test: p = 8.64 × 10−9, effect size = 0.35; short-τ neurons: ∆τ mean = 30 ms, ∆τ median = 19 ms; long-τ neurons: ∆τ mean = 2 ms, ∆τ median = 4 ms) (Fig. 4E). This effect was also observed individually in each monkey (Monkey J: p = 3.65 × 10−2, effect size = 0.19; Monkey Q: p = 4.10 × 10−9, effect size = 0.48) (Fig. S10C). Additionally, the changes in intrinsic neural timescales were not correlated with the changes in baseline firing rate (Spearman’s rank correlation: ρ = 0.25, p = 0.06) (Fig. S11A). Although baseline firing rates slightly increased following PPC inactivation (Wilcoxon signed-rank test: p = 5.94 × 10−3, mean ∆ firing rate = 0.93 Hz; Fig. S11B), these changes were similar between short-τ and long-τ neurons (Wilcoxon rank-sum test: p = 0.33) (Fig. S11C). These results indicate a selective dependence of FEF neural dynamics on PPC input.

PPC inactivation selectively weakens the salience representation in FEF neurons
Finally, we assessed the effects of PPC inactivation on the relationship between intrinsic timescales and the functional properties of FEF neurons. We first re-examined the multiple linear regression analysis under PPC inactivation to determine if the input from PPC is necessary for the observed correlations between neural timescales and neuronal function (Fig. 5A). Our results revealed that the link between intrinsic timescales and salience representation is critically dependent on input from the PPC. Specifically, the significant negative association between transient popout indices and timescales observed in the control condition (Permutation test: p = 2.05 × 10−2, β = −53.98) was no longer significant during PPC inactivation (Permutation test: p = 0.63, β = −12.45). Similarly, the significant positive association between sustained popout indices and timescales under control conditions (Permutation test: p = 9 × 10−4, β = 77.18) became nonsignificant during inactivation (Permutation test: p = 0.21, β = 32.12). For visual responses, PPC inactivation weakened but did not eliminate the significant negative correlation between timescales and transient single indices (Permutation test: p = 2 × 10−4, β = −73.98 under control; p = 2.53 × 10−2, β = −44.83 after inactivation). Thus, PPC inactivation exerted a larger impact on the correlation between neural timescales and salience representation compared to visual response.

We next assessed how PPC inactivation influenced visual responses and salience signals in individual short-τ and long-τ neurons by quantifying the change (∆) in their single and popout indices. Consistent with the regression analysis, PPC inactivation exerted a significantly larger effect on popout indices than on single indices during the sustained period (Wilcoxon rank-sum test: p = 2.17 × 10−4, effect size = 0.22; median ∆popout = 0.13 vs. median ∆single = 0.05). When comparing between the neuron groups, this impairment was significantly greater in long-τ neurons for both transient single indices (Crame´r–von Mises test: p = 1.50 × 10−2) and sustained popout indices (Crame´r–von Mises test: p = 1.90 × 10−2), indicating that they are more susceptible to the loss of PPC input (Fig. 5B).

At the population level, we performed pseudo-population decoding analyses to quantify the change in decoding performance for short-τ and long-τ neurons (Fig. S12). For the detectability task, PPC inactivation impaired performance across all conditions (Fig. 5C). For single-stimulus detection, the impairment was significantly larger for long-τ neurons in both the transient (−1.5% vs. −0.7% for short-τ; Wilcoxon rank-sum test: p = 4.77 × 10−8, effect size = 0.45) and sustained periods (−4.3% vs. −0.7% for short-τ; p = 5.16 × 10−21, effect size = 0.77). For popout stimulus detection, transient responses were slightly more impaired in short-τ neurons compared to long-τ neurons (−49.2% vs. −45.8%; p = 2.4 × 10−2, effect size = 0.18). However, during the sustained period, the impairment was substantially greater for long-τ neurons (−49.8% vs. −27.8% for short-τ; p = 4.2 × 10−30, effect size = 0.93) (Fig. 5E).

This disproportionate impact on long-τ neurons was even more pronounced in the discriminability task (Fig. 5D). For single-stimulus discrimination, the performance reduction was significantly greater for long-τ neurons in both the transient (−26.4% vs. −17.7% for short-τ; p = 3.63 × 10−33, effect size = 0.98) and sustained periods (−30.3% vs. −5.8% for short-τ; p = 2.56 × 10−34, effect size = 1). This pattern was amplified for popout stimulus discrimination, where long-τ neurons again showed significantly larger deficits than short-τ neurons during both the transient (−41.2% vs. −32.1%; p = 2.05 × 10−27, effect size = 0.89) and, most notably, the sustained period (−56.3% vs. −19.6%; p = 2.56 × 10−34, effect size = 1) (Fig. 5F).

In summary, PPC inactivation disrupted the correlations between intrinsic neural timescales and salience representation in FEF. At both individual and population levels, removing PPC input predominantly impairs salience computation in long-τ neurons, with the most substantial deficits observed during the sustained period in both detectability and discrimination tasks.

Methods
Subject details
All experimental procedures followed the guidelines set by the National Institutes of Health Guide for the Care and Use of Laboratory Animals, the Society for Neuroscience Guidelines and Policies, and the Stanford University Animal Care and Use Committee. The experiments involved two healthy adult male rhesus macaques (Macaca mulatta) obtained from the California National Primate Research Center (UC Davis): monkey Q (16 kg) and monkey J (17 kg). The number of animals used aligns with typical practices for neurophysiological studies involving non-human primates.

Experimental procedures
The details of the surgical procedures and neurophysiological recordings have been previously reported by Chen et al.19. In summary, two monkeys (Q, 16 kg; J, 17 kg) performed visual sensitivity and salience-driven attention tasks under PPC inactivation and control conditions while we monitored neural activity in the FEF (Fig. 1A). Eye position was monitored with an EyeLink 1000 Plus eye tracker (SR Research). Each trial began with an initial central fixation period (1 × 1 dva) of 500 ms on a gray background (60 cd/m2), with the last 300 ms serving as the baseline period. Following this, the monkeys were presented with either a single visual stimulus or a popout stimulus for 500 ms while continuing to fixate at the center (Fig. 1B). In the single-stimulus trials, a red or green square stimulus (7 × 7 dva) was displayed at one of 24 locations on a 6 × 4 grid (75 × 45 dva). In the popout trials, a square stimulus was placed among an array of stimuli with contrasting colors (e.g., a red square among green stimuli or a green square among red stimuli). After stimulus offset, the monkeys continued to fixate for an additional 500 ms to receive a reward (Fig. 1C). On average, there were 20 trials per location for each stimulus type in both the control and inactivation conditions.

Neurophysiological recording of FEF neurons
The recording locations within the right FEF were identified by eliciting short-latency saccadic eye movements using biphasic current pulses (≤ 50 µA; 250 Hz; 0.25 ms duration), in line with methods from previous studies56. Neural activity was recorded using 16- or 32-channel linear array electrodes (V and S-Probes, Plexon, Inc.), with contacts spaced 150 µm apart. These electrodes were inserted into the cortex via a hydraulic microdrive (Narishige International). Neural signals were referenced to a nearby stainless steel guide tube positioned near the electrode contacts and acquired using an OmniPlex Neural Data Acquisition System (Plexon, Inc.). The signal used for spike detection was filtered with a 4-pole Bessel high-pass filter at 300 Hz and a 2-pole Bessel low-pass filter at 6000 Hz, and sampled at 40 kHz. Initial classification of extracellular waveforms into single neurons or multi-units was performed online via template matching, with offline sorting subsequently verified using Offline Sorter (v3.0; Plexon, Inc.). In total, we recorded spike timestamps from 400 neural units in the FEF across the two monkeys.

Reversible inactivation of PPC
In each monkey, a pair of stainless steel cryoloops was implanted in the intraparietal sulcus (IPS) of the right hemisphere. These cryoloops were custom-designed to fit the contours of the IPS and fill the sulcus. The IPS was cooled by circulating chilled methanol through the loop tubing, with the temperature closely monitored and maintained within 1 ◦C of the target value by adjusting the methanol flow rate. A stable loop temperature of approximately 5 ◦C was reached within 5–10 minutes after the cooling process began, and the brain’s normal temperature was restored within about 2 minutes post-cooling. Temperatures around 5 ◦C in the loop have been shown to effectively suppress neuronal activity in the underlying cortex57. During the experimental sessions, inactivation periods lasted 30 to 60 minutes each. Neurophysiological data were first collected during a control block, followed by an inactivation block across eight sessions. In six additional sessions, data collection followed two consecutive sets of control and inactivation phases (control–inactivation–control–inactivation), allowing the examination of potential block order effects. The success of PPC inactivation on behavior was evaluated in separate sessions using double-target choice tasks and free viewing of natural images19.

Computation of intrinsic neural timescales
We analyzed neural activity from 400 neural units in the FEF across two monkeys. Each unit’s activity was recorded as spike timestamps, which were binned into 10 ms intervals to calculate spike counts for each trial. To normalize the spike counts at each time bin across all trials, we subtracted the mean firing rate of each time bin from the spike count in that bin. This step helped isolate the intrinsic temporal dependencies of the neuron’s firing pattern, independent of its overall firing rate. We then performed autocorrelation analysis on the 300 ms baseline windows of each trial to compute the sum of products of spike count values against themselves at various lags for each neuron. The autocorrelation values for each lag were first averaged across trials for each neuron. Next, these averaged autocorrelation values were scaled by dividing them by the zero-lag value, standardizing the results for each neuron.

We then fitted a single exponential decay function to these averaged autocorrelation values and calculated the decay time constants for each neuron, representing their intrinsic neural timescales (Fig. 1D). Importantly, a subset of neurons displayed significantly low autocorrelation values at short time lags, suggesting the presence of refractory periods or negative adaptation, as reported in previous studies2;6;8. Therefore, the fitting procedure was started at the time lag where the mean autocorrelation reached its maximum value (excluding the zeroth lag where autocorrelation equals one). The initial dip in autocorrelation profiles did not systematically differ between short-τ and long-τ groups; neither the magnitude at the first time lag (p = 0.31) nor at the subsequent peak (p = 0.17) differed significantly (Wilcoxon rank-sum test).

For the curve fitting, we used the Trust Region Reflective algorithm. The model used for the fitting process is given by the equation:
f(x) = A · e−x/τ + B (1)
where x is the time lag, τ represents the intrinsic timescale, A is the amplitude of the decay, and B is included to adjust for baseline shifts, addressing the influence of intrinsic neural timescales that extend beyond the measurement window.

To establish an objective criterion for the goodness of fit, we calculated pseudo-R2 values for nonlinear fitting based on the proportion of the total variance explained by the exponential decay function. We found that 5% of units had R2 values below 0.3, indicating a poor fit. Additionally, 6.5% of units fell within the range of 0.3 to 0.5, representing a moderate fit, and 13% of units had values between 0.5 and 0.7, suggesting a good fit. The majority, 75.5% of units, demonstrated values ranging from 0.7 to 1.0, indicating an excellent model fit (Fig. S1). Neurons with R2 values below 0.3 were excluded from further analysis, resulting in a remaining sample of 380 neural units for the control condition and 372 neural units for the inactivation condition. In addition, the uncertainty of each timescale estimate was quantified by the coefficient of variation (CV; standard error / τ), derived from the variance of the exponential fit. The resulting median CV of 0.22 confirmed the high precision of the timescale estimates.

Finally, we tested the stability of our timescale measurements using a split-half analysis. For each neuron, trials within a recording session were divided into first and second halves, and an intrinsic timescale was calculated for each subset independently using the procedure described above. We then used Spearman’s rank correlation to quantify the consistency of timescales between the two halves (Fig. S2).

Mode testing of intrinsic neural timescales
To investigate the distribution of intrinsic neural timescales in the FEF, we applied several statistical tests using the multimode R package58. Each test was chosen for its ability to highlight different aspects of distribution modality. We used the Hall and York Critical Bandwidth Test59, which refines bandwidth selection in kernel density estimation to improve the detection of subtle multimodal patterns. The Fisher and Marron Crame´r–von Mises Test60 was applied for its sensitivity in capturing subtle features like shoulder modes by assessing how well the empirical distribution function fits a hypothesized unimodal function using the Crame´r–von Mises statistic. Additionally, we used the Excess mass test39, which employs the excess mass criterion for mode detection, offering a robust method particularly suited for multidimensional datasets with high noise levels. All tests provided complementary results (Table S1).

Analysis of laminar position
We analyzed the laminar depth of each recorded neuron to infer whether the short-τ and long-τ classes were located at different cortical depths. This analysis was based on data from 12 recording sessions. In 11 of these sessions, we used 16-channel linear electrode arrays, while one session utilized a 32-channel array. The 16-channel configuration (150 µm spacing) spans 2.25 mm, a depth that corresponds well with the known thickness of the macaque FEF (approximately 2.0–2.5 mm). To ensure our measurement window was consistent and comparable across all 12 sessions, we restricted our analysis to neurons recorded from the first 16 channels, with the uppermost channels consistently positioned near the cortical surface. This resulted in the exclusion of 30 neurons recorded on deeper channels from the single 32-channel session. The depth of each neuron was then compared between the short-τ and long-τ groups using a nonparametric Wilcoxon rank-sum test.

Calculation of single and popout indices
We measured the receptive field of each neuron by analyzing their responses to a single stimulus presented at each of 24 locations. Locations where a neuron’s firing rate exceeded 95% of its maximum response were included within the neuron’s receptive field. In contrast, the four locations in the far-right column of the ipsilateral visual field were considered outside the receptive field. Following this, we computed visual indices for each neuron, reflecting the neural activity during transient (50–200 ms) and sustained (200–500 ms) periods for both single and popout stimulus trials. These indices were calculated based on the average differential spike rate when stimuli were presented within versus outside the neuron’s receptive field (In-RF vs. Out-RF; Fig. 1B), normalized by their sum:
Visual index = (In-RFi,type,period − Out-RFi,type,period) / (In-RFi,type,period + Out-RFi,type,period) (2)
Here, i represents each neuron, “type” refers to single or popout trials, and “period” denotes the transient or sustained epoch. We computed four single and popout indices corresponding to transient and sustained visual responses as well as salience representation. The distributions of single and popout indices in short-τ and long-τ neurons were compared using the Crame´r–von Mises test (Fig. 3c).

Analyzing visual onsets
We quantified the earliest time at which each neuron’s activity rose significantly above baseline (onset latency), separately for single and popout stimulus conditions. Specifically, we computed the mean spike count across trials for each 10 ms bin. A baseline threshold was then defined by adding two standard deviations to the average spike count during the pre-stimulus window. Within the post-stimulus period, we then identified the first occurrence of at least two consecutive bins (20 ms) exceeding this threshold. The leading bin in this sequence was determined as the neuron’s onset bin. By applying this procedure independently to single and popout stimulus trials, we observed that 154 of 168 short-τ neurons (92%) and 173 of 212 long-τ neurons (82%) showed a detectable onset under single-stimulus trials. Additionally, 145 of 168 short-τ neurons (86%) and 142 of 212 long-τ neurons (67%) showed a detectable onset during popout trials. These results provided onset latencies that characterized the timing of visually evoked responses across stimulus and experimental conditions (Fig. S7). Visual inspection of the neuronal activity traces confirmed the accuracy of these onset calculations.

Multiple linear regression analyses
We conducted multiple regression analyses to assess the individual contributions of each variable while accounting for their interdependencies. In our model, the dependent variable was the intrinsic neural timescale, while the independent variables included the single indices, popout indices, and the baseline mean firing rate. The regression model was specified as:
τi = β0 + β1 · Single-transienti + β2 · Single-sustainedi + β3 · Popout-transienti + β4 · Popout-sustainedi + β5 · Firing-ratei + ϵi (3)
In this equation, τi is the intrinsic timescale for neuron i. The predictors are the transient and sustained single (Single-transienti, Single-sustainedi) and popout (Popout-transienti, Popout-sustainedi) indices, along with the baseline mean firing rate (Firing-ratei). The term ϵi represents the residual error for each neuron.

To fit the linear model, we used robust regression, which minimizes the influence of outliers and is less sensitive to non-normality in the data. This approach ensures that the coefficients accurately reflect the effect of each predictor on the dependent variable without being disproportionately influenced by outliers. We utilized added-variable plots to illustrate the relationship between neural timescales and each predictor by plotting the controlled residuals against each independent variable (Fig. 3A). These plots provide a visual representation of the distinct contributions of each predictor to the model, with the slopes quantifying the effect of a one-unit change in each predictor on the timescales while holding the other variables constant.

We checked for multicollinearity among the independent variables in the multiple regression model using the variance inflation factor (VIF) analysis. The VIF quantifies the degree to which the variance of a regression coefficient is inflated due to linear dependence on other predictors in the model. Generally, a VIF value greater than 5 or 10 suggests problematic multicollinearity61, indicating that the affected predictors may be linearly dependent on others. In our analysis, we did not find significant multicollinearity (Fig. S8A).

Additionally, we examined the normality of the residuals in our model. A Q–Q plot was used to visually assess the distribution of residuals from our robust regression model against a theoretical normal distribution (Fig. S8B). Deviations from the line in this plot indicated departures from normality, suggesting that the residuals were not normally distributed. To address this, we used permutation testing to assess the significance of the regression coefficients. We employed a custom function to randomly shuffle the dependent variable (neural timescales) 10,000 times while keeping the independent variables unchanged. This generated a distribution of regression coefficients under the null hypothesis of no association (Fig. 3A). P-values were calculated as the proportion of permuted coefficients whose absolute values equaled or exceeded that of the observed coefficient.

Neuron-dropping decoding analyses
To assess whether short-τ and long-τ neurons differ in visual responses and salience representation at the population level, we conducted a neuron-dropping analysis62–64 (Fig. 3D–E). The general framework was applied to two distinct decoding tasks: a binary detectability task and a multi-class discriminability task. For both, we evaluated decoding during transient and sustained stimulus presentations in single-stimulus and popout trials. The core methodology involved analyzing subpopulations of increasing size from 3 to 120 neurons; subpopulation sizes were incremented by one neuron for the detectability task and by two neurons for the discriminability task. The cap of 120 neurons was set to avoid statistical issues that can arise when the subpopulation size approaches that of the fixed population62. At each step, we randomly sampled the specified number of neurons 500 times to ensure robust performance estimates. For each sample, neural data underwent a principal component analysis (PCA) where the top three components were retained, and these components were then used to train a support vector machine (SVM) classifier.

The two decoding tasks differed in their specific objective and trial balancing. For both tasks, neurons with fewer than 20 trials for any condition were excluded. The detectability task tested the ability to decode spatial location as either inside vs. outside the receptive field. For this task, trial counts were equalized for In-RF and Out-RF conditions. This analysis included 143 neurons in the short-τ group and 190 neurons in the long-τ group. The discriminability task, in contrast, tested the ability to decode the precise location of the stimulus from 24 possible locations. For this multi-class problem, trial counts were equalized across all stimulus locations for each subpopulation. This analysis resulted in a final pool of 141 short-τ and 178 long-τ neurons. For both tasks, decoding performances exhibited saturation curves as the number of neurons increased, indicating diminishing returns in performance improvement with additional neurons.

To assess differences in decoding performance between short-τ and long-τ neurons, we analyzed the area under the curve (AUC) of these saturation curves. First, a statistical baseline was established by computing the 95% confidence interval (CI) of chance performance for each neuron count. This CI was calculated from the 2.5th and 97.5th percentiles of a binomial distribution, with the number of trials at each population size estimated by simulating the neuron and trial sampling procedure 1,000 times. For the detectability task, the chance probability was p = 0.5, whereas for the discriminability task, it was p = 1/24, where 24 is the number of stimulus locations. The AUC was then calculated as the area between the decoding accuracy curve and the upper bound of this chance-level CI, using the trapezoidal rule. To generate a distribution of AUC scores, the 500 resampling iterations were randomly partitioned into 100 non-overlapping groups, and the AUC was computed for the average decoding curve of each group. These raw AUC values were then normalized by dividing each by the maximum possible area above the chance CI (i.e., the area between a perfect 100% accuracy curve and the CI’s upper bound). This yields a normalized score from 0 to 1, where 0 represents performance indistinguishable from chance and 1 represents perfect decoding across all population sizes. The resulting normalized AUC scores were compared between short-τ and long-τ neurons using the nonparametric Wilcoxon rank-sum test.

Testing the effect of parietal inactivation
To investigate the effects of parietal inactivation on intrinsic neural timescales, visual responses, and attentional modulation in the FEF, we conducted a series of statistical analyses. First, we performed paired Wilcoxon signed-rank tests to determine whether parietal inactivation led to significant changes in FEF neural timescales, assessing the direction of change (increase or decrease) across both monkeys and within each individual monkey. The rank biserial correlation effect size was calculated to quantify the magnitude of the observed changes (Fig. 4B). Next, we analyzed whether the change (∆: inactivation − control) in neural timescales differed between short-τ and long-τ neurons. We first tested against zero using a one-sample Wilcoxon signed-rank test to determine whether the change within each group was significant, followed by unpaired Wilcoxon rank-sum tests to compare the magnitude of change between the two groups (Fig. 4E).

We also examined whether the relationships between neural timescales and visual/attentional responses observed in the control condition persisted or were altered during inactivation. A multiple regression analysis was conducted using values obtained after inactivation, with intrinsic neural timescales as the dependent variable and visual indices along with baseline firing rate as independent variables (Fig. 5A). Additionally, changes in the distribution of single and popout indices were compared between short-τ and long-τ neurons using the Crame´r–von Mises test (Fig. 5B). Furthermore, we performed a neuron-dropping analysis62 under PPC inactivation to evaluate the changes in decoding performances at the population level (Fig. 5C–D and Fig. S12). We compared the relative change in the normalized AUC scores between the two groups following PPC inactivation (Fig. 5E–F).

Data availability
The data generated in this study have been deposited in the Figshare database (DOI: 10.6084/m9.figshare.28971866). Source data are provided with this paper.

Code availability
The analysis code used to perform all statistical analyses and generate all figures is publicly available via a Code Ocean capsule (https://codeocean.com/capsule/0814945). The capsule includes the code and the dataset needed to reproduce the analyses and figures (R v4.4.1; R Studio v2024.4.2.764).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Soyuhos et al. have investigated intrinsic timescales in the frontal eye field (FEF), an area that plays a key role in stimulus driven and goal directed visual attention. They identified two distinct neuronal populations within FEF — one with short and another one with long intrinsic timescales — that contribute differentially to transient stimulus processing and attentional functions, respectively. To test whether these timescales are influenced by factors beyond local circuitry, the authors inactivated the posterior parietal cortex (PPC) using cryoloops implanted in the intraparietal sulcus (IPS). This manipulation significantly reduced the number of short-timescale neurons and increased the number of long-timescale neurons in FEF, resulting in an overall shift toward longer intrinsic timescales. Additionally, PPC inactivation selectively disrupted the attentional modulation in long τ neurons. These findings highlight the importance of interareal interactions, demonstrating that intrinsic timescales are not solely shaped by local network interactions but also by input from other brain regions.\nWhile the study provides important insights on the heterogeneity of time intrinsic time scales with FEF, it lacks important large-scale population and laminar analysis. Such data and analysis could potentially offer a more comprehensive understanding of the spatial distribution and population-level dynamics/computations associated with the distinct neuronal classes identified in this study.\nRegarding novelty and significance, previous studies have already reported the heterogeneity in time scales within cortical areas, and those studies are cited. They have now shown that this also holds for FEF, an area important for visual attention. Their second finding, the influence of PPC on intrinsic timescales in FEF, is the real new finding. However, one cannot rule out the effect of other brain areas that might have contributed to the change in time scales with the inactivation of PPC. Hence the changes they report might not be exclusively determined by PPC.\nMajor comments:\n1. Do the two distinct neuron types based on intrinsic timescales form spatially segregated populations, and is there higher/lower functional coupling within/between these populations? Additionally, I am wondering whether there is a laminar dependence of the location of these distinct classes of neurons which could be important given how the input from PPC modulates their dynamics. Given that the authors recorded using laminar probes, in principle it should be possible to extract that information from the data.\n2. In the decoding analysis, the decoder performance is already highly saturated for the classes used (in/out of RF). Wouldn't it be more appropriate to use a set of stimuli try to decode stimulus information from neural responses of these 2 groups of neurons?\n3. The authors should include representative autocorrelograms for the two neuronal classes in Figure 1 as it will enhance the reader’s understanding of intrinsic timescales by providing visual intuition\n4. The authors mention that the FEF receives inputs from multiple brain areas. In this study they inactivated one of its inputs from the PPC. Do the authors have any mechanistic understanding on how inactivation of other brain areas might influence intrinsic timescales, and how these effects might compare in magnitude to the PPC inactivation results?\n5. Is there a possibility that inactivating PPC might affect other brain areas which in turn feed into FEF? As a result, can the findings reported here be the combined effect of indirect perturbation of other areas in addition to PPC? These possibilities are not adequately considered, which weaken the authors' conclusions.", "reviewer_2": "In this study, Syuhos et al. investigate how intrinsic neural timescales vary across neurons in the frontal eye field (FEF), considering both stimulus-driven and attention-modulated responses, and how these timescales are affected by inactivation of the posterior parietal cortex (PPC)—a region known to regulate attentional signals in FEF. Intrinsic timescales were estimated via the autocorrelation decay of spike-count fluctuations during spontaneous activity (i.e., the baseline period preceding stimulus onset).\nThe authors identified two distinct classes of FEF neurons based on their intrinsic timescales: short (<50 ms) and long (50–150 ms). When analyzing the neurons’ responses to visual stimuli, they found that short-timescale neurons were more active during the transient, early phase of the response, whereas long-timescale neurons exhibited stronger attentional modulation during the later, sustained phase. PPC inactivation led to an overall increase in FEF timescales, with a stronger effect observed in short-timescale neurons. Notably, attentional modulation was reduced following PPC inactivation, especially in long-timescale neurons.\nI found this study compelling. It is clearly written, the methods are well described, and the results are appropriately analyzed and interpreted. Nonetheless, I have a few concerns regarding the stability of the intrinsic timescale estimates and the theoretical framing of the findings:\n1. Stability of intrinsic timescales: The robustness of the estimated timescales should be assessed. For instance, the authors could divide each recording session into two halves and compute timescales separately to test their consistency (e.g., via a split-half correlation). This would help ensure that the timescale classification reflects stable intrinsic properties rather than session-specific noise.\n2. Theoretical framework: The study would benefit from a broader theoretical context. Recent work (Ponce-Alvarez et al., 2025, DOI: 10.1523/JNEUROSCI.1699-24.2024) has shown that neural timescales and variances relate systematically to a network’s topology and functional roles. Specifically, neurons at the core of the network exhibit longer timescales, lower variability, and weaker stimulus responses, while peripheral neurons show shorter timescales and greater responsiveness. This reflects a fundamental trade-off between stability and flexibility in network dynamics. Framing the current results within this context could strengthen the interpretation of the timescale classes and their functional relevance.\n3. Additional analyses: Following Ponce-Alvarez et al. (2025), it would be informative to analyze the intrinsic variance and pairwise correlations among neurons (and how they change during PPC inactivation). One might expect short-timescale neurons to exhibit higher variability and weaker correlations, whereas long-timescale neurons may show lower variance and stronger correlations among them. Exploring these dimensions could provide a more comprehensive characterization of the functional organization of the FEF.\nOther comments:\n- It seems that some neurons displayed significantly low autocorrelation values at short time lags, which led the authors to fit exponential functions to the autocorrelation functions (ACFs) for non-zero lags. Are these particular neurons classified as belonging to short- or long-timescale group?\n- The uncertainty of the estimated ACF decay (tau) should be reported. Additionally, it would be informative to examine how this uncertainty varies as a function of the timescale.\n- Figures 5C and 5D are difficult to interpret and could be improved for clarity. In particular, the use of a secondary axis should be avoided, as it is confusing.\n- I could not find the number of trials for each condition.\n- For the decoding, take into account the confidence interval of the chance level (which depends on the number of trials).\nI hope the authors find these comments constructive.", "reviewer_3": "The authors demonstrate that within the same cortical area, the frontal eye field, there are populations of neurons operating at a shorter and a longer time scale; these populations have distinct functional properties, and are differentially affected by inactivation of the posterior parietal cortex.\nExperiments are clearly designed; results are solid and clearly presented.\nI have only minor comments/suggestions.\nSpecies might be indicated in the title or abstract\nFig 1 legend\nor the same stimulus embedded among iden- 657\ntically colored distractors (“Popout”)\n-> distractors of a different uniform color (or other wording? now, \"identically colored\" may refer to either stimulus or distractors).\nFig. 1 C,D - time bin 10 ms?\nFig 6. Legend:\nShort-timescale (short-τ ) neurons in the frontal eye field (FEF; blue) receive direct feedfor- 736\nward inputs from the posterior parietal cortex (PPC; orange) and early visual cortical areas (pink). 737\nin the scheme, there is no direct Visual -> FEF connection; only via PPC.", "reviewer_4": "In this study, the authors present a novel analysis of a dataset originally collected by Chen et al. (Neuron, 2020), in which two monkeys performed visual sensitivity and saliency-driven attention tasks under both PPC-inactivation and control conditions. Neural activity was recorded in the frontal eye fields (FEF), revealing that neurons with shorter intrinsic timescales exhibited stronger transient responses to visual stimuli, suggesting a role in rapid visual processing. In contrast, neurons with longer intrinsic timescales showed more sustained responses, indicating their involvement in maintaining attention over time.\nImportantly, PPC inactivation caused a lengthening of the shorter intrinsic timescales and reduced attentional modulation in neurons with longer timescales, considering both multi-unit and single-unit activity (namely, MUA and SUA respectively). These results lead to the intriguing conclusion that the PPC plays a causal role in shaping the intrinsic neuronal dynamics and functional properties of the FEF.\nThe methodology employed in this study is rigorous, well-suited to the research questions, and clearly described. However, several of the claims made in the manuscript lack sufficient support, as outlined in the points below. These shortcomings somewhat undermine the overall persuasiveness of the work and suggest that the manuscript might be better positioned for publication in a more specialized neuroscience journal.\nMajor points:\n1. PPC inactivation determines the PPC input to FEF neurons.\nThe authors interpret the absence of modulation in intrinsic timescales during PPC inactivation experiments (Figs. 4 and 5) as evidence that such modulation depends on input from the PPC. Since the FEF receives direct projections not only from the PPC but also from other posterior visual areas, including regions in the occipital lobes, the authors note that \"PPC inactivation significantly altered their intrinsic dynamics but had a limited impact on their visual responses, possibly due to compensatory inputs from other visual regions.\" This suggests that inactivating the PPC slows down neuronal dynamics in the FEF, potentially by unmasking slower local dynamics or by enhancing activity within local recurrent circuits.\nHowever, because PPC and FEF are components of a broader network, the authors should clarify that it is an oversimplification to attribute changes solely to PPC input to the FEF. In other words, the causal relationship between PPC and FEF activity is likely indirect and mediated by multiple interconnected pathways. These considerations imply that the circuit mechanism proposed in Figure 6 simplifies the underlying processes occurring during PPC inactivation and does not fully capture the complexity of network interactions involved.\n2. Behavioral effects and functional relevance of intrinsic timescales.\nAt line 210 the authors state that PPC inactivation produced \"robust behavioral effects\" in both the free viewing and double-target choice tasks, citing Chen et al. (2020). However, the free viewing task, as described in Chen et al. (2020), appears to involve only passive observation of visual stimuli (Fig. 1B). If no active behavioral response (e.g., saccades, decisions) was required, what measurable \"behavioral effects\" were observed during inactivation? Clarifying whether these effects relate to eye movements, fixation patterns, or other quantifiable metrics is essential to establish the functional relevance of intrinsic timescales.\nMore specifically, while Fig. 3A demonstrates a correlation between intrinsic timescales and neuronal activity modulations (single/popout indexes), the functional relevance of this relationship remains unclear in the absence of behavioral changes during PPC inactivation. If neuronal changes (e.g., altered single/popout index correlations) occurred without any modulation of the behavioral reports, this weakens the argument that intrinsic timescales are behaviorally meaningful in this context.\nThe manuscript should explicitly acknowledge that the dissociation between neuronal and behavioral changes during inactivation complicates inferences about the purpose or necessity of intrinsic timescales. For example, are these timescales merely epiphenomenal in passive tasks, or do they reflect latent processes not captured by the current behavioral measures? Addressing this ambiguity would prevent overinterpretation of the results.\n3. Why pool MUA and SUA together?\nAt line 99, it is clearly stated that the analyzed dataset consists of \"400 single- and multi-unit responses.\" The authors chose to pool these heterogeneous neuronal signals together without providing any justification. Since MUA reflects the combined spiking of multiple neurons near the electrode tip, its spike-count autocorrelation likely represents an average across contributing cells. Consequently, timescales derived from MUA might tend to be longer than those computed from SUA autocorrelations. This difference may arise because averaging smooths out fast fluctuations in spiking activity.\nIf this is the case, the observed bimodal distribution of timescales might simply reflect the distinct contributions of MUA and SUA, with MUA associated with longer timescales and SUA with shorter ones. The authors should therefore explicitly examine how the timescale distributions differ between MUA and SUA populations to clarify this point.\n4. Are bimodal distributions of intrinsic timescales novel?\nA very recent publication by Zeisler et al. (J Neurosci 2025, DOI: 10.1523/JNEUROSCI.2155-24.2025), which is not cited in the manuscript, provides compelling evidence that bimodal distributions of neuronal timescales are a conserved feature across multiple species and brain regions. Such bimodality seems then to be a general organizational principle rather than a region- or species-specific phenomenon. Although Zeisler et al. do not specifically investigate the FEF, their findings are highly relevant to the current study’s claim with the consequence that the observation of bimodal timescales in FEF, while still valuable, appears less unexpected or novel. The manuscript would benefit from acknowledging this contemporary work and discussing how the present findings in FEF fit within the emerging understanding that bimodal timescale distributions may be a widespread feature of cortical organization.\n5. PPC inactivation affects the two monkeys differently.\nAt line 220, the authors state that \"This increase in intrinsic neural timescales was evident in both monkeys.” However, a close inspection of Figure 4B reveals that most data points (i.e., neurons) lie above the dashed line for Monkey Q, whereas for Monkey J, the majority appear below it, if I am not wrong. To verify this apparent discrepancy, I recommend computing separate histograms of the differences log(τ_inact) – log(τ_control) for each monkey. This analysis would allow assessment of whether the median changes are significantly positive for one animal and negative for the other, thereby clarifying the differential effects of PPC inactivation."}, "author_response": "We thank all reviewers for their thoughtful and constructive comments. Our point-by-point responses to each of the reviewers' comments are below.\nBlack bold: Reviewer comment\nRed regular: Our response\nRed italics: Excerpt from the revised manuscript\nReviewer Comments\nReviewer #1\nIn this manuscript, Soyuhos et al. have investigated intrinsic timescales in the frontal eye field (FEF), an area that plays a key role in stimulus driven and goal directed visual attention. They identified two distinct neuronal populations within FEF — one with short and another one with long intrinsic timescales — that contribute differentially to transient stimulus processing and attentional functions, respectively. To test whether these timescales are influenced by factors beyond local circuitry, the authors inactivated the posterior parietal cortex (PPC) using cryoloops implanted in the intraparietal sulcus (IPS). This manipulation significantly reduced the number of short-timescale neurons and increased the number of long-timescale neurons in FEF, resulting in an overall shift toward longer intrinsic timescales. Additionally, PPC inactivation selectively disrupted the attentional modulation in long τ neurons. These findings highlight the importance of interareal interactions, demonstrating that intrinsic timescales are not solely shaped by local network interactions but also by input from other brain regions.\nWhile the study provides important insights on the heterogeneity of time intrinsic time scales with FEF, it lacks important large-scale population and laminar analysis. Such data and analysis could potentially offer a more comprehensive understanding of the spatial distribution and population-level dynamics/computations associated with the distinct neuronal classes identified in this study.\nRegarding novelty and significance, previous studies have already reported the heterogeneity in time scales within cortical areas, and those studies are cited. They have now shown that this also holds for FEF, an area important for visual attention. Their second finding, the influence of PPC on intrinsic timescales in FEF, is the real new finding. However, one cannot rule out the effect of other brain areas that might have contributed to the change in time scales with the inactivation of PPC. Hence the changes they report might not be exclusively determined by PPC.\nWe thank the reviewer for their helpful guidance and constructive feedback.\n1. Do the two distinct neuron types based on intrinsic timescales form spatially segregated populations, and is there higher/lower functional coupling within/between these populations? Additionally, I am wondering whether there is a laminar dependence of the location of these distinct classes of neurons which could be important given how the input from PPC modulates their dynamics. Given that the authors recorded using laminar probes, in principle it should be possible to extract that information from the data.\nWe thank the reviewer for this suggestion. Although laminar analyses in the FEF are challenging due to the cortical folding, we performed an additional analysis examining the recording depth of each neuron. This analysis revealed a clear spatial segregation between the two neuronal classes. Short-τ neurons were located at more superficial contacts (median depth = 901 µm), whereas long-τ neurons were predominantly found at deeper contacts (median depth = 1351 µm). These results strengthen our claim of two distinct neuronal populations and suggest that they may preferentially be located at different cortical layers. The new findings are now presented in Figure 2E and Supplementary Figure S3, and described in the Results section.\nPage 3; lines 115-122: “... In addition, we observed a significant depth-dependent separation between the two neuronal classes. Short-τ neurons were recorded at more superficial sites (N = 151; median depth = 901 µm), whereas long-τ neurons were found deeper (N = 199; median depth = 1351 µm; Wilcoxon rank-sum test, p < 0.001; Fig. 2E). The likelihood of encountering a long-τ neuron increased with depth (channels 8–16; 1050–2250 µm; Wilcoxon signed-rank test, p = 3.59 × 10⁻²; Fig. S3). Although absolute recording depth does not directly correspond to cortical layers due to the cortical fold and relative penetration angle, these results reveal a systematic depth-dependent organization of neurons with distinct intrinsic timescales.”\nRegarding functional coupling, we performed a preliminary analysis of noise correlations and found that pairs of long-τ neurons showed significantly higher correlations compared to pairs of short-τ neurons or mixed short-long pairs. We have included a figure in this response letter (see Rebuttal Figure 1) to share this finding directly. While these results are interesting and align with the idea of distinct functional ensembles, a full exploration of these network-level interactions is the focus of a follow-up project. Therefore, to keep the manuscript focused, we have not included this analysis in the current revision. We are grateful for the suggestion, which confirms an important direction for our future research.\n2. In the decoding analysis, the decoder performance is already highly saturated for the classes used (in/out of RF). Wouldn't it be more appropriate to use a set of stimuli try to decode stimulus information from neural responses of these 2 groups of neurons?\nWe thank the reviewer for this suggestion. We have now performed an additional, more demanding decoding analysis. In addition to the original binary 'detectability' task (In-RF vs. Out-RF), we implemented a multiclass discriminability analysis to decode the stimulus location across 24 possible positions. The results of this more stringent multiclass analysis corroborate and extend our original findings, further confirming the robustness of the decoding results. These new analyses are now reported in the main text.\nPage 4; lines 172-196: “... Next, we assessed the visual and salience information encoded at the population level by short-τ and long-τ neurons. We performed two complementary pseudo-population decoding analyses. The first was a binary stimulus detectability task, in which we predicted whether a stimulus location was inside or outside the receptive field. The second was a multi-class stimulus discriminability task, where we predicted the specific location of the stimulus out of 24 possibilities. We then generated neuron-dropping curves (NDCs) from neuronal subsets of each group to examine decoding accuracy across varying population sizes (Fig. S9 and Fig. 3D-E). To quantify the performance of each population, we calculated the area under these curves (AUC) that exceeded the chance level.\nIn the detectability task, we found a dissociation between the two populations (Fig. S9). For single-stimulus detection, short-τ neurons excelled during the transient period (Wilcoxon rank-sum test: p = 2.41 x 10⁻¹³, effect size = 0.60; median = 0.989 for short-τ vs. 0.984 for long-τ), whereas long-τ neurons performed better during the sustained period (p = 7.71 x 10⁻³, effect size = 0.22; median = 0.953 for long-τ vs. 0.947 for short-τ). For popout detection, long-τ neurons consistently showed stronger performance in both transient (p = 3.94 x 10⁻⁴, effect size = 0.29; median = 0.64 for long-τ vs. 0.62 for short-τ) and sustained periods (p = 1.88 x 10⁻⁷, effect size = 0.43; median = 0.76 for long-τ vs. 0.73 for short-τ).\nThis pattern of specialization was even more evident in the discriminability task (Fig. 3F-G). The single-stimulus discrimination results mirrored the detectability results: short-τ neurons again had better performance in the transient period (p = 2.56 x 10⁻³⁴, effect size = 1.00; median = 0.31 for short-τ vs. 0.27 for long-τ), while long-τ neurons were better during the sustained period (p = 4.88 x 10⁻¹⁵, effect size = 0.64; median = 0.18 for long-τ vs. 0.17 for short-τ). This advantage for long-τ neurons was most apparent for popout discrimination, where they demonstrated better performance across both transient (p = 1.44 x 10⁻¹⁶, effect size = 0.68; median = 0.058 for long-τ vs. 0.054 for short-τ) and sustained periods (p = 2.56 x 10⁻³⁴, effect size = 1.00; median = 0.09 for long-τ vs. 0.06 for short-τ). The differences between the groups were most pronounced for single-stimulus discrimination in the transient period and popout stimulus discrimination in the sustained period.”\n3. The authors should include representative autocorrelograms for the two neuronal classes in Figure 1 as it will enhance the reader’s understanding of intrinsic timescales by providing visual intuition\nWe thank the reviewer for this helpful suggestion. We agree that this visualization is very useful. We have now added a new panel to Figure 2 (Panel C) showing representative autocorrelograms and their exponential fits for both a short-τ and a long-τ neuron, providing a clear visual intuition for timescale measurement.\n4. The authors mention that the FEF receives inputs from multiple brain areas. In this study they inactivated one of its inputs from the PPC. Do the authors have any mechanistic understanding on how inactivation of other brain areas might influence intrinsic timescales, and how these effects might compare in magnitude to the PPC inactivation results?\nWe thank the reviewer for raising this important point. Beyond the PPC, the FEF receives convergent inputs from multiple extrastriate visual areas, including from the pulvinar (Arcaro et al., 2018). We have expanded the Discussion to address two considerations: First, anatomical studies indicate that the reciprocal projections between PPC (particularly LIP) and FEF are especially strong, comprising a major fraction of its long-range visual inputs (Schall et al., 1995; Stanton et al., 1995). Given this dominant connectivity, PPC inactivation is likely to exert a more substantial influence on FEF dynamics than perturbations of other individual inputs. Second, we propose two complementary mechanisms for the observed slowing of timescales: 1) Because parietal neurons exhibit faster dynamics than prefrontal neurons (Chaudhuri et al., 2015; Murray et al., 2014; Wasmuht et al., 2018), removing this fast PPC input may unmask slower local dynamics within FEF, particularly in short-τ neurons with weaker recurrent connectivity. 2) PPC inactivation may also enhance local recurrent engagement within FEF or broader prefrontal loops, further prolonging integration times (Litwin-Kumar & Doiron, 2012). Based on this framework, we predict that removal of inputs from areas with shorter intrinsic dynamics (e.g., parietal cortex, occipital visual areas) would similarly prolong FEF timescales, whereas inactivating higher-order frontal inputs with longer dynamics (e.g., supplementary eye field) might instead shorten FEF timescales by reducing recurrent drive.\nWe have added this discussion to the revised manuscript (Page 8, lines 368-371). While these predictions remain to be directly tested, they provide a reference for future studies aimed at dissecting how distinct long-range inputs shape the temporal dynamics of prefrontal circuits.\n5. Is there a possibility that inactivating PPC might affect other brain areas which in turn feed into FEF? As a result, can the findings reported here be the combined effect of indirect perturbation of other areas in addition to PPC? These possibilities are not adequately considered, which weaken the authors' conclusions.\nWe thank the reviewer for raising this important point. We completely agree that the PPC and FEF are nodes within a large-scale, interconnected network and that attributing the observed effects solely to a direct, monosynaptic PPC-FEF projection would be an oversimplification. For example, pulvinar may serve as an important cortical-thalamic pathway regulating dynamics in both PPC and FEF (Aussel et al., 2023; Boshra & Kastner, 2022; Fiebelkorn & Kastner, 2019). Our results demonstrate that PPC activity is causally necessary for normal FEF dynamics; its removal perturbs the FEF in a specific manner, regardless of whether the influence is direct or mediated by polysynaptic pathways through other brain regions. To address this point more explicitly, we have revised the Discussion (Page 8, lines 355–361; Page 22, line 708) to acknowledge that indirect effects cannot be ruled out and have framed our conclusions more cautiously in the context of broader network interactions.\nReviewer #2\nIn this study, Syuhos et al. investigate how intrinsic neural timescales vary across neurons in the frontal eye field (FEF), considering both stimulus-driven and attention-modulated responses, and how these timescales are affected by inactivation of the posterior parietal cortex (PPC)—a region known to regulate attentional signals in FEF. Intrinsic timescales were estimated via the autocorrelation decay of spike-count fluctuations during spontaneous activity (i.e., the baseline period preceding stimulus onset).\nThe authors identified two distinct classes of FEF neurons based on their intrinsic timescales: short (<50 ms) and long (50–150 ms). When analyzing the neurons’ responses to visual stimuli, they found that short-timescale neurons were more active during the transient, early phase of the response, whereas long-timescale neurons exhibited stronger attentional modulation during the later, sustained phase. PPC inactivation led to an overall increase in FEF timescales, with a stronger effect observed in short-timescale neurons. Notably, attentional modulation was reduced following PPC inactivation, especially in long-timescale neurons.\nI found this study compelling. It is clearly written, the methods are well described, and the results are appropriately analyzed and interpreted. Nonetheless, I have a few concerns regarding the stability of the intrinsic timescale estimates and the theoretical framing of the findings:\nWe thank the reviewer for their detailed feedback and helpful suggestions.\n1. Stability of intrinsic timescales: The robustness of the estimated timescales should be assessed. For instance, the authors could divide each recording session into two halves and compute timescales separately to test their consistency (e.g., via a split-half correlation). This would help ensure that the timescale classification reflects stable intrinsic properties rather than session-specific noise.\nWe thank the reviewer for this suggestion. As recommended, we have now performed a split-half analysis by calculating intrinsic timescales separately for the first and second halves of each recording session. The analysis revealed a strong, significant positive correlation between the two halves (Spearman’s ρ = 0.72, p < 0.001), confirming that our timescale estimates are robust and reflect stable intrinsic properties of the neurons. This new analysis is presented in the new Supplementary Figure S2 and described in the Results section.\nPage 3; lines 102-105: “... Furthermore, a split-half analysis confirmed the stability of the measurements, showing strong positive correlations both between the two halves (Spearman's ρ = 0.72) and of each half with the full session data (ρ = 0.85 and 0.88, respectively; p < 0.001; Fig. S2).”\n2. Theoretical framework: The study would benefit from a broader theoretical context. Recent work (Ponce-Alvarez et al., 2025, DOI: 10.1523/JNEUROSCI.1699-24.2024) has shown that neural timescales and variances relate systematically to a network’s topology and functional roles. Specifically, neurons at the core of the network exhibit longer timescales, lower variability, and weaker stimulus responses, while peripheral neurons show shorter timescales and greater responsiveness. This reflects a fundamental trade-off between stability and flexibility in network dynamics. Framing the current results within this context could strengthen the interpretation of the timescale classes and their functional relevance.\nWe thank the reviewer for this suggestion and for pointing us to the highly relevant work of Ponce-Alvarez et al. (2025). We agree that this theoretical framework greatly enriches the interpretation of our findings. We have now revised our Discussion to incorporate this \"core-periphery\" context. Specifically, we discuss how our two neuronal classes align with this framework: our short-τ neurons, with their higher variability and transient responses, match the functional properties of a flexible \"periphery,\" while our long-τ neurons, characterized by sustained activity, are consistent with a stable \"core.\" These additions can be found in the revised Discussion section (Page 7, lines 299–304).\n3. Additional analyses: Following Ponce-Alvarez et al. (2025), it would be informative to analyze the intrinsic variance and pairwise correlations among neurons (and how they change during PPC inactivation). One might expect short-timescale neurons to exhibit higher variability and weaker correlations, whereas long-timescale neurons may show lower variance and stronger correlations among them. Exploring these dimensions could provide a more comprehensive characterization of the functional organization of the FEF.\nWe thank the reviewer for these suggestions. We have performed both of the requested analyses, and the results strongly support the reviewer's predictions and the core-periphery framework. First, we analyzed the temporal variability of baseline firing rates and found that short-τ neurons exhibit significantly higher variability than long-τ neurons. This new analysis is presented in Supplementary Figure S6 and described in the Results section.\nPage 3; lines 129-134: “... However, the two groups differed in their intrinsic firing patterns. Short-τ neurons exhibited significantly higher temporal variability (Wilcoxon rank-sum test: p = 4.6 × 10⁻⁵; Fig. S6A) but similar trial-to-trial variability (p = 0.31; Fig. S6B) in their firing rate compared to long-τ neurons. This suggests that a short-τ signifies more transient activity characterized by a dynamic baseline firing pattern, whereas a long-τ indicates sustained activity with a stable baseline firing pattern.”\nAdditionally, we analyzed pairwise noise correlations and confirmed that pairs of long-τ neurons are indeed more strongly correlated. As this second analysis is part of a larger, ongoing project, we have not included it in the manuscript itself but have shared the result in this letter for the reviewer's consideration (see Rebuttal Figure 1). We are grateful for these suggestions, which have strengthened our manuscript and validated a key aspect of our future work.\nOther comments:\n- It seems that some neurons displayed significantly low autocorrelation values at short time lags, which led the authors to fit exponential functions to the autocorrelation functions (ACFs) for non-zero lags. Are these particular neurons classified as belonging to short- or long-timescale group?\nOur analysis confirms that the properties of the initial dip in the ACF do not systematically differentiate the short-τ and long-τ groups. Specifically, we compared both the magnitude and timing of the initial ACF profile. We found no significant difference in the magnitude at the first time lag (p = 0.31) or at the subsequent peak (p = 0.17). We have now added this analysis to the Methods section.\nPage 10; lines 436-438: “... Here, the initial dip in autocorrelation profiles did not systematically differ between short-τ and long-τ groups; neither the magnitude at the first time lag (p = 0.31) nor at the subsequent peak (p = 0.17) differed significantly (Wilcoxon rank-sum test).”\n- The uncertainty of the estimated ACF decay (tau) should be reported. Additionally, it would be informative to examine how this uncertainty varies as a function of the timescale.\nWe now report the uncertainty of our timescale estimates using the coefficient of variation (CV). The median CV was 0.22, indicating high precision. This is now described in the Methods and Results section.\nPage 3; lines 100-105: “... Based on this, neurons with poor fits (R² < 0.3, n = 20) were excluded, leaving 380 neurons for analysis. For this final set of neurons, the precision of the fits was high with a median coefficient of variation (CV) of 0.22.”\nPage 3; lines 124-127: “... Consistent with this finding, the distinction between timescale groups was not explained by unit type (Chi-squared test of independence: χ²(1) = 1.38, p = 0.24) (Fig. S4) or by differences in the precision of the timescale estimates (Wilcoxon rank-sum test: p = 0.56; median CV = 0.21 for short-τ vs. 0.24 for long-τ).”\n- Figures 5C and 5D are difficult to interpret and could be improved for clarity. In particular, the use of a secondary axis should be avoided, as it is confusing.\nWe thank the reviewer for this feedback. To address this, we have redesigned these panels to be more direct and intuitive. The new Figures 5C and 5D now directly plot the relative chance in decoding accuracy as a function of population size, which clearly visualizes the magnitude of the change without needing a secondary axis. For readers interested in the full performance curves for both conditions, we have moved the original plots to the Supplementary Figure S13. This revised presentation makes the results much clearer and easier to interpret.\n- I could not find the number of trials for each condition.\nWe apologize for this omission. We have now added the average number of trials per condition to the Methods section.\nPage 9; lines 392-393: “... On average, there were 20 trials per location for each stimulus type in both the control and inactivation conditions.”\n- For the decoding, take into account the confidence interval of the chance level (which depends on the number of trials).\nWe thank the reviewer for this suggestion. Our original analysis used a fixed chance level as the baseline. We agree that a more rigorous approach is to use a baseline that accounts for the statistical confidence interval of chance-level performance, which varies with the number of test trials available at each population size. Following the reviewer’s advice, we have updated our analysis pipeline. For each step in the neuron-dropping curve (i.e., for each number of neurons), we now compute the 95% confidence interval around the chance level using a binomial distribution. The number of trials used for this calculation is determined by simulating our neuron and trial sampling procedure, which accurately reflects the number of test trials available to the decoder at that specific population size. This results in a baseline that correctly captures the statistical uncertainty across the analysis. Our primary metric, the Area Under the Curve (AUC), is now calculated as the area between the decoding accuracy curve and the upper bound of this CI. We have re-run our analysis with this improved method and confirmed that our initial findings remain the same. We have updated the Methods section to reflect this improved analysis.\nPage 13; lines 562-574: “... First, a statistical baseline was established by computing the 95% confidence interval (CI) of chance performance for each neuron count. This CI was calculated from the 2.5th and 97.5th percentiles of a binomial distribution, with the number of trials at each population size estimated by simulating the neuron and trial sampling procedure 1000 times. For the detectability task, the chance probability was p = 0.5, whereas for the discriminability task, it was p = 1/24, where 24 is the number of stimulus locations. The AUC was then calculated as the area between the decoding accuracy curve and the upper bound of this chance-level CI, using the trapezoidal rule. To generate a distribution of AUC scores, the 500 resampling iterations were randomly partitioned into 100 non-overlapping groups, and the AUC was computed for the average decoding curve of each group. These raw AUC values were then normalized by dividing each by the maximum possible area above the chance CI (i.e., the area between a perfect 100% accuracy curve and the CI’s upper bound). This yields a normalized score from 0 to 1, where 0 represents performance indistinguishable from chance and 1 represents perfect decoding across all population sizes.”\nI hope the authors find these comments constructive.\nReviewer #2 (Remarks on code availability):\nThe legibility of the provided code could be improved, although it appears to contain all the necessary information to reproduce the results. A README file is included with sufficient instructions for installing and running the application. I did not attempt to install or run the code.\nWe thank the reviewer for their feedback on the code. We have now substantially modularized the code to improve its organization. The various functions used in the main script have been grouped into separate folders corresponding to their respective analyses. Furthermore, each function is now organized into a stand-alone script that includes a detailed docstring explaining its purpose and input variables. The main README file has also been updated to reflect this improved folder structure and provide a clearer guide for navigating the code.\nReviewer #3\nThe authors demonstrate that within the same cortical area, the frontal eye field, there are populations of neurons operating at a shorter and a longer time scale; these populations have distinct functional properties, and are differentially affected by inactivation of the posterior parietal cortex.\nExperiments are clearly designed; results are solid and clearly presented.\nWe thank the reviewer for their kind words.\nI have only minor comments/suggestions.\nSpecies might be indicated in the title or abstract\nWe thank the reviewer for their suggestion. We have now specified the species in the Abstract.\nPage 1; line 13: “... We measured intrinsic timescales of frontal eye field (FEF) neurons in rhesus macaques and examined their changes during posterior parietal cortex (PPC) inactivation.”\nFig 1 legend\nor the same stimulus embedded among identically colored distractors (“Popout”)\n-> distractors of a different uniform color (or other wording? now, \"identically colored\" may refer to either stimulus or distractors).\nWe thank the reviewer for pointing out this ambiguity. We have revised the legend for Figure 1B.\nPage 15; lines 633-635: “... (B) Visual stimulus conditions included either a single colored stimulus presented alone (\"Single\") or the same stimulus embedded among distractors of a different, uniform color (\"Popout\").”\nFig. 1 C,D - time bin 10 ms?\nWe have now specified the time bin (10 ms) in the legends for Figure 1C and Figure 1D.\nPage 15; lines 637 and 639: “... (C) Example neuronal response illustrating mean firing rate to a single stimulus presented either inside (blue trace) or outside (gray trace) the neuron’s receptive field (10 ms bins). (D) Intrinsic neural timescales were measured using spike-count autocorrelation computed from neuronal activity during the baseline period. Blue dots indicate autocorrelation coefficients at successive 10 ms time lags.”\nFig 6. Legend:\nShort-timescale (short-τ ) neurons in the frontal eye field (FEF; blue) receive direct feedforward inputs from the posterior parietal cortex (PPC; orange) and early visual cortical areas (pink).\nin the scheme, there is no direct Visual -> FEF connection; only via PPC.\nWe thank the reviewer for this suggestion. We have revised the schematic in Figure 6A and its legend to clearly illustrate and describe the direct feedforward connection from early visual areas to the FEF (Page 22).\nReviewer #3 (Remarks on code availability):\ncode will be made publicly available after acceptance of the ms\nReviewer #4\nIn this study, the authors present a novel analysis of a dataset originally collected by Chen et al. (Neuron, 2020), in which two monkeys performed visual sensitivity and saliency-driven attention tasks under both PPC-inactivation and control conditions. Neural activity was recorded in the frontal eye fields (FEF), revealing that neurons with shorter intrinsic timescales exhibited stronger transient responses to visual stimuli, suggesting a role in rapid visual processing. In contrast, neurons with longer intrinsic timescales showed more sustained responses, indicating their involvement in maintaining attention over time.\nImportantly, PPC inactivation caused a lengthening of the shorter intrinsic timescales and reduced attentional modulation in neurons with longer timescales, considering both multi-unit and single-unit activity (namely, MUA and SUA respectively). These results lead to the intriguing conclusion that the PPC plays a causal role in shaping the intrinsic neuronal dynamics and functional properties of the FEF.\nThe methodology employed in this study is rigorous, well-suited to the research questions, and clearly described. However, several of the claims made in the manuscript lack sufficient support, as outlined in the points below. These shortcomings somewhat undermine the overall persuasiveness of the work and suggest that the manuscript might be better positioned for publication in a more specialized neuroscience journal.\nWe thank the reviewer for their valuable feedback on our manuscript.\nMajor points:\n1. PPC inactivation determines the PPC input to FEF neurons.\nThe authors interpret the absence of modulation in intrinsic timescales during PPC inactivation experiments (Figs. 4 and 5) as evidence that such modulation depends on input from the PPC. Since the FEF receives direct projections not only from the PPC but also from other posterior visual areas, including regions in the occipital lobes, the authors note that \"PPC inactivation significantly altered their intrinsic dynamics but had a limited impact on their visual responses, possibly due to compensatory inputs from other visual regions.\" This suggests that inactivating the PPC slows down neuronal dynamics in the FEF, potentially by unmasking slower local dynamics or by enhancing activity within local recurrent circuits.\nHowever, because PPC and FEF are components of a broader network, the authors should clarify that it is an oversimplification to attribute changes solely to PPC input to the FEF. In other words, the causal relationship between PPC and FEF activity is likely indirect and mediated by multiple interconnected pathways. These considerations imply that the circuit mechanism proposed in Figure 6 simplifies the underlying processes occurring during PPC inactivation and does not fully capture the complexity of network interactions involved.\nWe thank the reviewer for raising this point. We completely agree that the PPC and FEF are nodes within a large-scale network and that attributing our results solely to a direct, monosynaptic projection would be an oversimplification. Our goal was to establish a causal link showing that PPC activity is necessary for normal FEF dynamics, regardless of whether the influence is direct or mediated by polysynaptic pathways. To address this point explicitly, we have revised the Discussion to acknowledge that indirect effects cannot be ruled out and have framed our conclusions more cautiously (Page 8, lines 355-361). To further ensure clarity for the reader, we have also amended the text to emphasize that our schematic in Figure 6 is intended as a functional model of the proposed interactions, rather than a complete anatomical circuit diagram (Page 7, line 322; Page 22, lines 705 and 714-716).\n2. Behavioral effects and functional relevance of intrinsic timescales.\nAt line 210 the authors state that PPC inactivation produced \"robust behavioral effects\" in both the free viewing and double-target choice tasks, citing Chen et al. (2020). However, the free viewing task, as described in Chen et al. (2020), appears to involve only passive observation of visual stimuli (Fig. 1B). If no active behavioral response (e.g., saccades, decisions) was required, what measurable \"behavioral effects\" were observed during inactivation? Clarifying whether these effects relate to eye movements, fixation patterns, or other quantifiable metrics is essential to establish the functional relevance of intrinsic timescales.\nMore specifically, while Fig. 3A demonstrates a correlation between intrinsic timescales and neuronal activity modulations (single/popout indexes), the functional relevance of this relationship remains unclear in the absence of behavioral changes during PPC inactivation. If neuronal changes (e.g., altered single/popout index correlations) occurred without any modulation of the behavioral reports, this weakens the argument that intrinsic timescales are behaviorally meaningful in this context.\nThe manuscript should explicitly acknowledge that the dissociation between neuronal and behavioral changes during inactivation complicates inferences about the purpose or necessity of intrinsic timescales. For example, are these timescales merely epiphenomenal in passive tasks, or do they reflect latent processes not captured by the current behavioral measures? Addressing this ambiguity would prevent overinterpretation of the results.\nWe thank the reviewer for this critical and detailed comment. It gives us the opportunity to clarify the behavioral findings from our prior work, the rationale for our current task design, and how we have now strengthened the discussion of our findings in the manuscript.\nFirst, we would like to clarify a key point about the tasks cited from Chen et al. (2020). While the task in our current manuscript is passive, the \"free viewing\" task described in Chen et al. was, in fact, an active behavioral task where the same monkeys in the current study made saccades to freely explore hundreds of natural images. The \"robust behavioral effect\" we refer to was a significant and quantifiable impairment: during PPC inactivation, the monkeys' eye movements were no longer properly guided by the visual salience of the image in both the contralateral field and contralaterally directed saccades. This establishes a direct, causal link between PPC activity and the motor behavior of saccadic guidance to salient stimuli. In addition to the free-viewing paradigm, we also measured the change in target selection in the onset synchrony task.\nThe reason we then used a passive design in the current study was to isolate the underlying neural mechanism. Our goal was to characterize the representation of stimulus salience in the FEF. However, an active saccade task would have introduced confounding motor-planning signals and movement-related modulation, which are known to heavily modulate FEF activity (Bruce & Goldberg, 1985; Hanes & Schall, 1996). By using a passive design, we could show that PPC inactivation specifically disrupts these salience signals in FEF. Importantly, in the present work, the neurophysiology was recorded in the same animals and within overlapping experimental sessions that included the behavioral free-viewing paradigm and the onset synchrony task. These observed neural changes suggest a mechanistic explanation for the behavioral impairments observed in the active tasks.\nFinally, we agree with the reviewer that the manuscript should explicitly acknowledge the nuances of interpreting these neural changes. Following their suggestion, we have revised the final paragraphs of the Discussion (Page 8; lines 341-353).\n3. Why pool MUA and SUA together?\nAt line 99, it is clearly stated that the analyzed dataset consists of \"400 single- and multi-unit responses.\" The authors chose to pool these heterogeneous neuronal signals together without providing any justification. Since MUA reflects the combined spiking of multiple neurons near the electrode tip, its spike-count autocorrelation likely represents an average across contributing cells. Consequently, timescales derived from MUA might tend to be longer than those computed from SUA autocorrelations. This difference may arise because averaging smooths out fast fluctuations in spiking activity.\nIf this is the case, the observed bimodal distribution of timescales might simply reflect the distinct contributions of MUA and SUA, with MUA associated with longer timescales and SUA with shorter ones. The authors should therefore explicitly examine how the timescale distributions differ between MUA and SUA populations to clarify this point.\nWe thank the reviewer for raising this important point. We have now analyzed the timescale distributions for single-unit (SUA) and multi-unit (MUA) populations independently. Our results show that the bimodal distribution of intrinsic timescales is present in both populations. Specifically, the distribution was significantly bimodal for both SUA (Excess mass test: p = 2 × 10⁻³) and MUA (p < 2.2 × 10⁻¹⁶) recordings. Furthermore, a chi-squared test of independence confirmed that there is no significant association between unit type and timescale class (χ²(1) = 1.38, p=0.24). We have added a new supplementary figure (Fig. S4) to visualize these separate distributions and have updated the Results section accordingly.\nPage 3; lines 123-127: “... This bimodal timescale distribution was evident within both the multi-unit (N = 300; Excess mass test: p < 2.2 × 10⁻¹⁶) and single-unit (N = 80; p = 2 × 10⁻³) populations. Consistent with this finding, the distinction between timescale groups was not explained by unit type (Chi-squared test of independence: χ²(1) = 1.38, p = 0.24) (Fig. S4) ...”\n4. Are bimodal distributions of intrinsic timescales novel?\nA very recent publication by Zeisler et al. (J Neurosci 2025, DOI: 10.1523/JNEUROSCI.2155-24.2025), which is not cited in the manuscript, provides compelling evidence that bimodal distributions of neuronal timescales are a conserved feature across multiple species and brain regions. Such bimodality seems then to be a general organizational principle rather than a region- or species-specific phenomenon. Although Zeisler et al. do not specifically investigate the FEF, their findings are highly relevant to the current study’s claim with the consequence that the observation of bimodal timescales in FEF, while still valuable, appears less unexpected or novel. The manuscript would benefit from acknowledging this contemporary work and discussing how the present findings in FEF fit within the emerging understanding that bimodal timescale distributions may be a widespread feature of cortical organization.\nWe thank the reviewer for bringing this very recent study to our attention. We have now cited this important work and revised our Discussion to place our findings within this broader context. We agree that our work contributes to the emerging view that timescale heterogeneity is a conserved organizational principle. Our study, however, provides three critical and novel advances that build upon this foundation. We first establish this principle in the FEF, a key cortical hub for attention not previously studied in this context. Furthermore, our primary contribution is providing a functional dissociation between these populations, linking short-timescale neurons to rapid visual processing and long-timescale neurons to salience representation. Finally, we offer the first causal evidence that these intrinsic timescales are not fixed but are actively modulated by long-range cortical inputs from the PPC. We therefore believe our study provides a mechanistic understanding and causal extension to the general principle they describe. This view is supported by Zeisler et al. (2025) themselves, who cite our preprint when discussing the need for such functional characterization.\nPage 7; lines 294-299: “... This finding aligns with recent reports of multimodal timescale distributions across the cortex in rodents, macaques, and humans (Zeisler et al., 2025; Shi et al., 2025). Here, we provide the first evidence for this dual-timescale organization in the FEF, a critical prefrontal oculomotor area in the frontoparietal attention network (Boshra & Kastner, 2022; Xia et al., 2024), and show that these two classes support a functional division of labor.”\n5. PPC inactivation affects the two monkeys differently.\nAt line 220, the authors state that \"This increase in intrinsic neural timescales was evident in both monkeys.” However, a close inspection of Figure 4B reveals that most data points (i.e., neurons) lie above the dashed line for Monkey Q, whereas for Monkey J, the majority appear below it, if I am not wrong. To verify this apparent discrepancy, I recommend computing separate histograms of the differences log(τ_inact) – log(τ_control) for each monkey. This analysis would allow assessment of whether the median changes are significantly positive for one animal and negative for the other, thereby clarifying the differential effects of PPC inactivation.\nWe thank the reviewer for their comment. While our original submission included statistics showing a significant effect in each animal and differences in their effect sizes, we agree with the reviewer that a visual representation for each monkey makes the results clearer. We have now created a new figure (Supplementary Figure S10) that shows the timescale changes for each monkey individually. This new visualization, in conjunction with the statistics, confirms that while the magnitude of the effect differed between animals, the effect was consistent in direction and statistically significant in both. PPC inactivation led to a significant increase in intrinsic timescales for both Monkey J (p = 3.02 × 10⁻², effect size = 0.19) and Monkey Q (p = 4 × 10⁻¹⁵, effect size = 0.63).\nRebuttal Figures\nRebuttal Fig. 1 | Noise correlations are strongest between pairs of long-timescale neurons. Boxplots compare the distribution of spike count noise correlations during the baseline period for three types of neuron pairs: short-timescale neurons (short-short), long-timescale neurons (long-long), and mixed pairs (long-short). Each box indicates the median (center line) and the interquartile range (25th–75th percentiles), with whiskers representing the data range and circles indicating outliers. A Kruskal-Wallis test showed a significant difference among the groups, and pairwise comparisons using Dunn's post-hoc test with Bonferroni correction revealed that long-long pairs had significantly higher noise correlations than both short-short and long-short pairs. (***, p < 0.001).\nArcaro, M. J., Pinsk, M. A., Chen, J., & Kastner, S. (2018). Organizing principles of pulvino-cortical functional coupling in humans. Nature Communications, 9(1), 5382. https://doi.org/10.1038/s41467-018-07725-6\nAussel, A., Fiebelkorn, I. C., Kastner, S., Kopell, N. J., & Pittman-Polletta, B. R. (2023). Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention. eLife, 12, e67684. https://doi.org/10.7554/eLife.67684\nBoshra, R., & Kastner, S. (2022). 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Consistent Hierarchies of Single-Neuron Timescales in Mice, Macaques, and Humans. Journal of Neuroscience, 45(19). https://doi.org/10.1523/JNEUROSCI.2155-24.2025\nBlack bold: Reviewer comment\nRed regular: Our response\nReviewer Comments\nReviewer #1:\nI have no further comments. The authors have satisfactorily addressed my previous concerns.\nReviewer #2:\nThe authors have satisfactorily addressed all my concerns, and I endorse the paper for publication. A minor note: panel B in Figure S2 seems unnecessary, as correlating half of the data with the full dataset will inherently produce a strong correlation.\nWe agree with the reviewer and have removed panel S2B from the Supplementary Information. Figure S2 now includes only the split-half correlation (first half vs. second half). We have updated the corresponding Supplementary Figure legend and Methods section accordingly.\nReviewer #3:\nAll concerns from my previous review are adequately addressed; I have no further comments, and can recommend the manuscript for publication.\nReviewer #4:\nThe revised version of the manuscript has been significantly improved, and the authors have convincingly addressed all my concerns.\nMore specifically:\n- Regarding Point 1: The authors did not change the Fig. 6 but better refined the caption emphasizing in the Discussion that it serves only as a schematic illustration of how a broad fronto-parietal network can reciprocally modulate the timescales expressed by FEF neurons. This is a sufficient effort as I mainly asked to clarify that the inactivation of PPC can have widespread effects and only indirectly influence FEF. This point is now clearly stated in the Discussion.\n- Regarding Point 2: The added text in the Discussion now clarifies the extent to which the relationship between neural activity and behavioral modulation is addressed in this work, thereby solving the issue of overinterpretation of the presented results.\n- Regarding Point 3: The authors added a new supplementary figure that convincingly addresses my concern. A minor point to be considered at this stage is to provide the parameters used for the kernel density estimates in the caption of Fig. S4 – for SUA (left) and MUA (right) – as they are likely different.\nWe updated the Fig. S4 legend to report the kernel density bandwidth parameters. The legend now specifies that KDEs used R’s default bandwidth and reports the resulting bandwidth values (h = 16.00 ms for SUA; h = 12.29 ms for MUA).\n- Regarding Point 4: The authors have included the requested citation and added a clarifying text in the Discussion fully addressing my criticism.\n- Regarding Point 5: The Supplementary Fig. S10 convincingly demonstrates that, despite my concerns, PPC inactivation increases intrinsic neural timescales in both monkeys.\n2"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have no further comments. The authors have satisfactorily addressed my previous concerns.", "reviewer_2": "The authors have satisfactorily addressed all my concerns, and I endorse the paper for publication. A minor note: panel B in Figure S2 seems unnecessary, as correlating half of the data with the full dataset will inherently produce a strong correlation.", "reviewer_3": "All concerns from my previous review are adequately addressed; I have no further comments, and can recommend the manuscript for publication.", "reviewer_4": "The revised version of the manuscript has been significantly improved, and the authors have convincingly addressed all my concerns.\nMore specifically:\n- Regarding Point 1: The authors did not change the Fig. 6 but better refined the caption emphasizing in the Discussion that it serves only as a schematic illustration of how a broad fronto-parietal network can reciprocally modulate the timescales expressed by FEF neurons. This is a sufficient effort as I mainly asked to clarify that the inactivation of PPC can have widespread effects and only indirectly influence FEF. This point is now clearly stated in the Discussion.\n- Regarding Point 2: The added text in the Discussion now clarifies the extent to which the relationship between neural activity and behavioral modulation is addressed in this work, thereby solving the issue of overinterpretation of the presented results.\n- Regarding Point 3: The authors added a new supplementary figure that convincingly addresses my concern. A minor point to be considered at this stage is to provide the parameters used for the kernel density estimates in the caption of Fig. S4 – for SUA (left) and MUA (right) – as they are likely different.\n- Regarding Point 4: The authors have included the requested citation and added a clarifying text in the Discussion fully addressing my criticism.\n- Regarding Point 5: The Supplementary Fig. S10 convincingly demonstrates that, despite my concerns, PPC inactivation increases intrinsic neural timescales in both monkeys."}, "author_response": "We agree with the reviewer and have removed panel S2B from the Supplementary Information. Figure S2 now includes only the split-half correlation (first half vs. second half). We have updated the corresponding Supplementary Figure legend and Methods section accordingly.\nWe updated the Fig. S4 legend to report the kernel density bandwidth parameters. The legend now specifies that KDEs used R’s default bandwidth and reports the resulting bandwidth values (h = 16.00 ms for SUA; h = 12.29 ms for MUA)."}]

Decision letter