A heterogeneous population code at the first synapse of vision

Domain: neuroscience
Published: 2026-01-30
Editorial outcome: minor_revision

Abstract
Vision begins when photoreceptors convert fluctuations in light intensity into temporal patterns of glutamate release that drive the retinal network. The input-output relation at this first stage has not been studied in vivo so it is not known how it operates across a photoreceptor population. Using glutamate imaging in zebrafish, we find that individual type 1 cones (PR1; ancestral red cones), which dominate daylight vision in non-avian vertebrates, encode visual stimuli with high reliability and time-precision but routinely vary in sensitivity to luminance, contrast and frequency across the population. Variations in input-output relations are generated by feedback from the horizontal cell network that effectively decorrelate feature representation. A model capturing how zebrafish sample their visual environment indicates that heterogenous cone outputs expand the dynamic range of the retina to improve the coding of natural scenes. Moreover, we find that different kinetic release components are used to encode distinct stimulus features in parallel: sustained release linearly encodes low amplitude light and dark contrasts, but transient release encodes large amplitude dark contrasts. This study reveals an unexpected degree of functional heterogeneity within a population of cones and illustrates how separation of different visual features begins in the first synapse in vision.

Introduction
The light-encoding properties of photoreceptors underpin the performance limits of vision. While we have a detailed understanding of the processes that convert light into a photocurrent, we have much less understanding of the output signal by which photoreceptors drive the retinal circuit-the synaptic release of glutamate. Here we use larval zebrafish to make an in vivo investigation of the way in which cone photoreceptors encode visual stimuli.

Spatial correlations in natural visual scenes cause most stimuli to be encoded by the simultaneous activity of several cones. It is therefore important to understand how the input-output relation varies across a population of same-type photoreceptors. A similar relation across the population would indicate a high degree of redundancy in the signal transmitted at the first stage of vision but it is unclear if this is the case because individual cones can be re-tuned by cone-intrinsic factors, their surrounding circuitry and/or the local neuromodulator environment. We have investigated how a population of cones encodes visual stimuli by monitoring glutamate release from synaptic pedicles in vivo. We focus on type 1 cones (PR1, previously: ancestral red cones), which likely represent the ancestral general-purpose greyscale system of the vertebrate eye. More than 90% of cones in the human eye, comprising both red/L and green/M variants, are of this highly conserved photoreceptor type.

We find that while individual PR1 generates synaptic outputs that are exceptionally reliable and precise in time, the population as a whole is heterogenous in terms of sensitivity to luminance and contrast. For example, while some cones exhibited approximately linear contrast-response functions, others were strongly rectifying, signalling negative contrasts more strongly than positive. Similarly, while glutamate release rates from some cones reliably followed temporal contrast up to 20Hz, other cones ceased to respond above 8Hz. Blocking inhibitory feedback from horizontal cells causes all red cones to transmit signals at a lower frequency and become strongly biased to negative contrasts, demonstrating that the outer retinal circuitry plays a key role in determining the input-output relation of the first neurons in vision. A model of bipolar cells summing inputs from cones, as zebrafish sample their visual environment, indicates that heterogeneity in cone signals expands the retina's dynamic range, improving the coding of natural scenes.

Results
Isolating the synaptic output of individual PR1
Cones drive the retinal circuit through synaptic pedicles that are invaginated by post-synaptic bipolar cells and horizontal cells to create extracellular compartments tightly sealed against the surrounding circuit (Fig. 1A, B). In larval zebrafish, there is only one such invagination per cone, and we exploited this anatomical arrangement to use the dendritic tips of horizontal cells expressing the fluorescent glutamate sensor SFiGluSnFR as glutamate antennae. A spatially isolated SFiGluSnFR hotspot therefore provided a glutamate read-out from an individual cone pedicle, each typically comprising some 2-3 ribbons with a total surface area of 0.2–0.5 µm2 (Supplemental Figure S1). Applying line scans at 1 kHz typically allowed us to sample the outputs of 2–5 cone pedicles simultaneously (Supplemental Video 1).

To isolate responses of PR1, we used wide-field stimuli of amber light (~590 nm) because in larval zebrafish, only PR1 exhibit excitatory responses to light decrements at this wavelength (Fig. 1C). PR2 (ancestral green cones) are also sensitive to amber light, but they are red-opponent and therefore exhibit inhibitory responses to the same light decrements. The reliability of this strategy for identifying PR1 was confirmed by co-labelling PR1 with tdTomato (Supplemental Fig. S2).

Examples of synaptic responses are shown in Fig. 1D–H. At the start of this experiment, a light flash was applied, bright enough to reduce the fluorescence signal in the pedicle labelled PR1 to close to background fluorescence levels while also suppressing the noise caused by vesicle fusion in the dark, indicating complete, or almost complete, suppression of glutamate release (Fig. 1F). From this bright background, off-steps were applied by turning off the light for 40 ms. This pedicle was identified as belonging to a PR1 because it exhibited large amplitude glutamate transients in responses to the off-steps (Fig. 1G, H). In contrast, the neighbouring pedicle labelled PR2 exhibited transient suppressions of glutamate release, indicating that it belonged to a PR2. Beyond these two basic response types, other pedicles did not generate reliable SFiGluSnFR signals, indicating that they belonged PR3 or PR4 (ancestral blue/UV, respectively; not shown).

A notable feature of the glutamate transients generated by PR1 pedicles was that when the amplitude varied, the shape did not (Fig. 1K). This property allowed us to assess relative changes in the rate of glutamate release by deconvolving the SFiGluSnFR signal using the temporal kernel derived from the average response to brief off-steps (Fig. 1I, J). A similar approach has been used to detect individual vesicle release events at the synapse of retinal bipolar cells. In cones, however, it was not possible to reliably disambiguate the signals from single vesicles. Instead, we interpret our cone data as reflecting the summed signals from multiple vesicles released from multiple ribbons. Below, we use this technique to investigate how cone synapses encode light intensity and contrast.

Variablesensitivitytolightatthesynapticoutputofindividualcones
The usual approach to investigating the light sensitivity of photoreceptors, for instance, when measuring photocurrents, would be to apply brief flashes of fixed duration but different intensities on top of a dark background. We did not use this approach because the dark signal at the cone synapse is noisy (e.g., Fig. 1F, I) and the response to light is a decrease in the SFiGluSnFR signal caused by an interruption in the continuous release of vesicles. Measuring a decreasing signal from a high and noisy baseline degrades the signal-to-noise ratio compared to measuring an increasing signal from a low and stable baseline. We therefore carried out these measurements using off-steps, where the stimulus strength was the number of photons missing when the light was turned off, which was directly proportional to the duration. A sequence of off-steps 5-100 ms in duration were presented in a pseudorandom order. Electrophysiological measurements in vitro show consistent responses between cones but imaging the final synaptic output in vivo revealed that PR1 had significantly different sensitivities to light, as illustrated by the two nearby pedicles in Fig. 2A–D (see also Supplemental Video 2). While cone 1 reliably responded to 10 ms flashes and saturated at 20 ms, cone 2 only started to respond to flashes of ~18 ms, and this response did not saturate until the flash duration was 50 ms (Fig. 2C, D).

The relationship between flash duration (d) and relative rate of glutamate release (R) could be described as a sigmoid function,

R = R_max / (1 + exp((d_1/2 - d) / k)) (1)

where R_max is the saturating response, d_1/2 is the off-step duration generating the half-maximal response and k is a constant (Fig. 2E). Importantly, the range of flash durations used fall within the integration period of cones where flash duration and intensity are related linearly. The stimulus-response functions for cone 1 and 2 differed both in terms of R_max, reflecting synaptic gain, and d_1/2, reflecting light sensitivity. Qualitatively similar differences were routinely observed in simultaneously recorded neighbouring PR1, indicating that response heterogeneity reflected biological rather than experimental variation. A series of fits to the stimulus-response function of 70 cones from 12 fish, normalized to the maximum response, is shown in Fig. 2F, and the distribution of d_1/2 in Fig. 2G. The d_1/2 value ranged from 10 to 41 ms, and the mean value of d_1/2 was 21 ± 6.6 ms (± sd). These results demonstrate a substantial degree of response heterogeneity across PR1 in vivo.

High reliability and temporal precision of the synaptic signal. A key property of any neural signal is its reliability. Neural information is degraded by noise that causes responses to vary when a stimulus is repeated, and synapses are a major source of such noise because of the stochasticity of the presynaptic processes that control vesicle fusion. At many central synapses these processes obey Poisson statistics with responses to a repeated stimulus showing a coefficient of variation (CV = sd/mean) of 1. Although it has sometimes been assumed that ribbon synapses of cones also obey Poisson statistics, more recent work indicates that this is not the case in rods.

To explore the reliability of the cone output, we delivered many off-steps (usually 98) of a fixed duration (40 ms) (Fig. 3A). For individual PR1, the amplitudes of responses to this repeated stimulus were consistently found to be normally distributed (Fig. 3B). The scatterplot in Fig. 3C shows the relation between the standard deviation and mean of the responses output from 32 cones: in all cases the CV was far below 1 (dashed line) and averaged 0.13 ± 0.05 F’s-1 (mean ± sd; Fig. 3D). Re-expressing this metric as the signal-to-noise ratio (SNR = mean^2 / variance) yields an average of 93 for the output of one pedicle. The SNR will depend on the square root of the number of ribbons if these are affected by independent sources of noise. With this assumption and taking an average of two ribbons per pedicle we can place a lower limit of SNR = 66 for a single ribbon synapse.

Responses of sensory neurons vary in their timing as well as their amplitude. To investigate this aspect of the signal transmitted from cones we began by measuring the time-to-half peak (t_1/2) of responses to off-steps, as shown in Fig. 3E. The mean value of t_1/2 after a 40 ms off-step was 29 ± 6.5 ms across a sample of 32 PR1 (Fig. 3F). The temporal precision of these responses was measured as the standard deviation in t_1/2 at each cone output (Fig. 3G). Some cones displayed temporal jitter in the order of 1-2 ms, and the average was 3.1 ± 0.8 ms. Similar levels of temporal precision were observed for flashes of 30 ms and longer, but jitter increased as off-steps became shorter (Fig. 3H). A temporal precision of a few milliseconds can also be observed in the responses of bipolar cells and ganglion cells to high-contrast stimuli.

Together, the results in Figs. 1–3 demonstrate that while the ribbon synapses of PR1 transmit the first visual signal with extreme reliability and temporal precision (Fig. 3), the luminance sensitivity varies substantially across the population (Fig. 2). Similar variations were observed across different average levels of luminance (Supplemental Fig. S4).

Variations in sensitivity to contrast
Natural scenes rarely contain an off-step to darkness. The major task of the cone array is to encode continuous fluctuations in light intensity around an average luminance (Fig. 4) that varies much more slowly than the frequencies driving most vision (Fig. 5). The fact that intensity varies up and down is critical because neurons are rarely linear – changes in opposite directions are often encoded unequally. Synapses are key sites for such rectification. We therefore asked how far the first synapse in vision rectifies and found that the answer depended on timescale (Fig. 4). On short timescales, PR1 encode dark-transitions, but not light-transitions, with a transient burst of glutamate release. However, after this transient phase, a more sustained component of release allowed some cones to linearly represent both positive and negative contrasts. We reach the above conclusions after presenting 0.5 s steps from a photopic background (± 10-100% contrast, 1 Hz, 50% duty cycle). Figure 4A–C shows representative responses and their basic quantification from three PR1. Consistent with previous measurements of ribbon-mediated release at photoreceptor synapses, responses consistently displayed both transient and sustained components (Fig. 4D–F). Cone 1 responded to both negative and positive contrasts up to 50% in an approximately linear manner, cone 2 only responded to strong negative contrasts, and cone 3 mainly responded to positive contrasts.

A range of contrast-response functions were observed in our sample of 97 cones. The most consistent were generated by the transient output and this was always biased to negative contrasts (Off; Fig. 4E). The sustained component tended to be more linear around low contrasts before saturating in some cases (On; Fig. 4F). Bipolar cells post-synaptic to cones also vary in contrast-sensitivity but saturate more obviously above 50%. In larval zebrafish, the contrast gain measured as the slope of the temporal contrast-response function is at a maximum around 30% in both On and Off bipolar cells. To quantify the linearity of each response component, we computed a dark-light index (DLI) following ref. 10:

DLI = (R_Light - R_Dark) / (R_Light + R_Dark) (2)

where R_light and R_dark are the corresponding areas under the curve for the contrast response functions of transient or sustained responses (Methods). Accordingly, DLI ranged from -1 (responding exclusively to negative contrasts) to 1 (exclusively positive contrasts), while 0 indicated a balanced (linear) contrast-dependence. This revealed that both transient and sustained DLI varied greatly across cones, but while the sustained component spanned almost the entire possible coding range, including strongly positively rectified cones (e.g. cone 3), the transient components were always dark-biased (Fig. 4G). Nevertheless, across cones, the DLI of transient and sustained components covaried strongly (Pearson’s correlation coefficient; r = 0.78, p < 0.001), suggesting a single mechanism controlling rectification. Strong positive correlations were found between a number of functional metrics that varied between cones (Supplemental Fig. S3A–E). Moreover, PR1 heterogeneity was robustly observed in different eye regions, including nasal, dorsal, and the acute zone, with no significant difference in the distribution of DLI values between retinal regions (Supplemental Fig. S5A, C).

What might be the consequences of such kinetically unbalanced encoding of positive and negative contrasts? To explore this question, we used stimuli based on natural scenes and a simple model that captured these basic properties of cone release. Starting with an underwater video of translatory optic flow taken in the natural habitat of zebrafish (from ref. 43) (Fig. 4H), we processed each pixel’s intensity time-series to mimic transient and sustained release components of cones (Supplemental Video 3, Fig. 4I, J, Methods): to mimic fast and dark-biased transient release we differentiated and then negatively rectified each intensity sequence while to mimic sustained and linear release the sequence was smoothed in time. This procedure demonstrated that an approximately linear sustained component would yield a time-blurred but otherwise relatively faithful representation of the scene (Fig. 4J, compare first two sets of panels). By contrast, the transient component delivered a very different parallel representation that was strongly biased to features creating negative contrast in the foreground (Fig. 4J, 3rd set of panels – see also Discussion).

Are the variations in PR1 rectification apparent in immediately neighbouring cones? If it were, this would allow a postsynaptic bipolar cell integrating signals from cones with different sensitivities to encode a wider range of contrasts. The dendrites of bipolar cells in larval zebrafish typically span ~20 µm, which corresponds to ~3-4 PR1 in a row. To sample such a group of cones we scanned the laser in a line and compared the dark-light index values (DLi). In the example in Supplemental Fig. S6, neighbouring PR1 within a 5 µm distance exhibited DLI values ranging from -0.34 to -0.87. Moreover, we found the no systematic relationship between inter-cone distance and DLI difference, and the average difference in DLI between neighbouring cones was 0.18 (Supplemental Fig. S6E and F). Similar local heterogeneity in DLI was observed across most recordings (Supplemental Fig. S6G). We conclude that the observed cone heterogeneity is a local property of the cone array that routinely occurs at a spatial scale relevant to integration by immediate downstream circuits.

Variations in frequency-dependent output of individual cones
The decomposition of visual signals through retinal ganglion cells implementing different temporal filters has generally been thought to depend on inhibitory interactions between bipolar cells and amacrine cells in the inner retina. The results in Fig. 4 demonstrate that these different filters are implemented as early as cone synapses, differentially tuned to emphasise either transient or sustained visual signals. The striking emphasis on the transient signalling of negative contrasts rather than positive at the output of PR1 (Fig. 4E) may contribute to the observation that downstream retinal Off-circuits tend to transmit higher frequencies than on.

To characterise the temporal filters at PR1 synapses, we measured responses to a chirp stimulus in which the frequency of a full-field sinusoid (100% contrast) was swept from 20 Hz to 1 Hz over a period of 20 s. Figure 5A shows two example cones with different responses to this chirp. Cone 1 tracked the stimulus throughout the frequency range, while cone 2 responded preferentially at lower frequencies. As before, different frequency-responses were routinely observed amongst neighbouring PR1 recorded simultaneously. The frequency responses were characterised as the spectral centroid, the frequency where the centre of mass of the spectrum is located (Methods). Cones 1 and 2 had spectral centroids of 11.2 Hz and 7.5 Hz, respectively, and across 49 cones this central frequency ranged from ~6 to 11 Hz (mean 9.2 ± 1.1) (Fig. 5B).

To assess the variability in the timing of responses, we next presented a 20 Hz sinusoidal stimulus at 100% contrast for 30 s (Fig. 5C). PR1 again responded heterogeneously: while some were nearly perfectly phase-locked to the stimulus (e.g. cone 3 in Fig. 5D) others appeared to respond more stochastically (cone 4). As a measure of the temporal precision, we calculated the vector strength, a metric which varies from a value 1 for perfect synchronisation to zero for random response timing. Cones 3 and 4 had a vector strength of 0.98 and 0.41, respectively. Across a population of 49 cones from 6 fish, the vector strength at 20 Hz ranged from 0.17 to 0.98 and averaged 0.61 ± 0.25 (± sd) (Fig. 5E).

A key process during ongoing synaptic activity is the replenishment of the readily releasable pool (RRP) at the active zone. The ribbon structure of cones is thought to organize this refilling process to maintain vesicle release at rates required for vision. Figure 5A shows that refilling operated to allow cone 1 to maintain almost maximal responses at stimulus frequencies beyond 15 Hz while cone 2 became unreliable at frequencies above ~7 Hz and did not respond at all to stimuli beyond 12 Hz. To investigate refilling more directly, we used a paired-pulse protocol in which off-steps of 40 ms were delivered with interstimulus intervals (ISI) between 2 and 100 ms (Fig. 6A, B). A 40 ms step was used because it was just long enough to release the RRP involved in the transient phase of release (Figs. 2D, 4E). Overlaying the averaged trace for each ISI pair demonstrates that the peak release rate elicited by the second pulse was lower than that elicited by the first, but gradually recovered with increasing recovery interval (Fig. 6C).

The amount of refilling at a given interval was calculated as the paired pulse ratio (PPR; response 2 / response 1), but it was necessary to apply a correction to the second response in a pair when the ISI was short, and the first response had not recovered fully to baseline (Fig. 6D–F). This was done by using the decay phase of the response to a single 40 ms pulse as a template that was scaled to the first response in each pair; the amplitude of the second response was then measured relative to a baseline provided by the scaled template, as shown by the blue line in Fig. 6F. The PPR as a function of ISI for 40 cones is shown in Fig. 6G with the average recovery time-course for the whole population overlaid in red. Notably, there were a few cones in which the second stimulus generated a larger response than the first at short ISIs. The time for peak release rate during the second pulse to recover to 63% of the first varied widely, as shown by the histogram in Fig. 6H. A distinct subset of cones showed strong recovery at ISIs of 10 ms or less, demonstrating that the refilling process was indeed capable of following frequencies of 20 kHz, as observed in cone 1 in Fig. 5A. The remaining cones showed much slower refilling, with 63% recovery at intervals up to 100 ms. This heterogeneity in the dynamics of refilling likely contributes to wide variations in temporal filtering and jitter across cones (Fig. 5).

Together, the experiments in Figs. 4–6 illustrate how PR1 encode visual stimuli in a highly heterogeneous manner. The simple picture of a given cone type as representing a single temporal filter driving downstream circuits does not hold.

Heterogenous output of PR1 depends on horizontal cell feedback.
What is the source of the variability in the output of different cone synapses? Do these variations reflect functional differences intrinsic to each cone or differences in the feedback signals from the network in which they are embedded? We reasoned that the relative contributions from these two possible sources might be disambiguated by pharmacologically isolating cones from the rest of the retinal circuit. This was achieved by blocking the cone drive to horizontal cells using the AMPA receptor antagonist CNQX injected into the eye (Methods, estimated final concentration: 50 µM), a manipulation expected to hyperpolarise horizontal cells and reduce their negative feedback to cones.

A direct comparison of the output from one cone before and after inhibiting negative feedback is shown in Fig. 7A, where the stimulus is the same series of positive and negative contrast steps used in Fig. 4. With feedback intact, the sustained component of the response was approximately linear, but after injecting CNQX the output was strongly rectified to negative contrasts. A similar pattern was observed in the contrast-response functions of 26 cones in 17 fish (Fig. 7B, where for simplicity the DLI is the sum of the transient and sustained release components). These results immediately suggest a possible mechanism for the varying degrees of rectification in the contrast-response functions of PR1: differences in the strength of negative feedback from horizontal cells cause the synapses of different cones to operate around different set-points, defined by the baseline rate of vesicle release at a given average luminance (see also ref. 53).

To explore this idea, we estimated each cones’ full coding range based on the minimum release rate at the highest light intensity and the maximum rate in darkness (Fig. 7C). We then measured the baseline release rate as a fraction of this full range. Blocking feedback from horizontal cells lowered this set-point (Fig. 7D; p < 0.001, Wilcoxon Rank Sum). Crucially, the degree of rectification in cone output was strongly correlated with the baseline measured with or without block of negative feedback (Fig. 7E; Pearson’s correlation coefficient r = 0.77 in control and 0.89 in CNQX, both at p < 0.001). Removal of horizontal cell feedback always shifted an individual cone’s synapse towards a lower baseline and a correspondingly lower DLI as well as reducing the variability in these properties across the population (Fig. 7C, E; variance of DLI values for control = 0.07 and in CNQX = 0.04; variance of baseline values for control = 0.01 and in CNQX = 0.003). Qualitatively similar results were also observed when blocking horizontal cell feedback using HEPES instead of CNQX (Supplemental Fig. S3G–L). Blocking horizontal cell feedback also reduced preferred temporal frequencies (Fig. 7F–H; control = 6.2 ± 2.1 Hz, CNQX = 5.0 ± 1.7 Hz, Wilcoxon Rank Sum p < 0.05, n = 25 cones, 11 fish).

Together, these observations demonstrate that feedback received from horizontal cells determines the set-point of a cone synapse and, therefore, how it encodes positive versus negative contrasts. We conclude that a major source of heterogeneity in cone outputs is differences in their interactions with horizontal cells in the outer plexiform layer.

Possible benefits of cone-heterogeneity for encoding natural contrasts
Variations in the input-output relation of the first synapse in vision are expected to impact all downstream processing. To explore what those impacts might be, we set up a data-driven model of bipolar cells that sum cone inputs. We drove the model with a naturalistic contrast series and compared responses to homogeneous and heterogeneous populations of cone synapses (Fig. 8). These responses were obtained by repeatedly stimulating individual PR1 with a 15 s naturalistic contrast sequence extracted from the zebrafish natural environment (Fig. 8A–C). This sequence was synthesised by combining available video data from the zebrafish natural environment (Fig. 8A, B) with data on eye- and body-movements from free-swimming zebrafish (Fig. 8C, D; see Methods). The results indicate that heterogeneous sampling can improve representation of naturalistic contrast series (Fig. 8).

Five repeats of the contrast series were presented to a total of n = 20 PR1 from 8 fish. As before, different PR1 encoded the sequence in a highly heterogeneous manner, here illustrated by the two example cones shown in Fig. 8E. Despite this variation across different cones, the responses of both cones individually were highly reproducible, as expected from the high levels of response reliability and time precision (cf. Figs. 2, 3). To explore how these two cones differed in their encoding of a common stimulus, we next calculated each cone’s mean response across the five repeats and compared each to the original stimulus. We reasoned that the mean of five responses from a single cone is equivalent to the response of a hypothetical downstream neuron that averages individual responses from five functionally identical cones. In other words, the two mean responses mimic two different variants of a scenario in which the cone array is functionally homogeneous. To compare how accurately these two homogenous cone-scenarios represented the contrast sequence, we calculated the correlation coefficient between each mean and the stimulus over a 1 s sliding window (Fig. 8F, top traces, Methods). Neither cone’s output was systematically superior to the other; during some phases of the sequence cone 1 correlated more strongly with the stimulus than cone 2, but in other phases this was reversed. It appears difficult to define an optimal set point for an individual cone.

Next, we tested bipolar cell responses with heterogenous cone inputs. Using the same five stimulus repeats from twelve recorded cones, we compared performance with i) a population of 12 bipolar cells, each summing across the five trials recorded from the same cone (as above; homogeneous), and ii) a population of 1,000 bipolar cells, each randomly summing any five trials from the full set of 60 trials available (heterogeneous). The individual performances of all modelled bipolar cells are shown in Fig. 8F middle/bottom. We then took the mean performance of either population as an indication of their central tendency and computed their relative differences (Fig. 8G, Methods) such that a difference of 0% indicates that both strategies, on average, perform equally well, while positive or negative percentages indicate correspondingly superior performance of the heterogeneous and homogeneous strategy, respectively. According to this simple comparison metric, the heterogeneous model systematically outperformed the homogeneous model by ~8% on average, and by up to ~30% depending on the detail in the stimulus. Moreover, the estimated performance boost was positive in >97% of time epochs lasting 1 s.

This model provides a simple example of how a functionally heterogeneous array of cone synapses may improve the coding of a naturalistic contrast series in downstream neurons.

Methods
Experimental model
Animals. All procedures were carried out in accordance with the UK Animals (Scientific Procedures) Act 1986, under the UK Home Office guidelines, and approved by the University of Sussex Animal Welfare and Ethical Review Board. Zebrafish larvae (Danio rerio) were housed in petri dishes (maximum 50 animals per dish) at 28°C under a standard 14:10 day/night light cycle (light on at 8 am, light off at 10 pm, with a 15-minute luminance ramp up or down accordingly). Animals were grown in E2 medium (Westerfield, 2000) with the addition of 200 µM 1-phenyl-2-thiourea (PTU, Sigma, P7629) from 1 day post fertilisation (dpf) to prevent further melanogenesis (Karlsson, Von Hofsten and Olsson, 2001).

The following previously published transgenic lines of zebrafish were used: Tg(Cx55:5:nlsTrpR,-tUAS:SFiGluSnFR) for expression of SFiGluSnFR in horizontal cells (Yoshimatsu et al., 2021), Tg(thrb:TdTomato) for expression of TdTomato in PR1 (Suzuki et al., 2013). Adults were outcrossed with casper for minimised pigmentation (White et al., 2008) and crystal for minimised pigmentation and melanin (Antinucci and Hindges, 2016), and offspring were raised in E2 solution with the addition of 0.1 mM 1-phenyl-2-thiourea (PTU, Sigma-Aldrich, P7629) to prevent further melanogenesis.

For all two-photon imaging experiments, zebrafish larvae were 6-7 dpf larvae. Imaging experiments were carried out in a temperature-controlled room (~20°C) between 1 and 6 pm. Zebrafish larvae were immobilised in 2% low-melting-point agarose (Fisher Scientific, Cat: BP1360-100), positioned to lie on their side in an imaging chamber on top of a glass coverslip (thickness 0). The imaging chamber was filled with E2 fish medium, submerging the agarose-fixed larvae. Eye movements were further prevented by injection of α-bungarotoxin (1 nL of 2 mg/ml; Tocris, Cat: 2133) into the ocular muscles behind the eye.

Two-photon glutamate imaging. A Scientifica two-photon imaging system was used, equipped with a mode-locked Ti:Sapphire laser (Chameleon II, Coherent) tuned to 915 nm for excitation of the SFiGluSnFR reporter. Two-photon excitation was delivered through the objective (20X water-immersion, XLUMPlanFL, numerical aperture 0.95, Olympus). Emission of the SFiGluSnFR signal was captured above and below the sample, through the objective and condenser (Oil-immersion, numerical aperture 1.4, Olympus). The emitted signal was filtered through GFP filters (HQ 525/50, Chroma Technology) and detected by above- and sub-stage GaAsP photomultiplier tubes (PMTs, H7422P-40, Hamamatsu). The signal from the above- and sub-stage PMTs passed through a current-to-voltage converter before being summed with a custom-built summing amplifier and digitised. Image acquisition was controlled through ScanImage for Windows (3.8, MatLab). Functional recordings of the SFiGluSnFR signal were taken as 1 kHz line scans (128 X 1 pixels per frame, 1 ms per line). The dendritic tips of horizontal cells expressing SFiGluSnFR invaginate the photoreceptor pedicle (alongside bipolar cells (not labelled)) and act as an antenna for photoreceptor glutamate release. Line scans were positioned over the terminals of 3-5 cones per scan. Laser activation of photoreceptors was minimised by focusing the line scan across the pedicle, thus avoiding the photosensitive outer segment.

For consistency, and unless stated otherwise, recordings were taken from the nasal retina, approximately aligned with the outward-facing visual horizon (discussed in ref. 45). Moreover, unlike functional measurements, biosensor expression levels were judged to be relatively homogeneous, based on immunolabelling against GFP in a subset of experiments (not shown). Any observed functional heterogeneity is therefore unlikely to be related to possible heterogeneities in biosensor expression and associated buffering effects.

Light stimulation. Full-field visual stimulation was delivered from an amber light emitting diode (LED, 590 nm) through a light guide (ThorLabs) positioned approximately 1 cm from the zebrafish retina. The light was filtered through a 590/10 nm bandpass filter (Thorlabs) to maximally activate PR1, whilst minimally activating PR2, and to fall outside of the activation range of PR3 and PR4. At full power, the output of the LED at the sample plane was ~660 µW, sufficient to almost suppress glutamate release completely for tens of seconds. Stimulation protocols were driven through IGOR pro 6.3 for Mac (Wavemetrics), and the microscope was synchronised to visual stimulation.

Pre-processing. Movies were imported as tiff files in IGOR Pro 8.0 for Windows (Wavemetrics) and analysed using a suite of analysis routines; SARFIA, GlueSniffer Analysis Package (James et al., 2019), and custom written procedures.

The GlueSniffer Analysis Package was used to analyse line scans and isolate SFiGluSnFR signals to individual cone terminals. Briefly, the spatial profile of a line scan was measured by making a temporal average of the fluorescence signal along the line scan. The average fluorescence profile was fit with a sum of Gaussians, where each Gaussian component corresponds to a ROI. For each ROI, the change in fluorescence over time was extracted, weighted towards the pixels in the centre of the spatial profile, facilitating significant denoising. The relative change in fluorescence, or DF/F, was calculated using the most frequent value (that is, the baseline of the recording). Any drift in the resulting DF/F trace was baseline corrected where required, using a linear correction. Recordings with strong photobleaching or low signal:noise were discarded. Additionally, recordings were discarded if the amplitude of the response to 100% contrast off-steps was below 3 standard deviations of the baseline. The possibility of conflating SFiGluSnFR signals from multiple cone terminals was limited experimentally by taking line scans at spatially separated ROIs, avoiding planes of focus where multiple ROIs were crowded together. Second, the small point-spread function (PSF) of the microscope reduced conflation of signals. The microscope used for these experiments has a PSF of 0.7 µm in x and y, meaning the synapses of cones > 1 µm apart could be easily distinguished. The PSF in z was 2.2 µm. The average cone terminal width was approximately 2-3 µm (Supplemental Fig. S1). Moreover, during imaging the centre of the cone terminals of interest were approximately identified by the strongest SFiGluSnFR signal, and line scans were taken at this imaging plane to minimise the conflation of signal from terminals above and below the plane of focus was minimised.

Next, DF/F traces of the SFiGluSnFR signal were deconvolved to limit the decay element of the SFiGluSnFR fluorescent reporter from the recordings and produce traces that are proportional to the rate of glutamate release, or DF/F per second (F’s-1), using a Wiener filter. The decay of the SFiGluSnFR reporter was estimated by fitting transient responses with a kernel. Transients at most cone terminals could be described with a kernel with a decay of 0.06 s. By filtering the decay of the fluorescent reporter from the signal, it was possible to recover an estimate of the glutamate signal.

Dark-light index. The dark-light index (DLI) was calculated from the cone’s contrast-response function; the area under the curve (AUC) for the negative contrast flashes was measured, giving value b, and the AUC for the positive contrast flashes was measured, giving value a. To calculate an index value, both numbers must be positive; therefore, the value was multiplied by -1. Following this, if the 'a' value was negative (i.e., because the contrast response curve sat above zero for the positive contrast flashes due to a noisy signal), the 'a' value was assumed to be zero. The dark-light index was calculated as (a-b)/(a+b), producing a DLI value between -1 and 1. Where the DLI = -1, the cone exclusively responded to negative contrast flashes, where the DLI = 0 the cone responded to both positive and negative contrast flashes equally, and where DLI = 1 the cone responded exclusively to positive contrast flashes.

Spectral centroid. Glutamate responses to the 20 Hz chirp stimulus were analysed to identify the spectral centroid, that is, the centre of mass of the frequencies in the glutamate responses, following:

Spectral centroid = (Sum_{k=1}^{N} k * F_k) / (Sum_{k=1}^{N} F_k) (3)

Briefly, the Fourier transform (FT) of the response trace was weighted by the power of the frequencies. The sum of this weighted FT was divided by the sum of the FT to produce the spectral centroid.

Vector strength. Glutamate responses to the 20 Hz sinusoid stimulus were analysed to measure the phase locking of responses over repeats of the stimulus phase. Responses were detected by differentiating the glutamate records and using the built-in peak-finding function in Igor Pro 8.0 for Windows, PeakFinder, to detect the rise of the response transient. The mean + 1 sd of the glutamate signal during exposure to mean-light levels (between 6 and 8 s of stimulus) was applied as the threshold for detecting a response. Response times were converted to a phase angle, θ, i.e., where they occur within a stimulus period. Briefly, where one stimulus phase is equal to 360 degrees, the time of the rise of each response was converted to the degree at which it occurred in the stimulus phase, and then to radians. The vector strength was calculated, as described elsewhere, as follows:

Vector strength = sqrt((Sum_{j=1}^{N} sin(θ_i))^2 + (Sum_{j=1}^{N} cos(θ_i))^2) / N (4)

The vector strength for each cone recording is the square root of the sum of the sine and cosine of the vector values for each response. The vector strength may vary from 0 to 1, with 1 implying perfect synchronisation of responses with the stimulus.

Statistics. Statistical analysis was carried out using in-built functions of IGOR Pro 8.0 for Windows (Wavemetrics). When data were not normally distributed, non parametric tests were applied. Tests were two-sided and significance was defined as p < 0.05. Where required, post-hoc tests were used to correct for multiple groups. If an experiment required the delivery of more than one visual stimulus protocol, the order of the stimuli was randomised. Data collection and analyses were not carried out blind as PR1 responses were specifically selected for during experiments and analysis. Recordings were excluded from analysis if a low signal-to-noise ratio of the deconvolved SFiGluSnFR signal impeded accurate detection of light responses to off-steps of light.

Pharmacology. For some experiments, HCs' light responses were blocked with an injection of cyanquixaline (CNQX, Tocris, Cat: 1045, final concentration of approximately 50 µM) in artificial cerebrospinal fluid (aCSF). CNQX is a synthesised non-NMDA (N-Methyl-D-Aspartate) receptor antagonist that blocks AMPA- and kainate-receptors, effectively blocking HC light responses. CNQX was injected intravitreally into the eye, through the cornea adjacent to the lens (usually temporal to the lens).

Beyond blocking cone input into HCs, CNQX additionally affects downstream retinal circuitry, which in turn could modulate the neuromodulator milieu of cones, for example via dopaminergic interplexiform cells (DA-IPCs). However, this is unlikely to cause major off-target effects on PR1 and associated synaptic pathways, because (I) DA-IPCs are thought to be primarily driven by the brain and (ii) previous work points to the rod system, rather than cones, as the major target for dopaminergic modulation in the zebrafish retina.

Immunohistochemistry and confocal imaging. The transgenic line expressing TdTomato in PR1 (Tg(thrb:TdTomato)) was outcrossed with the crystalline, and the resulting larvae were screened for TdTomato expression and a crystal phenotype (clear, non-pigmented eyes). The positive larvae were euthanised by tricaine methanesulfonate (MS222, Sigma Aldrich) overdose and fixed in 4% paraformaldehyde (PFA, Agar Scientific, AGR1026) in 1X PBS on a rocker for 30 minutes exactly at room temperature. After three washes in 1 X PBS, whole eyes were enucleated, and the cornea was removed by using the tip of a 30G needle. The dissected eyes were incubated in 0.1% Triton X-100 (Sigma, X100) made up in 1x PBS for 15 minutes at room temperature. In fresh 0.1% Triton X-100, primary antibody was added; anti-1D4 (Santa Cruz, mouse, sc-57432) at 1:20. Samples were incubated at 4°C for 4 days. Samples were washed five times in 0.1% Triton X-100 in 1X PBS and treated with DAPI nuclear dye (Invitrogen, 33342) at 1:2000, and secondary antibody; Donkey-anti-mouse Dylight 647 (ThermoFisher, A32787) at 1:200. After one day incubation at 4°C, samples were washed in 0.1% Triton X-100 in 1X PBS twice. Samples were mounted in 1.5% agarose in PBS on a coverslip. The eyes were manually oriented with the lens facing down, and the PBS was replaced with mounting media (Vectorlabs, Vectashield, H-1000) for imaging.

Confocal image stacks were taken on an LSM880 (Zeiss) using a 40X oil immersion objective (plan apochromat 40X/1.3 oil DIC UV-IR M27), or a 63x oil immersion objective (HC PL APO CS2, Leica). Contrast, brightness, and pseudo-colour were adjusted for display in Fiji (NIH). Quantification of soma length, outer segment length, and terminal width were performed using custom scripts in IGOR Pro 8.0 (Wavemetrics) after manually marking the inner and outer locations of each organelle of interest.

Electron microscopy and analysis. Serial block face scanning electron microscopy (SEM) volumes were gratefully received from Prof Rachel Wong (University of Washington, USA), as previously part-analysed and published in ref. 14. The data consist of 652 vertical slices through the OPL of the Acute Zone region of the retina of a 6 dpf wild-type larval zebrafish. There is 50 nm between each layer, and the xy pixel dimension is 5 nm.

Using the TrakEM2 plugin in FIJI, the data set was manually aligned, and structures of interest (e.g., photoreceptor outer segment, mitochondria, nucleus, terminal, and HC and BC nuclei and dendritic projections) were manually traced. From here, 3D reconstructions of the traced structures were generated, and basic measurements e.g. volumes and surface areas, were calculated using in-built functions of FIJI.

Modelling bipolar cell responses
A representative stimulus segment was extracted by combining a previously published video-segment (60 Hz) showing an underwater scene of a typical zebrafish natural habitat in Northern India with a typical azimuth-only eye and body movement trajectory measured in a free swimming zebrafish as shown in Fig. 8A–D. To this end, the video was converted to 8-bit greyscale by averaging the RGB components, followed by reading out of the single pixel brightness values over time (1 pixel per frame) as dictated by the eye-body movement trajectory. For simplicity, we did not include eye movements in elevation and kept the entire sequence at -10 degrees relative to the visual horizon, approximately aligned with the position sampled by the nasal PR1 during physiology. We presented five repeats of this stimulus sequence as a 60 Hz widefield stimulus to a total of 20 cones and extracted their glutamate responses. Twelve out of these 20 cones passed a quality criterion of 0.4 (see definitions in ref. 90) and were used for the presented analysis.

Data Exclusions, reproducibility, samples sizes
Glutamate recordings were excluded from analysis if a low signal to noise ratio of the deconvolved SFiGluSnFR signal impeded accurate detection of light responses to off-steps of light. In addition, where applicable, a threshold of mean + 1 sd of the glutamate signal during exposure to bright light was applied, and responses not surpassing this threshold were discarded to reduce the false detection of noise as a response. Reproducibility of the experimental findings was verified by repeating experiments across different batches of larval zebrafish and observing similar effects; by repeating experiments on a different two-photon microscope and observing similar effects; and by collaborating with researchers to verify data collection, data extraction, and data analysis methods. All attempts at replication were successful. Experiments were repeated across at least 5 separate imaging sessions. No statistical method was used to predetermine samples sizes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesting and exciting study that increases our understanding of early visual processing by the retina. They show\nheterogeneity of synaptic glutamate release at cone synapses that has important functional implications for early processing\nof vision. To do this, they expressed a glutamate sensor, iGluSnFr, in zebrafish horizontal cells and selectively stimulated\nred-sensitive cones with an amber light stimulus. By performing this with intact fish, they avoid variability that can arise from\ndifferences in the health of individual cells when one studies isolated retina. They build on their experimental results by\nmodeling the effects of synaptic changes in contrast sensitivity on visual behavior. They used a range of sophisticated\nstimuli and analytical techniques to analyze their data. It is of the highest technical quality. To put the work in better context, I\nsuggest a number of key relevant citations. I also have questions about the potential impact of glutamate buffering by\niGluSnFr and measurement noise on their results, along with a few smaller questions.\n1. A major question of mine is how much of the measurement variability is due to technical measurement variability of small\nfluorescent signals and how much is genuine biological variability. Please discuss how the authors assessed technical\nvariability to address this issue.\n2. The authors should consider and discuss the potential impact of glutamate buffering by iGluSnFr in horizontal cells. In this\ncontext, they can cite Armbruster, Dulla, and Diamond, 2020, eLife.\n3. Line 257. In addition to measurement noise, to what extent are the smaller responses to light increments due to the limited\nability of iGluSnFr to report decrements in release? The authors discuss the smaller signal to noise for responses to light\nincrements on line 155 when explaining why they studied off steps. This limited signal to noise for light flashes is also\nrelevant for later measurements in which they suggest greater sensitivity to light off than light on.\n4. To put these studies in a proper context of prior work, I suggest they note that Burkhardt and colleagues previously\nshowed a tremendous (up to 20fold) increase in contrast sensitivity across the cone synapse by comparing cone vs. bipolar\ncell contrast responses (Burkhardt and Fahey, J. Neurophysiol. 1998). They also showed that the range of contrast\nsensitivities measured in bipolar cells closely matched the range of contrasts present in real world images (Burkhardt,\nFahey and Sikora, Vis Neurosci, 2006). Furthermore, this increase in contrast sensitivity is present in synaptic currents of both ON\nand OFF bipolar cells suggesting it arises presynaptically (Thoreson and Burkhardt, Vis Neurosci 2003), consistent with\npresent results.\n5. When discussing the role of horizontal cell feedback in modulating contrast sensitivity (line 678), I also suggest they cite\nwork by Fahey and Burkhardt who showed evidence that the increase in contrast sensitivity at cone synapses can be\nmodulated by horizontal cell feedback (Fahey and Burkhardt, Vis Neurosci 2001), consistent with the present results.\n6. Page 5. In contrast to the wide variations in amplitude/intensity relationships seen in Fig. 2, amplitude/intensity\nrelationships for cone light responses are essentially identical among cones with the same spectral sensitivity (e.g., Baylor\nand Fuortes, J Physiol 1970). I suggest the authors note this fact and contrast it with heterogeneity in sensitivity\nmeasurements of Fig. 1 to highlight further transformations at the cone synapse.\n7. I suggest including additional discussion of mechanisms that might contribute to cone-to-cone variations in synaptic\nrelease. In that context, Grassmeyer and Thoreson (Front Cell Neurosci 2019) combined electrophysiology and calcium\nimaging at individual cone ribbons to show that while the voltage-dependence of whole cell calcium currents is quite similar\namong cones, there is greater variability in the voltage-dependence of calcium signals measured at individual ribbons. This\nstudy directly showing ribbon-to-ribbon variability could be cited when discussing potential sources of heterogeneity (e.g.,\nline 417). Another potential contributor to cone-to-cone variability in synaptic output that can also be mentioned is variation\namong cones in their resting membrane potentials (and this has never been carefully measured).\n8. Using paired recordings and confocal calcium imaging, Grassmeyer and Thoreson also showed directly that a single\nhorizontal cell can modify calcium signals at one ribbon independent of another in the same cone. This study can be cited as\nadditional strong evidence for the role of horizontal cells in directly adjusting synaptic sensitivity (e.g., line 678).\n9. Did blocking feedback alter variability in D1/2 measurements? If the greater heterogeneity in D1/2 values of Fig. 2 arises\nat the synapse, then one would predict that it would also be sensitive to blockade of feedback. If not, then I would be more\nconcerned that it might reflect measurement variability rather than biological variability.\n10. Line 179. I suggest explicitly stating that the range of flash durations used in Fig. 2 fall within the integration period of an\nindividual cone where flash duration and intensity are linearly related to one another (Baylor and Hodgkin, 1973; J Physiol).\nIn this temporal range, the half maximal duration can thus be related directly to half maximal intensity.\n11. Line 264, 287, 636. I suggest citing the highly relevant study by Jackman et al. (Nature Neurosci 2009) who showed that\nthe prominent off responses seen in horizontal cells following an OFF step arise from the replenishment of vesicles to the\nreleasable pool on the ribbon during the prior period of illumination (and thus hyperpolarization of the cone).\n12. While it is likely that the major effects seen in the present study are due to blocking negative feedback, I suggest that the\nauthors note that CNQX will also block positive feedback from horizontal cells (Jackman et al., PLoS Biol 2011). If they wish\nto disentangle these things in future studies, the authors may wish to use HEPES to selectively block negative feedback.\n13. Why average only 12 trials for the homogenous model but 1000 for the heterogeneous? While I can see there might be\nless of a need for a large sample when using a homogenous population, that is an unexpectedly huge difference. Is any of\nthe improved performance of the heterogeneous vs. homogeneous model in Fig. 7F simply due to averaging 1000 trials for\nthe former but only 12 for the latter?", "reviewer_2": "This is a beautiful study by Herzog et al which addresses fundamental questions of signal encoding at the photoreceptor\nsynapse in vivo. The authors use in vivo two-photon optical imaging of glutamate release from putative single cone\nphotoreceptor terminals in zebrafish larval retina and identify unique features of cone synapse function. The authors identify\nseveral interesting aspects of cone synapse function - i) high reliability and precision of signal encoding that deviates from\nthe classical stochastic (Poisson) process of neurotransmitter release in conventional synapses; ii) heterogeneity in\nluminance, contrast and temporal encoding between synapses of neighboring cones with more pronounced encoding of\nlight decrements than increments and iii) local heterogeneity in output of cone synapse is caused due to HC feedback. In\naddition, the authors develop a model of cone convergence on bipolar cells showing that a heterogenous population of cone\nsignals is better suited for encoding variations in contrast encountered during natural vision than a homogenous population.\nOverall, the results are exciting, the data is solid, figures and text well presented. However, there are a few remaining issues\noutlined below which need to be addressed to bolster the significance of this story.\n1. How does the reliability and temporal precision of cone output vary with the mean release rate? Can the authors use a\nslightly lower background light level and test if both reliability and temporal precision is poorer with higher baseline vesicle\nrelease as one might expect?\n2. Why does cone output response to lower flash durations increase the temporal jitter by ~3-fold compared to 40 ms flash\ndurations? Is this simply a signal to noise issue and perhaps an ambiguity in detecting the peak of the cone response for a\nweaker stimulus? Does the cone response to shorter flash durations also exhibit a low variance across trials?\n3. From the cone responses to 20Hz flickering stimuli, it seems that some cone output synapses can reliably track signals\nand may exhibit a faster rate of recovery from synaptic depression. Can this timescale of synaptic recovery be measured by\npaired light flashes of varying intervals?\n4. Does the strength of temporal tuning correlate with the DLI across cone output synapses?\n5. HC feedback should also affect the kinetics of cone output responses to light decrement and increment flashes. This\nshould be tested in addition to any potential effect of HC feedback on luminance coding, reliability and temporal precision of\ncone output.\n6. The DLI of cone output should be highly dependent on the background light level. More hyperpolarized cones with lower\nbaseline release rate should have a more negative DLI. This should be directly shown. This also suggests that the effect of\nHC feedback on DLI will be less at lower or moderate background light level compared to brighter backgrounds."}, "author_response": "General preamble to all reviewers\nWe thank both reviewers for critically engaging with our work and for their valuable\nsuggestions. In response, we have performed new experiments and analysis and rewritten\nseveral parts of the main text. Additions include:\n1. New experiments explore heterogeneity in the kinetics of vesicle refilling using a\npaired-pulse protocol, shown in a new Figure 6.\n2. New experiments test the effect of different levels of luminance on release from cones,\nshown in the new Supplemental Figure S4.\n3. New experiments deal with minor points, including biosensor expression levels (which\nare quite homogeneous) and heterogeneity in DLI across luminance levels.\n4. New analyses of various datasets which confirm and extend previous assertions.\n5. Editing for clarity and addition of various references, in line with reviewer suggestions\nIn addition, we have updated the nomenclature to describe ‘red’ cones to align with the new\nnaming system put forward in a recent consensus paper, aimed at aligning cone-naming\nsystems across vertebrates (Baden et al. 2025 PLoS Biol). In the new system, zebrafish\n‘ancestral red single cones’ correspond to ‘type 1 photoreceptors’, or PR1. The new naming\nsystem highlights the deep homology of vertebrate cones and illustrates that zebrafish PR1 is\nhomologous to mouse and human PR1 (previously “green/M” cones in mouse, and red/green;\nL/M-cones in human):\nFigure 1 from PLoS Biology 2025 consensus view; A standardized nomenclature for the rods\nand cones of the vertebrate retina. Baden et al.\nWe hope the reviewers will find these revisions useful and suitable to address all points raised,\nand we look forward to hearing back in due course.\nReviewer #1\nThis is an interesting and exciting study that increases our understanding of early visual\nprocessing by the retina. They show heterogeneity of synaptic glutamate release at cone\nsynapses that has important functional implications for early processing of vision. To do this,\nthey expressed a glutamate sensor, iGluSnFr, in zebrafish horizontal cells and selectively\nstimulated red-sensitive cones with an amber light stimulus. By performing this with intact fish,\nthey avoid variability that can arise from differences in the health of individual cells when one\nstudies isolated retina. They build on their experimental results by modeling the effects of\nsynaptic changes in contrast sensitivity on visual behavior. They used a range of sophisticated\nstimuli and analytical techniques to analyze their data. It is of the highest technical quality.\nWe thank the reviewer for their encouraging engagement with our work and for their\nvaluable suggestions.\nTo put the work in better context, I suggest a number of key relevant citations. I also have\nquestions about the potential impact of glutamate buffering by iGluSnFr and measurement\nnoise on their results, along with a few smaller questions.\nThank you – we have aimed to respond to all points raised in full.\n1. A major question of mine is how much of the measurement variability is due to technical\nmeasurement variability of small fluorescent signals and how much is genuine biological\nvariability. Please discuss how the authors assessed technical variability to address this issue.\nWe thank the reviewer for raising this important point. In short, we are confident that\nour reported functional heterogeneity across the population of cones relates to\nmeaningful biological variation rather than measurement noise. Even though\nheterogeneity is evident in single trials within individual cones, the metrics of variability\nacross the population are all based on averaged measurements across many cones.\nIn each cone, many trials were used to obtain that average (typically between 10 and\n98). Properties measured in this way include contrast-response functions (Fig 2),\namplitudes, CV and temporal jitter (Fig 3), DLI (Fig 4) and baseline (Fig. 6).\nAdditionally, several of these metrics are normalised to an individual cone’s response\nrange before comparison.\nThe variability between cones was not due to variations in expression of the biosensor.\nWe performed new experiments staining against GFP in Tg(cx55.5:SFiGluSnFR) and\nthese showed much more consistent levels of expression across the HC dendritic\nterminals invaginating cone terminals compared to SFiGluSnFR signal observed under\ntwo-photon in live tissue (Fig. 1 below).\nReviewer figure 1 | Tg(cx55.5:SFiGluSnFR) expression is variable in vivo under two-photon\nconditions, but not in fixed tissue under confocal. A, A typical experimental view of Tg(SFiGluSnFR)\nin HCs of a 6 dpf larval zebrafish in the nasal retina of a living larval zebrafish, in a sagittal plane. HC\ndendritic connections with pre-synaptic cones are recognised by their globular structure in the distal OPL,\nand are circled (red, dashed). As shown in the data throughout the paper, the fluorescence signals at HC\ntips, reflecting pre-synaptic cone activity, are heterogeneous between nasal PR1, as well as between\ncone types. B, Fixed, flat-mount retina expressing Tg(cx55.5:SFiGluSnFR) of a 6 dpf larval zebrafish,\nimmunolabeled against GFP. HC dendritic projections to pre-synaptic cones are recognisable by their\nrosette patterning, and are circled (red, dashed). Note the heterogeneity in reporter expression between\ncone terminals is much reduced compared to the same transgenic line in vivo, under two-photon.\n2. The authors should consider and discuss the potential impact of glutamate buffering by\niGluSnFr in horizontal cells. In this context, they can cite Armbruster, Dulla, and Diamond,\n2020, eLife.\nWe agree that as with any exogenously expressed proteins, overexpression might\ndisrupt cellular function (e.g. here, by competing with endogenous glutamate signals).\nHowever, we do not think that putative overexpression can explain our results. As\nnoted above (point 1), baseline fluorescence across PR1 in vivo was heterogeneous\nwhile immunolabelling for the GFP within the SFiGluSnFR showed no such\nheterogeneity. Accordingly, it appears that horizontal cells including their tips express\nsimilar levels of SFiGluSnFR, at a level that permits a substantial range of physiological\nheterogeneity.\nWe now note this in lines 778ff, alongside the suggested citation.\n3. Line 257. In addition to measurement noise, to what extent are the smaller responses to\nlight increments due to the limited ability of iGluSnFr to report decrements in release? The\nauthors discuss the smaller signal to noise for responses to light increments on line 155 when\nexplaining why they studied off steps. This limited signal to noise for light flashes is also\nrelevant for later measurements in which they suggest greater sensitivity to light off than light\non.\nThe reviewer is referring to the analysis in Figure 4 (Differential encoding of positive\nand negative contrasts). The smaller responses to light increments are not affected by\nthe lower SNR for decreasing iGluSNFR signals because they are not single trials –\nthey are averages of multiple trials. The example traces in Fig. 4C and D are averages\nof 10 trials within individual cones and the collected results in Fig. 4E and F are from\n90 cones. The smaller responses to light increments that we report are therefore based\non measurements made after averaging out noise.\n4. To put these studies in a proper context of prior work, I suggest they note that Burkhardt\nand colleagues previously showed a tremendous (up to 20fold) increase in contrast sensitivity\nacross the cone synapse by comparing cone vs. bipolar cell contrast responses (Burkhardt and\nFahey, J. Neurophysiol. 1998). They also showed that the range of contrast sensitivities\nmeasured in bipolar cells closely matched the range of contrasts present in real world images\n(Burkhardt, Fahey and Sikora, Vis Neurosci, 2006). Furthermore, this increase in contrast\nsensitivity is present in synaptic currents of both ON and OFF bipolar cells suggesting it arises\npresynaptically (Thoreson and Burkhardt, Vis Neurosci 2003), consistent with present results.\nWe have extensively investigated the transmission of the visual signal through bipolar\ncells in the retina of larval zebrafish. We agree that there is, on average, an increase\nin contrast-sensitivity of bipolar cells compared to cones and now note this with the first\nBurkhardt reference as well as one of our own which is specific to zebrafish (text\nrelating to Fig. 4F). However, the increase in contrast-sensitivity does not arise pre-\nsynaptically, at least in zebrafish. In unpublished experiments, we have used iGluSnFR\nto simultaneously measure the synaptic inputs and outputs of individual bipolar cells\nand find that the saturation of the output at higher contrasts is caused by inhibition\nthrough the amacrine cell network in the IPL.\n5. When discussing the role of horizontal cell feedback in modulating contrast sensitivity (line\n678), I also suggest they cite work by Fahey and Burkhardt who showed evidence that the\nincrease in contrast sensitivity at cone synapses can be modulated by horizontal cell feedback\n(Fahey and Burkhardt, Vis Neurosci 2001), consistent with the present results.\nThank you, we could not find the 2001 Vis Neuro paper as noted but there is the 2003\none by the same authors/journal (“Center-surround organisation in bipolar cells..”)\nwhich partly deals with horizontal cell feedback. We suspect this is the one the reviewer\nwas referring to? Added as suggested.\n6. Page 5. In contrast to the wide variations in amplitude/intensity relationships seen in Fig. 2,\namplitude/intensity relationships for cone light responses are essentially identical among\ncones with the same spectral sensitivity (e.g., Baylor and Fuortes, J Physiol 1970). I suggest\nthe authors note this fact and contrast it with heterogeneity in sensitivity measurements of Fig.\n1 to highlight further transformations at the cone synapse.\nDone.\n7. I suggest including additional discussion of mechanisms that might contribute to cone-to-\ncone variations in synaptic release. In that context, Grassmeyer and Thoreson (Front Cell\nNeurosci 2019) combined electrophysiology and calcium imaging at individual cone ribbons\nto show that while the voltage-dependence of whole cell calcium currents is quite similar\namong cones, there is greater variability in the voltage-dependence of calcium signals\nmeasured at individual ribbons. This study directly showing ribbon-to-ribbon variability could\nbe cited when discussing potential sources of heterogeneity (e.g., line 417). Another potential\ncontributor to cone-to-cone variability in synaptic output that can also be mentioned is variation\namong cones in their resting membrane potentials (and this has never been carefully\nmeasured).\nDone.\n8. Using paired recordings and confocal calcium imaging, Grassmeyer and Thoreson also\nshowed directly that a single horizontal cell can modify calcium signals at one ribbon\nindependent of another in the same cone. This study can be cited as additional strong\nevidence for the role of horizontal cells in directly adjusting synaptic sensitivity (e.g., line 678).\nAdded as suggested.\n9. Did blocking feedback alter variability in D1/2 measurements? If the greater heterogeneity\nin D1/2 values of Fig. 2 arises at the synapse, then one would predict that it would also be\nsensitive to blockade of feedback. If not, then I would be more concerned that it might reflect\nmeasurement variability rather than biological variability.\nWe did not record responses to the mixed off-step duration stimulus before and after\nHC block, therefore we cannot comment how stimulus-response functions may shift\nafter HC blockade, and how this would affect heterogeneity at the population level.\nHowever, we do show how contrast-response functions change after HC block, and\nthat at the population level the extracted response metrics of DLI and baseline release\ntend to decrease in variability (Fig 6, B, D and E).\n10. Line 179. I suggest explicitly stating that the range of flash durations used in Fig. 2 fall\nwithin the integration period of an individual cone where flash duration and intensity are linearly\nrelated to one another (Baylor and Hodgkin, 1973; J Physiol). In this temporal range, the half\nmaximal duration can thus be related directly to half maximal intensity.\nDone.\n11. Line 264, 287, 636. I suggest citing the highly relevant study by Jackman et al. (Nature\nNeurosci 2009) who showed that the prominent off responses seen in horizontal cells following\nan OFF step arise from the replenishment of vesicles to the releasable pool on the ribbon\nduring the prior period of illumination (and thus hyperpolarization of the cone).\nDone.\n12. While it is likely that the major effects seen in the present study are due to blocking\nnegative feedback, I suggest that the authors note that CNQX will also block positive feedback\nfrom horizontal cells (Jackman et al., PLoS Biol 2011). If they wish to disentangle these things\nin future studies, the authors may wish to use HEPES to selectively block negative feedback.\nThanks, to explore this we did use HEPES as an alternative to CNQX, with essentially\nthe same results – see Supplemental Figure S3. This additional dataset was however\ninsufficiently flagged in the previous MS (now been addressed, line 441/2).\n13. Why average only 12 trials for the homogenous model but 1000 for the heterogeneous?\nWhile I can see there might be less of a need for a large sample when using a homogenous\npopulation, that is an unexpectedly huge difference. Is any of the improved performance of\nthe heterogeneous vs. homogeneous model in Fig. 7F simply due to averaging 1000 trials for\nthe former but only 12 for the latter?\nThanks for raising this. The 1,000 iterations were chosen to reasonably capture the\ncone-combinatorial space to map the 12 recorded cones onto all possible\ncombinations of five. By contrast, the combinatorial space to map the 12 cones\nhomogeneously is 12 by definition).\nThe full heterogeneous combinatorial space equals 792:\nC(n,k) = n! / (k!(n-k)!),\nWhere n = 12 (total datasets) and k = 5 (number chosen at a time)\nSo: 12! / (5!(12-5)! = 12!/(5!*7!) = 95040/120 = 792.\nChanges to this iteration number did not qualitatively affect the results.\nReviewer #2\nThis is a beautiful study by Herzog et al which addresses fundamental questions of signal\nencoding at the photoreceptor synapse in vivo. The authors use in vivo two-photon optical\nimaging of glutamate release from putative single cone photoreceptor terminals in zebrafish\nlarval retina and identify unique features of cone synapse function. The authors identify several\ninteresting aspects of cone synapse function - i) high reliability and precision of signal\nencoding that deviates from the classical stochastic (Poisson) process of neurotransmitter\nrelease in conventional synapses; ii) heterogeneity in luminance, contrast and temporal\nencoding between synapses of neighboring cones with more pronounced encoding of light\ndecrements than increments and iii) local heterogeneity in output of cone synapse is caused\ndue to HC feedback. In addition, the authors develop a model of cone convergence on bipolar\ncells showing that a heterogenous population of cone signals is better suited for encoding\nvariations in contrast encountered during natural vision than a homogenous population.\nOverall, the results are exciting, the data is solid, figures and text well presented.\nWe thank the reviewer for critically engaging with our work and their support.\nHowever, there are a few remaining issues outlined below which need to be addressed to\nbolster the significance of this story.\n1. How does the reliability and temporal precision of cone output vary with the mean release\nrate? Can the authors use a slightly lower background light level and test if both reliability and\ntemporal precision is poorer with higher baseline vesicle release as one might expect?\nThank you for raising this interesting point, which we now address in new experiments\nshowing how amplitude, reliability and temporal precision remain high across all\ndifferent light conditions. This new data is now included and discussed as\nSupplemental Figure S4.\nSpecifically, we imaged cone glutamate responses to trains of off-steps at three\ndifferent background light intensities. The recordings were continuous, and an\nelectronic filter wheel containing neutral density filters positioned in the stimulus light\npath generated different background light intensities for each repeat of the off-step\ntrain (A). ND 1 (10-fold decrease), ND 0.5 (3.2-fold decrease) and ND 0 (no filter)\nconditions consistently evoked different sustained glutamate release rates, visibly\ndecreasing with increasing background light level (A, B and D), with correspondingly\nincreased amplitudes (C and E). Across light conditions, cones continued to exhibit\nhighly reliable responses, with variation in response amplitudes consistently falling\nbelow Poisson (F,G).\nLikewise, the temporal precision of PR1 responses to trains of off-steps of light remains\nhigh across all three conditions (H-J), with the majority of cone responses fluctuating\nin the order of 1-2 ms (J).\nThroughout, functional heterogeneity remained a prominent feature of responses\nacross conditions. The dimmest light condition tended to produce responses with the\nhighest amplitude variability, but increasingly brighter background light did not always\nresult in more consistent responses; some cones exhibit lowest CV at ND 0.5, and\nothers at 1.0. Likewise for temporal precision, ND 0.5 tended to produce responses\nwith the lowest temporal jitter, which may increase in both the dimmer and brighter\nconditions.\nSupplemental Figure S4 | Response amplitude and timing vary with mean light level. A, Trains of\n40 ms flashes to darkness (30 repeats, 460 ms inter stimulus interval) were delivered at 3 background\nlight levels; neutral density filters (ND) produced a ~10-fold (ND 1.0, black) and ~3.2-fold decrease (ND\n0.5, red) from full LED brightness (no ND filter, ND 0, blue). Deconvolved glutamate responses of an\nexample PR1 are shown. B, Different light levels evoke glutamate responses with different baselines\n(dashed lines) and amplitudes (based on A). C, Response amplitude measured from baseline glutamate\nrelease for each background light condition (mean release during 4 s of steady background light prior to\noff-step train) to the response peak. Distributions of response amplitudes for each light condition are\nplotted as histograms (based on A), with Gaussian fits superimposed (ND 1.0, black, mean = 0.8 ± 0.2;\nND 0.5, red, mean = 1.9 ± 0.3; ND 0, blue, mean = 2.4 ± 0.2). D, Across 139 PR1 (6 fish), baseline\nglutamate release decreases with increased background light levels. E, Response amplitude tends to\nincrease with background light levels, although heterogeneity in this trend is present between ND 0.5 to\nND 0: 52% of cones exhibited a decrease in response amplitude when background light levels were\nhighest (ND 0). F, In all background light conditions, the relationship between mean and standard deviation\n(SD) of response amplitudes consistently fell below the equivalence line (where mean = SD), as would be\nexpected from a Poisson release process (dashed line). G, The coefficient of variance (CV) of the data\nshown in E and F shows responses at the lowest background light level exhibit significantly higher variance\n(ND 1.0, black, mean = 0.3 ± 0.1; ND 0.5, red, mean = 0.1 ± 0.1; ND 0, blue, mean = 0.2 ± 0.1; post-hoc\nTurkey test following a significant one-way ANOVA of p < .00001) for pairwise comparisons between ND\n1.0 and ND 0.5, and ND 1.0 and ND 0 (both p < .00001), but not significant between ND 0.5 and ND 0 (p\n= 0.32)). High temporal precision was observed over all three light conditions. Response time was\nmeasured as time to threshold (mean glutamate release + 2 SDs), as shown for the example cone\nresponses to the ND 1.0 condition, H, and for all PR1 across the three light conditions, I, (ND 1.0, black,\nmean = 30.5 ± 3.1; ND 0.5, red, mean = 27.0 ± 1.9; ND 0, blue, mean = 28.9 ± 3.6). Temporal precision,\nquantified as temporal jitter (SD of time to threshold), (ND 1.0, black, mean = 3.4 ± 2.0; ND 0.5, red, mean\n= 1.6 ± 1.0; ND 0, blue, mean = 2.4 ± 1.8). Pairwise comparisons of temporal jitter between ND 1.0 and\nND 0.5, and ND 0 and ND 0.5 are both significantly different (both p < .00001), but not comparison\nbetween ND 1.0 and ND 0 (p = 0.51) (post-hoc Turkey test following a significant one-way ANOVA of p <\n.00001). As with amplitude, there is heterogeneity in how background light level affects the response time\nand temporal precision of nasal red-cones, and a general rule is not present. (For D - G, I, J, n = 139 red-\ncones, 6 fish).\n2. Why does cone output response to lower flash durations increase the temporal jitter by ~3-\nfold compared to 40 ms flash durations? Is this simply a signal to noise issue and perhaps an\nambiguity in detecting the peak of the cone response for a weaker stimulus? Does the cone\nresponse to shorter flash durations also exhibit a low variance across trials?\nThanks for raising this. In response, we reanalysed glutamate release events in\nresponse to off-steps of light (both trains of off-steps and mixed off-step duration stimuli\n– i.e. Figs. 2,3). The results from this are summarised in Reviewer Figure 2 below.\nFirst, changes in glutamate release were only counted as light-evoked responses if\nthey surpassed a threshold, here defined as the mean + 2 SD of the glutamate signal\nduring steady bright light exposure (2 s at start of recording and 2 s at end of recording),\nwhen glutamate release is suppressed to almost zero thereby giving a measure of the\n“noise” in the recording. Using a threshold to count only the responses that rise above\nthe noise threshold in the recording facilitates a more accurate detection of responses,\nreduces the chance of noise being counted as a response, and reduces ambiguity in\nresponse detection for smaller responses.\nSecond, the timings of responses exceeding threshold were re-analysed using time-\nto-threshold in place of time-to-half-peak, and temporal jitter (SD) around the threshold\nin place of temporal jitter around half-peak. The amplitudes of these responses\nexceeding the threshold were counted as the maximum peak within the response\nwindow, as before.\nAs a result, the amplitude and timing analyses of responses shown in all panels of\nfigure 3 are now updated to reflect a more accurate detection of responses.\nTo directly answer the reviewers query about responses to shorter off-step durations,\nwe observe that these responses tend to be smaller in amplitude (A) and a larger\nproportion of responses do not reach the threshold, compared to larger amplitude\nresponses typical of longer off-step duration conditions. The responses that do exceed\nthreshold exhibit a higher variance in amplitude across trials compared to other\nconditions, demonstrated by the higher CV value at shorter off-step duration (B and\nD). However, when plotting the mean versus SD of response amplitudes for all stimulus\nconditions for all cones, >99% fall below Poisson distribution, indicating that across all\nconditions measured, the detected response amplitude remains highly reliable (C).\nLikewise, the temporal precision of responses to shorter off-step is reduced, with a\nslightly longer time-to-threshold (E) and higher temporal jitter across trials (F).\nThe results from this more stringent re-analysis of amplitude and timing precision of\nresponses to shorter off-steps align with what is shown in the original figures.\nReviewer figure 2 | Re-analysis of amplitude reliability and temporal precision of responses to mixed-\nduration off-steps of light. The amplitude (A-D) and temporal precision (E-F) or responses surpassing a set\nthreshold were re-analysed for 70 red-cones, 12 fish. (A) Mean response amplitude (± SD) and (B) response\ncoefficient of variance (CV) are plotted as a function of off-step duration. (C) SD of response amplitude over 25\nstimulus repetitions is plotted against mean response amplitude for each off-step duration, for each cone. The SD\naxis is plotted in log, with the union line shown in dashed red. (D) Response CV is plotted again mean response\namplitude for each off-step duration, for each cone. Temporal precision of responses were measure as time to\nthreshold (E) and temporal jitter around threshold (F), here measured as SD of time to threshold over 25 stimulus\nrepetitions.\n3. From the cone responses to 20Hz flickering stimuli, it seems that some cone output\nsynapses can reliably track signals and may exhibit a faster rate of recovery from synaptic\ndepression. Can this timescale of synaptic recovery be measured by paired light flashes of\nvarying intervals?\nThank you for suggesting these experiments. We have carried them out and they are\nreported in the new Figure 6 and associated text where we conclude that “This\nheterogeneity in the dynamics of refilling likely contributes to wide variations in\ntemporal filtering and jitter across cones.”\n4. Does the strength of temporal tuning correlate with the DLI across cone output synapses?\nThanks, yes it does!\nWhile we did not record cone responses to both the chirp stimulus and the varying\ncontrast stimulus (to calculate temporal tuning and DLI, respectively), we could\nnevertheless calculate release at mean light levels (which correlates strongly with DLi,\nsee Fig 6E) from responses to the chirp stimulus. Re-analysis of this data shows that\nrelease at mean light levels within the dynamic range of the cone does correlate with\ntemporal tuning (Reviewer Figure 3, below).\nSpecifically, mean glutamate release during the 2 s of light on, light off, and light at\nmean light levels presented at the start and end of the chirp stimulus was measured.\nThe mean values for light on and light off provide an approximate dynamic range for\nthe cone, and the position within which baseline release sits within this dynamic range\ncan then be calculated. Plotting baseline release within the normalised dynamic range\nagainst spectral tuning reveals a positive correlation between the two metrics, that is,\nthe higher baseline release sits, the higher the spectral tuning of a given cone (Pearson\ncorrelation, r = 0.598, p < 0.001). The results indicate that higher baseline release is\nlinked with an ability to transmit information about higher frequencies.\nReviewer figure 3 | Higher baseline release predicts tuning to higher frequencies. Baseline release was\ncalculated as the mean glutamate release during exposure to mean light levels within the normalised dynamic\nrange of the cone, giving a value between 0 and 1. When plotted as a function of spectral centroid, extracted from\nresponses to a 20 Hz chirp stimulus, baseline release forms a positive correlation (Pearson correlation, r = 0.598,\np < 0.001) for n = 41 red-cones, 12 fish.\n5. HC feedback should also affect the kinetics of cone output responses to light decrement\nand increment flashes. This should be tested in addition to any potential effect of HC feedback\non luminance coding, reliability and temporal precision of cone output.\nThanks, yes, they do!\nSpecifically, we now compare response kinetics of cone glutamate release to positive\nand negative responses before and after HC block. For simplicity, we show the\nresponses to 100% positive and 100% negative contrast step conditions (see reviewer\nFigure 4 below).\nTime to threshold and temporal jitter around threshold were measured around a\nthreshold. For responses to -100% contrast which trigger an increase in glutamate\nrelease, the threshold was mean glutamate release at mean light levels + 1 sd, and for\nresponses to +100% contrast which trigger a decrease in glutamate release, the\nthreshold was mean - 1 sd. The lower threshold used here (compared to the usual\nmean + 2 SD) facilitated better capture of the typically smaller, hyperpolarising\nresponses to +100% contrast in CNQX.\nWe find that after HC block, time to threshold significantly increases for both\nincrements and decrements of light; for -100% contrast shifting from 20.8 to 25.6 ms\n(p < 0.001) and for +100% contrast shifting from 42.2 to 50.3 (p < 0.05). Under these\nconditions, blocking HC feedback causes a delay in responses to light increments and\ndecrements. HC feedback accelerates glutamate release, tightly coupling the\nglutamate release event with the onset of the contrast stimulus. The temporal precision\nof responses to -100% contrast was not significantly altered after HC block, but\ntemporal jitter does significantly increase after HC block for +100% contrast responses\n(p < 0.05). Note, the small, decrements in glutamate release in the +100% CNQX\ncondition are typically dominated by noise, and therefore a single point readout such\nas time to threshold is likely unreliable. In other contrast response analyses, we have\nused AUC or the average over a time window to circumnavigate this issue which\nparticularly affects On-contrast steps under the CNQX condition.\nAmplitude was measured as the mean response during the transient window. We find\nthat absolute amplitude significantly increases for -100% contrast responses (p <\n0.001), but not for +100% contrast responses. Amplitude reliability, here measured as\nthe coefficient of variance, does not significantly change for either condition.\nTogether the data demonstrate that HC feedback significantly accelerates responses\nto increments and decrements of light and tempers the amplitude of responses to -\n100% contrast. Under these conditions, HC feedback appears to have no significant\ninfluence on amplitude reliability, nor temporal precision of -100% contrast responses.\nThe changes in luminance coding are already shown in Fig 6, where the DLI is shown\nto decrease after HC block, reflecting a stronger bias towards encoding negative\ncontrasts in the absence of HC feedback.\nReviewer figure 4 | HC feedback accelerates glutamate release. A, Mean glutamate release in response to 10\nrepetitions of the -100% contrast step is shown for an example cone. Note, after HC block, the response is delayed\n(red). Histograms showing a significant increase in time to threshold (mean + or - 1 SD) for individual cones before\n(black) and after (red) HC block for both -100% contrast (B) and +100% contrast (C) steps. C, No significant\ndifference is observed in temporal jitter of responses to -100% contrast, but is for responses to +100% contrast, E.\nF, Peak response amplitudes are marked (blue dots) for the example cone’s responses to -100% (top, left),\n+100% (bottom, left) contrast steps in control conditions, and to -100% (top, right) and +100% (bottom, right) contrast steps\nafter CNQX injection. G, Mean transient amplitude to -100% contrast significantly increases after HC block, but\nchanges in the coefficient of variance are not significant, H. Transient amplitude, I, and response reliability, J, for\nresponses to +100% contrast do not significantly change after HC block. Statistics: non-parametric Wilcoxon\nSigned-Rank Sum Test, significance defined as P < 0.05. N = 27 red-cones, 17 fish.\n6. The DLI of cone output should be highly dependent on the background light level. More\nhyperpolarized cones with lower baseline release rate should have a more negative DLI. This\nshould be directly shown. This also suggests that the effect of HC feedback on DLI will be less\nat lower or moderate background light level compared to brighter backgrounds.\nThank you. We now performed new experiments that directly confirm this notion.\nSpecifically, similar to new Supplemental Figure S4, PR1 glutamate responses to three\nrepeats of pseudo-randomised contrast steps of light were measured at three different\nbackground light intensities. The recordings were continuous, and an electronic filter\nwheel containing neutral density filters positioned in the stimulus light path generated\ndifferent background light intensities for each repeat of the contrast stimulus (A). The\nND 1.0 (10-fold decrease), ND 0.5 (3.2-fold decrease) and ND 0 (no filter, maximum\nLED brightness) evoked different mean glutamate release rates, visibly decreasing the\nhigher the background light level (A - D).\nUnder these conditions, red-cones did not reliably produce responses clear enough to\naccurately calculate dark-light index (DLI) for each light condition for each cone.\nTherefore, it was not possible to measure how DLI changes as a function of\nbackground light level. In place of DLI, we used baseline release within the cone’s\nresponse range. Baseline strongly and positively correlates with DLI (see Fig. 6E), so\ncan serve as a proxy for DLI.\nAt the dimmest background light condition, baseline release sits high within the\nresponse range of the cone, compared to baseline sitting lower within the response\nrange at the two brighter background light level conditions (C,D, and E). This predicts\nthat at dimmer light levels, cones will have a more balanced response to dark and light\ncontrast steps. At brighter light levels, where baseline is low, cones are likely to have\na stronger bias towards dark contrasts, with a compressed dynamic range for encoding\npositive contrast steps. Functionally, the reduced glutamate baseline reflects the loss\nof positive contrast responses, and a compression of the portion of the dynamic range\nused for responding to positive contrasts.\nOverall, we have shown that the contrast response functions of cones depend on\nbackground light level, and indeed at brighter light levels in a more hyperpolarised\nstate, cones exhibit a lower absolute baseline release rate, and a lower baseline\nrelease rate within the response range predicting a low DLI and bias towards negative\ncontrasts.\nWe agree that under lower light conditions, the effect of HC feedback on DLI will likely\nbe less than observed at bright light levels. Under bright light conditions, negative\nfeedback from HCs should be strongly activated, pushing back against the\nhyperpolarising effect of the bright light. Under dimmer light conditions, HC negative\nfeedback should be less, as the hyperpolarising effect of the light is weaker. Therefore,\nwhilst we see a significant drop in DLI (a stronger dark bias) when comparing contrast\nresponses before and after pharmacological HC block (see Fig. 6), it is likely that at\ndimmer light levels where HC feedback is likely weaker, the predicted drop in DLI would\nbe smaller.\nOverall, the conclusions from these new experiments are strongly in line w the those\nshown in new Supplemental Figure S7, For reasons of brevity we have therefore opted\nexclude these additional results into the present MS.\nReviewer figure 5 | PR1 exhibit a stronger dark bias exposed to bright light. A, Positive and negative contrast\nsteps from a mean light level (500 ms steps with 500 ms ISI, 4 contrast conditions; +/- 50 and 100 %, each condition\nrepeated 5 times) were delivered at 3 background light levels; neutral density filters (ND) produced a ~10-fold\ndecrease (ND 1.0, labelled in black) and a ~3.2-fold decrease (ND 0.5, labelled in red) from full LED brightness (no\nND filter, ND 0, labelled in blue). The deconvolved glutamate response of an example red-cone is shown. The\nmean responses to each stimulus condition are shown for the example cone under the ND 1.0 condition (B), ND\n0.5 condition (C) and ND 0 condition (D). For each background light condition, the absolute baseline release\n(calculated as mean glutamate release during 4 s of continuous light exposure prior to contrast steps) is marked\nby a dashed line. and the response range of the cone under each light condition is marked by the vertical line\n(calculated as the difference between the mean minimum and mean maximum response during contrast steps). E,\nAbsolute baseline release significantly decreases as mean light levels increase, as shown for n = 144 PR1, 7 fish\n(pairwise comparisons of baseline release is significant for ND 1.0 and ND 0.5, ND 1.0 and ND 0, and ND 0.5 and\nND 0 (all p < .00001 post-hoc Turkey test following a significant one-way ANOVA of p < .00001). F, Baseline\nrelease within the normalised response range for each condition is significantly higher in the dimmest background\nlight condition (ND 1.0 to ND 0.5 and ND 1.0 to ND 0, both p < 0.0001), predicting a more balanced dark-light bias.\nBaseline release within the response range sits lower at ND 0.5 and ND 0, indicating a dark bias in responses\nunder these conditions."}, {"round": 2, "reviewer_comments": {"reviewer_1": "My earlier questions and concerns have all been adequately addressed in this interesting and technically impressive study.", "reviewer_2": "The authors have done a great job addressing all of my concerns.\nI have a minor comment about mentioning and citing some relevant literature about the asymmetry in cone responses to\nnegative versus positive contrasts. In line 607, the authors can add citations to recent work (Angueyra et al J Neurosci 2021\nand Saha et al Nature Comm 2024) in primate cones which show that contrast asymmetry within individual cones is already\nquite prominent at the level of transduction, as seen in the photocurrent and photovoltage responses which show greater\nsensitivity to negative than positive contrasts, but as nicely shown in this study the cell-to-cell variation in contrast asymmetry\nacross cone population may be created at the synaptic level.\nOverall the revisions to the manuscript have significantly strengthened the story and provided new mechanistic insights. I\nhave no further comments."}, "author_response": ""}]

Decision letter