Biology subjects

Gollisch, T.

Publications and source records attributed to Gollisch, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Image modifications reduce differences in natural-image encoding by retinal ganglion cells between natural and optogenetic stimulation

Optogenetics is a promising approach for restoring vision after photoreceptor degeneration. Yet, little is known about how responses to natural stimuli in optogenetically treated retinas compare to photoreceptor-driven responses and how any differences might be counteracted by adjusting the stimulation. We here therefore directly compared the encoding of natural images by individual retinal ganglion cells under both conditions in mouse retinas with intact photoreceptors and channelrhodopsin expression in ganglion cells. This showed that channelrhodopsin-evoked responses display reduced dynamic range, more linear encoding of receptive-field activation, and reduced sensitivity to local spatial contrast. We thus devised image modifications, combing thresholding and scaling of pixel intensities with spatial low-pass filtering, and found that applying such modified images for optogenetic stimulation restores original encoding characteristics, yielding responses more similar to the original photoreceptor-evoked activity. These findings may help optimize stimulation of optogenetically treated retinas to achieve more natural vision in future therapeutic applications.

neuroscience↗

bioRxiv2025

Interpreting convolutional neural networks to study wide-field amacrine cell inhibition in the retina

Wide-field amacrine cells (ACs) play a unique role in retinal processing by integrating visual information across a large spatial area. Their inhibitory influence has been implicated in multiple retinal functions such as differential motion detection and the suppression of retinal activity during eye movements. However, a coherent understanding of their general function is lacking due to difficulties in directly recording from these cells and identifying effective visual stimuli to activate them. In this study, we used convolutional neural networks (CNNs) to investigate widefield inhibition mediated by wide-field ACs in the marmoset retina. We trained CNNs to mimic the function of the retina by predicting retinal ganglion cell (RGC) population responses to naturalistic movie stimuli and optimising the most exciting inputs (MEIs) to visualise RGCs receptive field (RF) structures. We then optimized suppressive surrounds beyond classical RGC RF boundaries, intended to capture the inhibitory effect of wide-field ACs on RGC activity. These optimized surrounds reduced MEI-elicited activity by 10% to 30%, demonstrating that CNNs not only mimic retinal responses but can also reveal hidden computational aspects of wide-field inhibition. However, suppression strength and generalization varied across architectures and datasets, indicating potential model-specific effects, high-lighting the importance of cautious interpretation. Overall, our approach illustrates how interpretability methods applied to artificial neural networks can offer new hypotheses regarding biological retinal computation, paving the way for targeted experimental validation. The code is available here.

neuroscience↗

bioRxiv2025

Spatial Adaptation of Primate Retinal Ganglion Cells between Artificial and Natural Stimuli

The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, or motion. However, it is unclear to what extent it also adapts to spatial frequency content - as theories of efficient coding would predict - for instance, when switching between natural scenes and white noise. To address this, we analyzed neural activity of marmoset retinal ganglion cells (RGCs) in response to white noise and naturalistic movie stimuli. We trained linear-nonlinear models on both stimuli, evaluated their performance and compared their receptive fields (RFs) across the stimulus domains. We found that the models with spatial filters trained on either one of the stimulus ensembles were not able to predict the neural activity on the other as accurately as the models trained on the target stimulus. This suggests that spatial processing adapts to stimulus statistics. Different RGC types exhibited distinct changes: the midget OFF cells RFs became enlarged under natural movie statistics, resulting in a lower cutoff frequency. Parasol cells did not change their RF size significantly. Large OFF cells RFs decreased in size. All cell types exhibited stronger surrounds under natural movies, resembling the whitening filters predicted by efficient coding. However, quantifying the effect of the filter adaptation on the stimulus power spectrum showed a significant contribution towards whitening only in ON parasol cells. The whitening effect emerged regardless of the training stimulus. These results suggest that while RGCs adapt to the spatial frequency content of the input, efficient coding can only partially account for this adaptation. Significance statementNatural scenes differ from artificial stimuli like white noise, in spatial frequency structure. How the retina adapts to these differences remains unclear. To explore this, we studied responses of four primate retinal ganglion cell types to artificial and natural stimuli. Our results show that some cell types, like midget cells, enlarge their receptive fields and surrounds under natural stimuli, while others, like parasol cells, only enhance surrounds. These changes align qualitatively with the efficient coding theory, which posits redundancy reduction. However, in three cell types, the enhanced surrounds did not significantly whiten responses to natural stimuli, contrary to efficient coding predictions. These findings challenge how fully efficient coding explains retinal adaptation, suggesting that other principles underlie the processing of visual inputs.

systems biology↗

bioRxiv2024

Accelerated spike-triggered non-negative matrix factorization reveals coordinated ganglion cell subunit mosaics in the primate retina

A standard circuit motif in sensory systems is the pooling of sensory information from an upstream neuronal layer. A downstream neuron thereby collects signals across different locations in stimulus space, which together compose the neurons receptive field. In addition, nonlinear transformations in the signal transfer between the layers give rise to functional subunits inside the receptive field. For ganglion cells in the vertebrate retina, for example, receptive field subunits are thought to correspond to presynaptic bipolar cells. Identifying the number and locations of subunits from the stimulus-response relationship of a recorded ganglion cell has been an ongoing challenge in order to characterize the retinas functional circuitry and to build computational models that capture nonlinear signal pooling. Here we present a novel version of spike-triggered non-negative matrix factorization (STNMF), which can extract localized subunits in ganglion-cell receptive fields from recorded spiking responses under spatiotemporal white-noise stimulation. The method provides a more than 100-fold speed increase compared to a previous implementation, which can be harnessed for systematic screening of hyperparameters, such as sparsity regularization. We demonstrate the power and flexibility of this approach by analyzing populations of ganglion cells from salamander and primate retina. We find that subunits of midget as well as parasol ganglion cells in the marmoset retina form separate mosaics that tile visual space. Moreover, subunit mosaics show alignment with each other for ON and OFF midget as well as for ON and OFF parasol cells, indicating a spatial coordination of ON and OFF signals at the bipolar-cell level. Thus, STNMF can reveal organizational principles of signal transmission between successive neural layers, which are not easily accessible by other means.

neuroscience↗

bioRxiv2024

AMPA receptor desensitization shapes gain control in the lateral geniculate nucleus

Synaptic short-term plasticity and EPSP summation determine the processing of visual information by dLGN relay cells. In vitro studies suggested that desensitization of AMPA receptor results in pronounced short-term depression in retinogeniculate synapses thereby influencing activation of relay cells by retinal ganglion cell spikes. However, whether retinogeniculate synapses depress and whether AMPA receptors desensitize in vivo is controversial. We here tested the role of AMPA receptor desensitization in processing of visual information by generating a computational model of a dLGN relay cell containing AMPA receptors with different levels of desensitization. In addition, we investigated the in vivo activity of dLGN relay cells in wildtype and CKAMP44-/- mice. CKAMP44 is an auxiliary subunit that increases desensitization of AMPA receptors in retinogeniculate synapses. The comparison of responses of relay cells in the two genotypes therefore allowed us to obtain information about the contribution of AMPA receptor desensitization in processing of visual information. The simulations and the in vivo data show that AMPA receptor desensitization affects relay cell activity by decreasing input gain and response gain. In addition, desensitization sharpens the response of relay neurons to moving gratings in the temporal domain by decreasing the firing rate when the non-preferred stimulus is presented. Altogether, we uncover a complex role of CKAMP44 and AMPA receptor desensitization in information processing of dLGN relay neurons in vivo.

neuroscience↗

bioRxiv2024

Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies

Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields. This creates sensitivity to spatial contrast, letting the cells respond to spatially structured visual stimuli even when no net change in overall illumination of the receptive field occurs. Yet, computational models of ganglion cell responses are often based on linear receptive fields, and typical nonlinear extensions, which separate receptive fields into nonlinearly combined subunits, are often cumbersome to fit to experimental data. Previous work has suggested to model spatial-contrast sensitivity in responses to flashed images by combining signals from the mean and variance of light intensity inside the receptive field. Here, we extend and adjust this spatial contrast model for application to spatiotemporal stimulation and explore its performance on spiking responses that we recorded from ganglion cells of marmosets under artificial and naturalistic movies. We show how the model can be fitted to experimental data and that it outperforms common models with linear spatial integration to different degrees for different types of ganglion cells. Finally, we use the model framework to infer the cells spatial scale of nonlinear spatial integration. Our work shows that the spatial contrast model can capture aspects of nonlinear spatial integration in the primate retina with only few free parameters. The model can be used to assess the cells functional properties under natural stimulation and provides a simple-to-obtain benchmark for comparison with more detailed nonlinear encoding models. Author SummaryOur visual experience depends on the retinas remarkable ability to detect light patterns and contrast in the world around us. Retinal ganglion cells, the output neurons of the retina, modulate their activity based on signals within small, specific regions of the visual scene, called their receptive fields. But many cells do not only encode overall brightness, summed linearly across the receptive field, but are also sensitive to local spatial contrast, that is, variations in brightness within the receptive field. Computational models that account for this nonlinear spatial integration exist, but require large amounts of data and are challenging to fit. We therefore developed the spatial contrast model, which takes a simple measure of light-intensity variations as an input, and tested it on measured responses of primate retinal ganglion cells to both artificial and naturalistic movies. The model substantially outperformed standard models with linear receptive fields, despite having only one additional tunable parameter. Furthermore, we used the model to investigate the spatial scale at which the cells integrate spatial contrast and found striking consistency across cell types. The spatial contrast model thus offers a practical tool for capturing retinal stimulus encoding and a simple-to-obtain benchmark for modeling nonlinear spatial integration.

neuroscience↗

bioRxiv2024

Convolutional neural network models of the primate retina reveal adaptation to natural stimulus statistics

Understanding the nonlinear encoding mechanisms of retinal ganglion cells (RGCs) in response to various visual stimuli presents a central challenge in neuroscience, driving the development of increasingly complex predictive models. Here, we systematically evaluate linear-nonlinear (LN) models - applying various regularization techniques - and convolutional neural networks (CNNs) of increasing depth, to predict RGC responses to white noise and natural movies. Our analysis includes publicly available datasets from marmoset and salamander retinas. We demonstrate that LN models, when equipped with appropriate inductive biases, can achieve robust predictive performance on neural responses to both white noise and natural movie stimuli. The optimal inductive biases vary substantially across datasets and stimulus types, indicating that the LN models performance is susceptible to these choices. This warrants care when using LN models as baselines: their performance is not fixed, and inappropriate design choices can lead to "unfair" comparisons. However, even in the optimal inductive bias scenario, CNNs consistently outperform LN models across conditions, confirming the advantage derived from their nonlinear representation capacity. Investigating cross-stimulus generalization, we observe that models trained on white noise generalize better to natural movies than vice versa. Notably, LN models exhibit a smaller performance gap between in-domain and cross-domain predictions compared to CNNs, suggesting that the nonlinear processing captured by CNNs is more stimulus-specific. Overall, this study provides valuable benchmarks and methodological insights for neuroscientists designing predictive models of retinal encoding.

neuroscience↗

bioRxiv2023

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cells receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within less than an hour of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

neuroscience↗

bioRxiv2023

Natural stimuli drive concerted nonlinear responses in populations of retinal ganglion cells

The role of the vertebrate retina in early vision is generally described by the efficient coding theory, which predicts that the retina discards spatiotemporal correlations in natural scenes. It is unclear, however, whether the predicted decorrelation in the activity of ganglion cells, the retinas output neurons, holds under gaze shifts, which dominate the natural visual input. We here show that species-specific gaze patterns in natural stimuli can drive strong and correlated spiking responses both within and across distinct types of ganglion cells in marmoset as well as mouse retina. These concerted responses violate efficient coding and signal fixation periods with locally high spatial contrast. Finally, novel model-based analyses of ganglion cell responses to natural stimuli reveal that the observed response correlations follow from nonlinear pooling of ganglion cell inputs. Our results reveal how concerted population activity can surpass efficient coding to detect gaze-related stimulus features.

neuroscience↗

bioRxiv2022

Filter-based models of suppression in retinal ganglion cells: comparison and generalization across species and stimuli

The dichotomy of excitation and suppression is one of the canonical mechanisms explaining the complexity of neural activity. Computational models of the interplay of excitation and suppression in single neurons aim at investigating how this interaction affects a neurons spiking responses and shapes the encoding of sensory stimuli. Here, we compare the performance of three filter-based stimulus-encoding models for predicting retinal ganglion cell responses recorded from axolotl, mouse, and marmoset retina to different types of temporally varying visual stimuli. Suppression in these models is implemented via subtractive or divisive interactions of stimulus filters or by a response-driven feedback module. For the majority of ganglion cells, the subtractive and divisive models perform similarly and outperform the feedback model as well as a linear-nonlinear (LN) model with no suppression. Comparison between the subtractive and the divisive model depended on cell type, species, and stimulus components, with the divisive model generalizing best across temporal stimulus frequencies and visual contrast and the subtractive model capturing in particular responses for slow temporal stimulus dynamics and for slow axolotl cells. Overall, we conclude that the divisive and subtractive models are well suited for capturing interactions of excitation and suppression in ganglion cells and perform best for different temporal regimes of these interactions.

neuroscience↗

bioRxiv2022

Diversity of Ganglion Cell Responses to Saccade-like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of pre- and post-saccadic images. All identified cell types, On and Off parasol and midget cells as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the pre- or the post-saccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of pre- and post-saccadic visual stimuli. This contributes to the functional diversity of the retinas output signals and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity.

neuroscience↗

bioRxiv2022

Novel transient cell clusters provide a possible link between early neural activity and angiogenesis in the neonatal mouse retina

Developing neurons become spontaneously active while growing blood vessels begin to irrigate their surroundings. However, surprising little is known about early interactions between neural activity and angiogenesis. In the neonatal mouse retina, spontaneous waves of impulses sweep across the ganglion cell layer (GCL), just underneath the growing superficial vascular plexus. We discovered clusters of transient auto-fluorescent cells in the GCL, forming an annulus that co-localizes with the frontline of the growing plexus. Blood vessel density is highest within cluster areas, suggesting their involvement in angiogenesis. Once the clusters and blood vessels reach the retinal periphery by the end of the first postnatal week, the clusters disappear, eliminated by microglial phagocytosis. Electrical imaging suggests that they have their own electrophysiological signature. Blocking Pannexin1 (PANX1) hemi-channels with probenecid blocks the waves and the fluorescent clusters disappear following prolonged exposure to the drug. Spontaneous waves initiation points follow a developmental center-to-periphery progression similar to the cluster cells. We suggest that these transient cells are specialized, hyperactive neurons residing in the GCL. They generate spontaneous activity hotspots, thereby triggering waves through purinergic paracrine signaling via PANX1 hemi-channels. The strong activity generated around these hotspots triggers angiogenesis, attracting new blood vessels that provide local oxygen supply. Signaling through PANX-1 attracts microglia that establish contact with these cells, eventually leading to their elimination by phagocytosis. These cluster cells may provide the first evidence that specialized transient neuronal populations guide angiogenesis in the developing CNS through neural activity.

neuroscience↗

bioRxiv2021

Simple Model for Encoding Natural Images by Retinal Ganglion Cells with Nonlinear Spatial Integration

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. We find that response predictions for salamander ganglion cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration in the salamander retina translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields. Author SummaryFor understanding how sensory systems operate in the natural environment, an important goal is to develop models that capture neuronal responses to natural stimuli. For retinal ganglion cells, which connect the eye to the brain, current standard models often fail to capture responses to natural visual scenes. This shortcoming is at least partly rooted in the fact that ganglion cells may combine visual signals over space in a nonlinear fashion. We here show that a simple model, which not only considers the average light intensity inside a cells receptive field but also the variance of light intensity over space, can partly account for these nonlinearities and thereby improve current standard models. This provides an easy-to-obtain benchmark for modeling ganglion cell responses to natural images.

neuroscience↗

bioRxiv2021

Common molecular mechanisms underlie the transfer of alpha-synuclein, Tau and huntingtin and modulate spontaneous activity in neuronal cells

The misfolding and accumulation of disease-related proteins are common hallmarks among several neurodegenerative diseases. Alpha-synuclein (aSyn), Tau and huntingtin (wild-type and mutant, 25QHtt and 103QHtt, respectively) were recently shown to be transferred from cell-to-cell through different cellular pathways, thereby contributing to disease progression and neurodegeneration. However, the relative contribution of each of these mechanisms towards the spreading of these different proteins and the overall effect on neuronal function is still unclear. To address this, we exploited different cell-based systems to conduct a systematic comparison of the mechanisms of release of aSyn, Tau and Htt, and evaluated the effects of each protein upon internalization in microglial, astrocytic, and neuronal cells. In the models used, we demonstrate that 25QHtt, aSyn and Tau are released to the extracellular space at higher levels than 103QHtt, and their release can be further augmented with the co-expression of USP19. Furthermore, cortical neurons treated with recombinant monomeric 43QHtt exhibited alterations in neuronal activity that correlated with the toxicity of the polyglutamine expansion. Tau internalization resulted in an increase in neuronal activity, in contrast to slight effects observed with aSyn. Interestingly, all these disease-associated proteins were present at higher levels in ectosomes than in exosomes. The internalization of both types of extracellular vesicles (EVs) by microglial or astrocytic cells elicited the production of pro-inflammatory cytokines and promoted an increase in autophagy markers. Additionally, the uptake of the EVs modulated neuronal activity in cortical neurons. Overall, our systematic study demonstrates the release of neurodegenerative disease-associated proteins through similar cellular pathways. Furthermore, it emphasizes that protein release, both in a free form or in EVs, might contribute to a variety of detrimental effects in receiving cells and to progression of pathology, suggesting they may be exploited as valid targets for therapeutic intervention in different neurodegenerative diseases. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/452825v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1400e8org.highwire.dtl.DTLVardef@1478fccorg.highwire.dtl.DTLVardef@1319abcorg.highwire.dtl.DTLVardef@1a5ad40_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Ectosomes and exosomes are distinct proteomic entities that modulate spontaneous activity in neuronal cells

Extracellular vesicles (EVs) are important mediators in intercellular communication. However, understanding the biological origin and functional effects of EVs subtypes has been challenging due to the moderate differences in their physical properties and absence of reliable markers. Here, we characterize the proteomes of ectosomes and exosomes using an improved differential ultracentrifugation protocol and quantitative proteomics. Cytoskeleton and glycolytic proteins are distinctively present in ectosomes, while endosomal sorting complexes proteins and tetraspanins are enriched in exosomes. Furthermore, annexin-A2 was identified as a specific marker for ectosomes derived from cell media and human cerebrospinal fluid. Expression of EGFP as a cytosolic reporter leads to its incorporation in EVs and enables their imaging with higher resolution. Importantly, ectosomes and exosomes internalization in neuronal cells results in the modulation of neuronal spontaneous activity. Our findings suggest that EVs cargoes reflect core intracellular processes, and their functional properties might regulate basic biological and pathological processes.

neuroscience↗

bioRxiv2020

Linear and nonlinear chromatic integration in the mouse retina

The computation performed by a neural circuit depends on how it integrates its input signals into an output of its own. In the retina, ganglion cells integrate visual information over time, space, and chromatic channels. Unlike the former two, chromatic integration in the retina is largely unexplored. Analogous to classical studies of spatial integration, we here studied chromatic integration in mouse retina by identifying chromatic stimuli for which activation from one cone type is maximally balanced by deactivation in the other cone type. This revealed nonlinear chromatic integration in subsets of On, Off, and On-Off ganglion cells. Nonlinear On cells differed from the other two classes by displaying response suppression rather than activation under balanced chromatic stimulation. Furthermore, nonlinear chromatic integration occurs independently of nonlinear spatial integration, depends on inhibitory signals from the receptive field surround, and may provide information about chromatic boundaries, such as the skyline in natural scenes.

neuroscience↗

bioRxiv2020

Nonlinear spatial integration underlies the diversity of retinal ganglion cell responses to natural stimuli

How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early visual system, standard encoding models assume that neurons linearly filter incoming stimuli through their receptive fields, but artificial stimuli, such as reversing gratings, often reveal nonlinear spatial processing. We investigated whether such nonlinear processing is relevant for the encoding of natural images in ganglion cells of the mouse retina. We found that standard linear receptive field models fail to capture the spiking activity for a large proportion of cells. These cells displayed pronounced sensitivity to fine spatial contrast, and local signal rectification was identified as the dominant nonlinearity. In addition, we also observed a class of nonlinear ganglion cells with opposite tuning for spatial contrast and a particular sensitivity for spatially homogeneous stimuli. Our work highlights receptive field nonlinearities as a crucial component for understanding early sensory encoding in the context of natural stimuli.

neuroscience↗

bioRxiv2020

Nonlinearities in retinal bipolar cells shape the encoding of artificial and natural stimuli

The retina dissects the visual scene into parallel feature channels, and bipolar cells are speculated to play a key role in this signal separation. Yet, bipolar cells are traditionally viewed as simple, linear neurons. Here, using the salamander retina, we investigated the hypothesis of linear signal processing in bipolar cells by intracellularly recording their voltage signals under artificial and natural visual stimuli. We observed nonlinear representation of contrast and, unexpectedly, also nonlinear spatial integration in a sizable fraction of bipolar cells. Furthermore, linear receptive field models fail to describe responses of nonlinear bipolar cells to spatially structured artificial and natural stimuli. The nonlinear properties occur in the receptive field center and may be cell-type specific, with stronger effects in transient than sustained bipolar cells. Thus, our data suggest that nonlinear signal pooling starts earlier than previously thought, that is, before signal integration in bipolar cells.

neuroscience↗