Biology subjects

Zylberberg, J.

Publications and source records attributed to Zylberberg, J..

12 recordsLinked to original sources

bioRxiv2026

Designing stimuli for non-invasive, cell-type-specific manipulation of retinal ganglion cell activity

The retina encodes visual information through parallel pathways formed by distinct retinal ganglion (RGC) types. Current understanding of how each cell type contributes to downstream visual processing and perception is limited by a lack of tools for selectively manipulating the activities of individual RGC types. Here, we introduce a computational framework called STORM (SpatioTemporal Orthogonal Response Modulation) that creates visual stimuli that aim to selectively modulate the activities of specific RGC types while minimally affecting the activities of nearby RGCs of non-targeted types. To illustrate the approach, demonstrate its flexibility, and identify conditions under which each simulated RGC type could be selectively manipulated, we first applied this framework to simplified models of RGC function, such as Linear-Nonlinear-Poisson (LNP) and LN-LN cascade models. We also deployed this approach on more complex (and more accurate) models of RGC responses, based on convolutional neural networks. We demonstrate that differences in spatial receptive fields, temporal dynamics, response polarities, and nonlinearities can provide sufficient degrees of freedom for designing stimuli that selectively manipulate individual RGC types. We also generalize this approach to conditions where the receptive field locations are unknown for in vivo and clinical applications.

neuroscience↗

bioRxiv2024

Should I stay or should I go? Generalized marginal value theorem with temporal discounting

Consider a person in an environment containing patchy rewards. Under what circumstances should they stay in a given reward patch or leave that patch to seek out a new one? In a landmark 1976 study, Charnov derived the action policy that maximizes the expected rate at which this person gathers rewards. This result is called the Marginal Value Theorem (MVT), and decades of study have shown that humans and other animals behaviors qualitatively follow MVT, but with notable and systematic deviations. These deviations have been hypothesized to arise from the fact that MVT does not incorporate temporal discounting whereas humans and other animals tend to value current rewards more than future ones. Rigorously testing that hypothesis has been challenging because there is no mathematical theory that determines optimal patch foraging decision policies for agents who use temporal discounting. To fill this knowledge gap, I derived the optimal patch foraging policy for agents who exponentially discount future rewards and studied how that optimal policy depends upon their temporal discount rate, and upon the structure of their environment. Notably there are conditions under which the optimal policy with temporal discounting is to leave earlier than is predicted by MVT (under-staying), while under other conditions the optimal policy is to stay longer than is predicted by MVT (over-staying). The theory presented here delineates when each situation arises and may help to interpret the otherwise-puzzling ways in which human and animal behaviors deviate from MVT.

animal behavior and cognition↗

bioRxiv2023

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

neuroscience↗

bioRxiv2023

Biophysical neural adaptation mechanisms enable deep learning models to capture dynamic retinal computation

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on Deep Retina, a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.

neuroscience↗

bioRxiv2021

Learning from unexpected events in the neocortical microcircuit

Scientists have long conjectured that the neocortex learns the structure of the environment in a predictive, hierarchical manner. According to this conjecture, expected, predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of information. It is theorized that the neocortex then changes the representation of incoming stimuli, guided by differences in the responses to expected and unexpected events. In line with this conjecture, different responses to expected and unexpected sensory features have been observed in spiking and somatic calcium events. However, it remains unknown whether these unexpected event signals occur in the distal apical dendrites where many top-down signals are received, and whether these signals govern subsequent changes in the brains stimulus representations. Here, we show that both somata and distal apical dendrites of cortical pyramidal neurons exhibit distinct unexpected event signals that systematically change over days. These findings were obtained by tracking the responses of individual somata and dendritic branches of layer 2/3 and layer 5 pyramidal neurons over multiple days in primary visual cortex of awake, behaving mice using two-photon calcium imaging. Many neurons in both layers 2/3 and 5 showed large differences between their responses to expected and unexpected events. Interestingly, these responses evolved in opposite directions in the somata and distal apical dendrites. These differences between the somata and distal apical dendrites may be important for hierarchical computation, given that these two compartments tend to receive bottom-up and top-down information, respectively.

neuroscience↗

bioRxiv2020

Models of the ventral stream that categorize and visualize images

An open question in systems neuroscience is which objective function (or computational "goal") best describes the computations performed by the ventral stream (VS) of primate visual cortex. Substantial past research has suggested that object categorization could be such a goal. Recent experiments, however, showed that information about object positions, sizes, etc. is encoded with increasing explicitness along this pathway. Because that information is not necessarily needed for object categorization, this motivated us to ask whether primate VS may do more than "just" object recognition. To address that question, we trained deep neural networks, all with the same architecture, with three different objectives: a supervised object categorization objective; an unsupervised autoencoder objective; and a semi-supervised objective that combined autoencoding with categorization. We then compared the image representations learned by these models to those observed in areas V4 and IT of macaque monkeys using canonical correlation analysis (CCA). We found that the semi-supervised model provided the best match the monkey data, followed closely by the unsupervised model, and more distantly by the supervised one. These results suggest that multiple objectives - including, critically, unsupervised ones - might be essential for explaining the computations performed by primate VS.

neuroscience↗

bioRxiv2019

Ignoring correlated activity causes a failure of retinal population codes under moonlight conditions

The retina encodes visual stimuli across light intensities spanning 10-12 orders of magnitude from starlight to sunlight. To accommodate this enormous range, adaptation alters retinal output, changing both the signal and noise among populations of retinal ganglion cells (RGCs). Here we determine how these light level-dependent changes in signal and noise impact decoding of retinal output. In particular, we consider the importance of accounting for noise correlations among RGCs to optimally read out retinal activity. We find that at moonlight conditions, correlated noise is greater and assuming independent noise severely diminished decoding performance. In fact, assuming independence among a local population of RGCs produced worse decoding than using a single RGC, demonstrating a failure of population codes when correlated noise is substantial and ignored. We generalize these results with a simple model to determine the signal and noise conditions under which this failure of population processing can occur. This work elucidates the circumstances in which accounting for noise correlations is necessary to take advantage of population-level codes and shows that sensory adaptation can strongly impact decoding requirements on downstream brain areas.

neuroscience↗

bioRxiv2019

Transformation of population code from dLGN to V1 facilitates linear decoding

How neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

neuroscience↗

bioRxiv2019

Natural global motion processing by populations of direction-selective retinal ganglion cells

Simple stimuli have been critical to understanding neural population codes in sensory systems. Yet it remains necessary to determine the extent to which this understanding generalizes to more complex conditions. To explore this problem, we measured how populations of direction-selective ganglion cells (DSGCs) from mouse retina respond to a global motion stimulus with its direction and speed changing dynamically. We then examined the encoding and decoding of motion direction in both individual and populations of DSGCs. Individual cells integrated global motion over ~200 ms, and responses were tuned to direction. However, responses were sparse and broadly tuned, which severely limited decoding performance from small DSGC populations. In contrast, larger populations compensated for response sparsity, enabling decoding with high temporal precision (<100 ms). At these timescales, correlated spiking was minimal and had little impact on decoding performance, unlike results obtained using simpler local motion stimuli decoded over longer timescales. We use these data to define different DSGC population decoding regimes that utilize or mitigate correlated spiking to achieve high spatial versus high temporal resolution.

neuroscience↗

bioRxiv2018

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models [1] to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting \"Reliable Moment\" model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns.

neuroscience↗

bioRxiv2017

A self-organizing memory network

Working memory requires information about external stimuli to be represented in the brain even after those stimuli go away. This information is encoded in the activities of neurons, and neural activities change over timescales of tens of milliseconds. Information in working memory, however, is retained for tens of seconds, suggesting the question of how time-varying neural activities maintain stable representations. Prior work shows that, if the neural dynamics are in the null space of the representation - so that changes to neural activity do not affect the downstream read-out of stimulus information - then information can be retained for periods much longer than the time-scale of individual-neuronal activities. The prior work, however, requires precisely constructed synaptic connectivity matrices, without explaining how this would arise in a biological neural network. To identify mechanisms through which biological networks can self-organize to learn memory function, we derived biologically plausible synaptic plasticity rules that dynamically modify the connectivity matrix to enable information storing. Networks implementing this plasticity rule can successfully learn to form memory representations even if only 10% of the synapses are plastic, they are robust to synaptic noise, and they can represent information about multiple stimuli.

neuroscience↗

bioRxiv2017

Untuned But Not Irrelevant: A Role For Untuned Neurons In Sensory Information Coding

To study sensory representations, neuroscientists record neural activities while presenting different stimuli to the animal. From these data, we identify neurons whose activities depend systematically on each aspect of the stimulus. These neurons are said to be \"tuned\" to that stimulus feature. It is typically assumed that these tuned neurons represent the stimulus feature in their firing, whereas any \"untuned\" neurons do not contribute to its representation. Recent experimental work questioned this assumption, showing that in some circumstances, neurons that are untuned to a particular stimulus feature can contribute to its representation. These findings suggest that, by ignoring untuned neurons, our understanding of population coding might be incomplete. At the same time, several key questions remain unanswered: Are the impacts of untuned neurons on population coding due to weak tuning that is nevertheless below the threshold the experimenters set for calling neurons tuned (vs untuned)? Do these effects hold for different population sizes and/or correlation structures? And could neural circuit function ever benefit from having some untuned neurons vs having all neurons be tuned to the stimulus? Using theoretical calculations and analyses of in vivo neural data, I answer those questions by: a) showing how, in the presence of correlated variability, untuned neurons can enhance sensory information coding, for a variety of population sizes and correlation structures; b) demonstrating that this effect does not rely on weak tuning; and c) identifying conditions under which the neural code can be made more informative by replacing some of the tuned neurons with untuned ones. These conditions specify when there is a functional benefit to having untuned neurons.\n\nAuthor SummaryIn the visual system, most neurons firing rates are tuned to various aspects of the stimulus (motion, contrast, etc.). For each stimulus feature, however some neurons appear to be untuned: their firing rates do not depend on that stimulus feature. Previous work on information coding in neural populations ignored untuned neurons, assuming that only the neurons tuned to a given stimulus feature contribute to its encoding. Recent experimental work questioned this assumption, showing that neurons with no apparent tuning can sometimes contribute to information coding. However, key questions remain unanswered. First, how do the untuned neurons contribute to information coding, and could this effect rely on those neurons having weak tuning that was overlooked? Second, does the function of a neural circuit ever benefit from having some neurons untuned? Or should every neuron be tuned (even weakly) to every stimulus feature? Here, I use mathematical calculations and analyses of data from the mouse visual cortex to answer those questions. First, I show how (and why) correlations between neurons enable the untuned neurons to contribute to information coding. Second, I show that neural populations can often do a better job of encoding a given stimulus feature when some of the neurons are untuned for that stimulus feature. Thus, it may be best for the brain to segregate its tuning, leaving some neurons untuned for each stimulus feature. Along with helping to explain how the brain processes external stimuli, this work has strong implications for attempts to decode brain signals, to control brain-machine interfaces: better performance could be obtained if the activities of all neurons are decoded, as opposed to only those with strong tuning.

neuroscience↗