A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

Ding, Xuehao; Lee, Dongsoo; Grant, Satchel; Stein, Heike; McIntosh, Lane; Maheswaranathan, Niru; Baccus, Stephen A.

doi:10.1109/ieeeconf53345.2021.9723187

Cited by 3 publications

(3 citation statements)

References 9 publications

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“… 27 , 51 However, we have begun to extend the model to longer timescales by incorporating biophysical components that capture the slow synaptic dynamics thought to underlie these phenomena. 52 …”

Section: Discussionmentioning

confidence: 99%

Interpreting the retinal neural code for natural scenes: From computations to neurons

Maheswaranathan

McIntosh

Tanaka

et al. 2023

Neuron

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Section: Discussionmentioning

confidence: 99%

Interpreting the retinal neural code for natural scenes: From computations to neurons

Maheswaranathan

McIntosh

Tanaka

et al. 2023

Neuron

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“…Thus, the deterministic part of our model has a clear correspondence with the real retina in that its circuitry is mechanistically interpretable and it captures many retinal computations. Every convolutional filter in our model was implemented by linearly stacking a sequence of 3 × 3 small filters, which outperforms the traditional method [21]. A parametric tanh nonlinearity was attached to the last convolutional layer for the purpose of enforcing the refractory period constraint:…”

Section: Methodsmentioning

confidence: 99%

“…[11]. Every convolutional filter in our model was implemented by linearly stacking a sequence of 3 × 3 small filters, which outperforms the traditional method [16]. A parametric tanh nonlinearity was attached to the last convolutional layer for the purpose of enforcing the refractory period constraint:…”

Section: Stochastic Modelmentioning

confidence: 99%

Information Geometry of the Retinal Representation Manifold

Ding

Lee

Melander

et al. 2023

Preprint

Self Cite

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The ability to discriminate visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability were limited to either low-dimensional artificial stimuli or theoretical considerations without a realistic model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we are able to compute the Fisher information metric over stimuli and study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. We found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes noise correlations in the retina are information-limiting rather than aiding in increasing information transmission as has been previously speculated. We observed that sensitivity saturates less in the population than for single cells and also that Fisher information varies less than sensitivity as a function of firing rate. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.

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Adaptation of retinal discriminability to natural scenes

Ding,

Lee,

Melander

et al. 2024

Preprint

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Sensory systems discriminate stimuli to direct behavioral choices, a process governed by two distinct properties - neural sensitivity to specific stimuli, and stochastic properties that importantly include neural correlations. Two questions that have received extensive investigation and debate are whether visual systems are optimized for natural scenes, and whether noise correlations contribute to this optimization. However, the lack of sufficient computational models has made these questions inaccessible in the context of the normal function of the visual system, which is to discriminate between natural stimuli. Here we take a direct approach to analyze discriminability under natural scenes for a population of salamander retinal ganglion cells using a model of the retinal neural code that captures both sensitivity and stochasticity. Using methods of information geometry and generative machine learning, we analyzed the manifolds of natural stimuli and neural responses, finding that discriminability in the ganglion cell population adapts to enhance information transmission about natural scenes, in particular about localized motion. Contrary to previous proposals, noise correlations reduce information transmission and arise simply as a natural consequence of the shared circuitry that generates changing spatiotemporal visual sensitivity. These results address a long-standing debate as to the role of retinal correlations in the encoding of natural stimuli and reveal how the highly nonlinear receptive fields of the retina adapt dynamically to increase information transmission under natural scenes by performing the important ethological function of local motion discrimination.

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A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

Cited by 3 publications

References 9 publications

Interpreting the retinal neural code for natural scenes: From computations to neurons

Interpreting the retinal neural code for natural scenes: From computations to neurons

Information Geometry of the Retinal Representation Manifold

Adaptation of retinal discriminability to natural scenes

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