Biology subjects

Ocko, S. A.

Publications and source records attributed to Ocko, S. A..

4 recordsLinked to original sources

bioRxiv2020

A unified theory for the computational and mechanistic origins of grid cells

The discovery of entorhinal grid cells has generated considerable interest in how and why hexagonal firing fields might mechanistically emerge in a generic manner from neural circuits, and what their computational significance might be. Here we forge an intimate link between the computational problem of path-integration and the existence of hexagonal grids, by demonstrating that such grids arise generically in biologically plausible neural networks trained to path integrate. Moreover, we develop a unifying theory for why hexagonal grids are so ubiquitous in path-integrator circuits. Such trained networks also yield powerful mechanistic hypotheses, exhibiting realistic levels of biological variability not captured by hand-designed models. We furthermore develop methods to analyze the connectome and activity maps of our trained networks to elucidate fundamental mechanisms underlying path integration. These methods provide an instructive roadmap to go from connectomic and physiological measurements to conceptual understanding in a manner that might be generalizable to other settings.

neuroscience↗

bioRxiv2020

Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual andretrosplenial cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains unknown. Here, we combined electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three areas that support navigation: primary visual (V1), retrosplenial (RSC) and medial entorhinal cortex (MEC). Path integration influenced position estimates in MEC more than in V1 and RSC. V1 coded position retrospectively, likely reflecting delays in sensory processing, whereas MEC coded position prospectively, and RSC was intermediate between the two. In combining path integration with landmarks, MEC showed signatures of Kalman filtering, and we report a distance-tuned neural population that could implement such filtering through attractor dynamics. Our results show that during navigation, MEC serves as a specialized cortical hub for reconciling path integration and landmarks to estimate position and suggest an algorithm for calculating these estimates.

neuroscience↗

bioRxiv2019

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

AO_SCPLOWBSTRACTC_SCPLOWThe vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck. Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks. This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli. These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system.

neuroscience↗

bioRxiv2018

The emergence of multiple retinal cell types through efficient coding of natural movies

One of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells.

neuroscience↗