A stimulus-dependent maximum entropy model of the retinal population neural code

Abstract

Event Abstract Back to Event A stimulus-dependent maximum entropy model of the retinal population neural code Einat Granot-Atedgi1, Gasper Tkacik2, Ronen Segev3 and Elad Schneidman1* 1 Weizmann Institute of Science, Department of Neurobiology, Israel 2 University of Pennsylvania, Department of Physics and Biocenter Oulu,, United States 3 Ben-Gurion University, Israel The nature of the code by which information is represented by the joint activity patterns of groups of neurons, depends on the stimulus-response properties of each of the single cells, and the dependencies among neurons. However, most models of neural encoding, describing the selectivity and stochastic nature of neural response to various stimuli, have focused on single neurons or small groups of cells, using simplified, artificial stimuli. Recent results, in several different neural systems, have used maximum entropy pairwise models to show that the typically weak pairwise correlations between neurons add up to dominate the population activity patterns. While reflecting the strong effect of pairwise correlations on the collective behavior of the population, these models have not addressed the stimulus dependent properties of neural population code. Here we aim at elucidating how large neural populations encode sensory information, and uncovering the functional role of the interactions between neurons. We present a novel stimulus-dependent maximum entropy model, which captures both the pairwise correlations between neurons and the stimulus-dependent firing rates of single cells. We apply this model to the recordings of large groups of retinal ganglion cells responding to artificial and naturalistic stimuli, and show that it significantly outperforms conditionally independent models. In particular, our model captures the time-dependent activity of single cells and the population activity patterns for both classes of stimuli, where the classical receptive-field models perform poorly. Finally, we find that the pairwise interaction map that underlies the population response is similar under different stimuli, suggesting that the functional interactions between cells could encode a prior over the binary words a neural population may emit. Our results provide a framework for combining single cell receptive field models with the maximum entropy description of network states. We show that this approach allows accurate modeling of large neural population responses to non-stationary and rich stimuli, and suggests a biologically plausible way for the neural systems to implement population decoders. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session III Citation: Granot-Atedgi E, Tkacik G, Segev R and Schneidman E (2010). A stimulus-dependent maximum entropy model of the retinal population neural code. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00254 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 Mar 2010; Published Online: 05 Mar 2010. * Correspondence: Elad Schneidman, Weizmann Institute of Science, Department of Neurobiology, Rehovot, Israel, elad.schneidman@weizmann.ac.il Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Einat Granot-Atedgi Gasper Tkacik Ronen Segev Elad Schneidman Google Einat Granot-Atedgi Gasper Tkacik Ronen Segev Elad Schneidman Google Scholar Einat Granot-Atedgi Gasper Tkacik Ronen Segev Elad Schneidman PubMed Einat Granot-Atedgi Gasper Tkacik Ronen Segev Elad Schneidman Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.