Sparseness is not actively optimized in V1

Abstract

Event Abstract Back to Event Sparseness is not actively optimized in V1 Pietro Berkes1*, Benjamin L. White1 and Jozsef Fiser1 1 Brandeis University, United States Sparse coding is a powerful idea in computational neuroscience referring to the general principle that the cortex exploits the benefits of representing every stimulus by a small subset of neurons. Advantages of sparse coding include reduced dependencies, improved detection of co-activation of neurons, and an efficient encoding of visual information. Computational models based on this principle have reproduced the main characteristics of simple cell receptive fields in the primary visual cortex (V1) when applied to natural images. However, direct tests on neural data of whether sparse coding is an optimization principle actively implemented in the brain have been inconclusive so far. Although a number of electrophysiological studies have reported high levels of sparseness in V1, these measurements were made in absolute terms and thus it is an open question whether the observed high sparseness indicates optimality or simply high stimulus selectivity. Moreover, most of the recordings have been performed in anesthetized animals, but it is not clear how these results generalize to the cell responses in the awake condition. To address this issue, we have focused on relative changes in sparseness. We analyzed neural data from ferret and rat V1 to verify two basic predictions of sparse coding: 1) Over learning, neural responses should become increasingly sparse, as the visual system adapts to the statistics of the environment. 2) An optimal sparse representation requires active competition between neurons involving recurrent connections. Thus, as animals go from awake state to deep anesthesia, which is known to eliminate recurrent and top-down inputs, neural responses should become less sparse, since the neural interactions that support active sparsification of responses are disrupted. To test the first prediction empirically, we measured the sparseness of neural responses in awake ferret V1 to natural movies at various stages of development, from eye opening to adulthood. Contrary to the prediction of sparse coding, we found that the neural code does adapt to represent natural stimuli over development, but sparseness steadily decreases with age. In addition, we observed a general increase in dependencies among neural responses. We addressed the second prediction by analyzing neural responses to natural movies in rats that were either awake or under different levels of anesthesia ranging from light to very deep. Again, contrary to the prediction, sparseness of cortical cells increased with increasing levels of anesthesia. We controlled for reduced responsiveness of the direct feedforward connections under anesthesia, by using appropriate sparseness measures and by quantifying the signal-to-noise ratio across levels of anesthesia, which did not change significantly. These findings suggest that the representation in V1 is not actively optimized to maximize the sparseness of neural responses. A viable alternative is that the concept of efficient coding is implemented in the form of optimal statistical learning of parameters in an internal model of the environment. This work has been supported by the Swartz Foundation and the Swiss National Science Foundation. 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: Berkes P, White BL and Fiser J (2010). Sparseness is not actively optimized in V1. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00310 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: 08 Mar 2010; Published Online: 08 Mar 2010. * Correspondence: Pietro Berkes, Brandeis University, Waltham, United States, berkes@brandeis.edu 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 Pietro Berkes Benjamin L White Jozsef Fiser Google Pietro Berkes Benjamin L White Jozsef Fiser Google Scholar Pietro Berkes Benjamin L White Jozsef Fiser PubMed Pietro Berkes Benjamin L White Jozsef Fiser 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.