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A Neurally-Efficient Implementation of Sensory Population Decoding

2010; Frontiers Media; Volume: 4; Linguagem: Inglês

10.3389/conf.fncom.2010.51.00023

1662-5188

Lisberger Steven,

Neural dynamics and brain function

Event Abstract Back to Event A Neurally-Efficient Implementation of Sensory Population Decoding Kris S. Chaisanguanthum1* and Steven G. Lisberger1, 2 1 University of California, Sloan-Swartz Center for Theoretical Neurobiology, W. M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, United States 2 Howard Hughes Medical Institute, HHMI Investigator, United States We are interested in decoding the response of a population of cells to some stimulus variable, for example, the response of motion-sensitive cells in primate visual area MT to a coherently moving target, and, specifically, how such computations are implemented biologically. The initiation of smooth pursuit requires an estimate of the target velocity from the MT population response in roughly 100 milliseconds. We suggest a sampling-based approach in which the aggregate population response is approximated via supralinear spike integration, which provides a gain-independent estimate without an implementation of divisive normalization. We verify with a model population, which replicates key features found in neural data, that this yields an estimate of target motion of comparable quality to traditional center-of-mass (“vector averaging”) calculations. We study the correlation between single neuron activity variation and the output of various decoding models as a function of the neuron’s turning; these curves may be experimental signatures of specific population decoding algorithms performed in the brain. Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Poster Abstract Topic: Bernstein Conference on Computational Neuroscience Citation: Chaisanguanthum KS and Lisberger SG (2010). A Neurally-Efficient Implementation of Sensory Population Decoding. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00023 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: 20 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Kris S Chaisanguanthum, University of California, Sloan-Swartz Center for Theoretical Neurobiology, W. M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, San Francisco, United States, chaisang@phy.ucsf.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 Kris S Chaisanguanthum Steven G Lisberger Google Kris S Chaisanguanthum Steven G Lisberger Google Scholar Kris S Chaisanguanthum Steven G Lisberger PubMed Kris S Chaisanguanthum Steven G Lisberger 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.