A Neurally-Efficient Implementation of Sensory Population Decoding
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1
University of California, Sloan-Swartz Center for Theoretical Neurobiology, W. M. Keck Foundation Center for Integrative Neuroscience and Department of Physiology, United States
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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
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Received:
20 Sep 2010;
Published Online:
23 Sep 2010.
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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