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INFERRING AUDITORY NEURONAL RESPONSE PROPERTIES
 

Summary: INFERRING AUDITORY NEURONAL
RESPONSE PROPERTIES
FROM NETWORK MODELS
Sven E. Anderson
Peter L. Rauske
Daniel Margoliash
sven@data.uchicago.edu
pete@data.uchicago.edu
dan@data.uchicago.edu
Department of Organismal Biology and Anatomy
University of Chicago, Chicago, IL 60637, U.S.A.
Abstract
Relating cell response to stimulus parameters is an important analytic method
by which neural systems are understood. We inferred neurally encoded stim-
ulus parameters by training artificial neural networks to predict single cell re-
sponse to auditory stimuli. A relatively simple time-delay architecture modeled
each cell. For three cells, models successfully predict response to complex song
stimuli based on optimization using much simpler artificial stimuli. For these
models, average error is less than 40% of the cell's response variance. Model pa-
rameters are directly comparable to stimulus parameters and thereby estimate

  

Source: Anderson, Sven - Computer Science Program, Bard College

 

Collections: Computer Technologies and Information Sciences