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Covariance-based synaptic plasticity in an attractor network model accounts for fast adaptation in free operant learning.
MedLine Citation:
PMID:  23345226     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
In free operant experiments, subjects alternate at will between targets that yield rewards stochastically. Behavior in these experiments is typically characterized by (1) an exponential distribution of stay durations, (2) matching of the relative time spent at a target to its relative share of the total number of rewards, and (3) adaptation after a change in the reward rates that can be very fast. The neural mechanism underlying these regularities is largely unknown. Moreover, current decision-making neural network models typically aim at explaining behavior in discrete-time experiments in which a single decision is made once in every trial, making these models hard to extend to the more natural case of free operant decisions. Here we show that a model based on attractor dynamics, in which transitions are induced by noise and preference is formed via covariance-based synaptic plasticity, can account for the characteristics of behavior in free operant experiments. We compare a specific instance of such a model, in which two recurrently excited populations of neurons compete for higher activity, to the behavior of rats responding on two levers for rewarding brain stimulation on a concurrent variable interval reward schedule (Gallistel et al., 2001). We show that the model is consistent with the rats' behavior, and in particular, with the observed fast adaptation to matching behavior. Further, we show that the neural model can be reduced to a behavioral model, and we use this model to deduce a novel "conservation law," which is consistent with the behavior of the rats.
Authors:
Tal Neiman; Yonatan Loewenstein
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of neuroscience : the official journal of the Society for Neuroscience     Volume:  33     ISSN:  1529-2401     ISO Abbreviation:  J. Neurosci.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8102140     Medline TA:  J Neurosci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1521-34     Citation Subset:  IM    
Affiliation:
Department of Neurobiology, Alexander Silberman Institute of Life Sciences, Interdisciplinary Center for Neural Computation, Edmond and Lily Safra Center for Brain Sciences, and Center for the Study of Rationality, Hebrew University, Jerusalem 91904, Israel.
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