From fixed points to chaos: three models of delayed discrimination.

Prog Neurobiol

Center for Theoretical Neuroscience, Columbia University, New York, NY 10032, USA.

Published: April 2013

AI Article Synopsis

  • Working memory plays a key role in cognitive tasks, but the exact neuronal mechanisms are not fully understood, with existing models varying widely in their assumptions about neural connectivity and representations.
  • Recent research indicates that while some prefrontal cortex neurons are consistently tuned to the frequency of remembered stimuli, many display complex and time-varying relationships with the stimuli.
  • Experiments comparing different models suggest that initially random neural networks can perform well on working memory tasks, but a partially trained model provides a better fit for the data, indicating that prefrontal networks may start off random and require training to become more organized and effective.

Article Abstract

Working memory is a crucial component of most cognitive tasks. Its neuronal mechanisms are still unclear despite intensive experimental and theoretical explorations. Most theoretical models of working memory assume both time-invariant neural representations and precise connectivity schemes based on the tuning properties of network neurons. A different, more recent class of models assumes randomly connected neurons that have no tuning to any particular task, and bases task performance purely on adjustment of network readout. Intermediate between these schemes are networks that start out random but are trained by a learning scheme. Experimental studies of a delayed vibrotactile discrimination task indicate that some of the neurons in prefrontal cortex are persistently tuned to the frequency of a remembered stimulus, but the majority exhibit more complex relationships to the stimulus that vary considerably across time. We compare three models, ranging from a highly organized line attractor model to a randomly connected network with chaotic activity, with data recorded during this task. The random network does a surprisingly good job of both performing the task and matching certain aspects of the data. The intermediate model, in which an initially random network is partially trained to perform the working memory task by tuning its recurrent and readout connections, provides a better description, although none of the models matches all features of the data. Our results suggest that prefrontal networks may begin in a random state relative to the task and initially rely on modified readout for task performance. With further training, however, more tuned neurons with less time-varying responses should emerge as the networks become more structured.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622800PMC
http://dx.doi.org/10.1016/j.pneurobio.2013.02.002DOI Listing

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