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Curr Opin Neurobiol. 2014 Apr;25:156-63. doi: 10.1016/j.conb.2014.01.008. Epub 2014 Feb 5.

Neural circuits as computational dynamical systems.

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1
Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, CA 94305, United States. Electronic address: sussillo@stanford.edu.

Abstract

Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.

PMID:
24509098
DOI:
10.1016/j.conb.2014.01.008
[Indexed for MEDLINE]
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