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Curr Opin Neurobiol. 2017 Oct;46:31-38. doi: 10.1016/j.conb.2017.07.003. Epub 2017 Jul 27.

Once upon a (slow) time in the land of recurrent neuronal networks….

Author information

1
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
2
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA. Electronic address: bdoiron@pitt.edu.

Abstract

The brain must both react quickly to new inputs as well as store a memory of past activity. This requires biology that operates over a vast range of time scales. Fast time scales are determined by the kinetics of synaptic conductances and ionic channels; however, the mechanics of slow time scales are more complicated. In this opinion article we review two distinct network-based mechanisms that impart slow time scales in recurrently coupled neuronal networks. The first is in strongly coupled networks where the time scale of the internally generated fluctuations diverges at the transition between stable and chaotic firing rate activity. The second is in networks with finitely many members where noise-induced transitions between metastable states appear as a slow time scale in the ongoing network firing activity. We discuss these mechanisms with an emphasis on their similarities and differences.

PMID:
28756341
DOI:
10.1016/j.conb.2017.07.003
[Indexed for MEDLINE]

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