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Nat Neurosci. 2016 Mar;19(3):383-93. doi: 10.1038/nn.4242.

The mechanics of state-dependent neural correlations.

Author information

1
Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
2
Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.
3
Center for Theoretical Neuroscience, Columbia University, New York, New York, USA.
4
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
5
Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA.
6
Allen Institute for Brain Science, Seattle, Washington, USA.
7
Department of Mathematics, University of Houston, Houston, Texas, USA.
8
Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA.

Abstract

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.

PMID:
26906505
PMCID:
PMC5477791
DOI:
10.1038/nn.4242
[Indexed for MEDLINE]
Free PMC Article

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