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Nat Neurosci. 2017 Jan;20(1):107-114. doi: 10.1038/nn.4433. Epub 2016 Oct 31.

The spatial structure of correlated neuronal variability.

Author information

1
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
2
Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA.
3
Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
4
Fox Center for Vision Restoration, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
5
Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.
6
Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York, USA.
7
Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Yeshiva University, Bronx, New York, USA.
8
Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Abstract

Shared neural variability is ubiquitous in cortical populations. While this variability is presumed to arise from overlapping synaptic input, its precise relationship to local circuit architecture remains unclear. We combine computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits. Extending the theory of networks with balanced excitation and inhibition, we find that spatially localized lateral projections promote weakly correlated spiking, but broader lateral projections produce a distinctive spatial correlation structure: nearby neuron pairs are positively correlated, pairs at intermediate distances are negatively correlated and distant pairs are weakly correlated. This non-monotonic dependence of correlation on distance is revealed in a new analysis of recordings from superficial layers of macaque primary visual cortex. Our findings show that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.

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PMID:
27798630
PMCID:
PMC5191923
DOI:
10.1038/nn.4433
[Indexed for MEDLINE]
Free PMC Article

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