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An analytically tractable model of neural population activity in the presence of common input explains higher-order correlations and entropy

2010/09/15 by Jakob H Macke, Macke, Jakob H, Manfred Opper +3
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Neurons and Cognition (q-bio.NC) #Statistics and Probability (physics.data-an) #cond-mat.dis-nn #physics.data-an #q-bio.NC

paper · pdf · doi:10.48550/arxiv.1009.2855

arxiv created 2010/09/17 · arxiv updated 2010/09/20

Abstract

Simultaneously recorded neurons exhibit correlations whose underlying causes are not known. Here, we use a population of threshold neurons receiving correlated inputs to model neural population recordings. We show analytically that small changes in second-order correlations can lead to large changes in higher correlations, and that these higher-order correlations have a strong impact on the entropy, sparsity and statistical heat capacity of the population. Remarkably, our findings for this simple model may explain a couple of surprising effects recently observed in neural population recordings.

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