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Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84066

Macke,  JH
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Macke, J., Opper, M., & Bethge, M. (2011). Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity. Physical Review Letters, 106(20), 1-4. doi:10.1103/PhysRevLett.106.208102.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BBAA-0
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-order redundancies, and that the resulting interactions have a strong impact on the entropy, sparsity, and statistical heat capacity of the population. Our findings for this simple model may explain some surprising effects recently observed in neural population recordings.