Literature DB >> 31702278

Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons.

Leenoy Meshulam1,2,3, Jeffrey L Gauthier1, Carlos D Brody1,4,5, David W Tank1,2,4, William Bialek2,3,6.   

Abstract

We develop a phenomenological coarse-graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse-grained variables seem to approach a fixed non-Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a nontrivial fixed point.

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Year:  2019        PMID: 31702278      PMCID: PMC7335427          DOI: 10.1103/PhysRevLett.123.178103

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  24 in total

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5.  Optimal Encoding in Stochastic Latent-Variable Models.

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6.  Neural Systems Under Change of Scale.

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8.  The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics.

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9.  Bridging Single Neuron Dynamics to Global Brain States.

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10.  Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters.

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