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arXiv · 1812.11904

Coarse--graining and hints of scaling in a population of 1000+ neurons

Leenoy Meshulam·Jeffrey L. Gauthier·Carlos D. Brody·David W. Tank·William Bialek
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Abstract

In many systems we can describe emergent macroscopic behaviors, quantitatively, using models that are much simpler than the underlying microscopic interactions; we understand the success of this simplification through the renormalization group. Could similar simplifications succeed in complex biological systems? We develop explicit coarse-graining procedures that we apply to experimental data on the electrical activity in large populations of neurons in the mouse hippocampus. Probability distributions of coarse-grained variables seem to approach a fixed non-Gaussian form, and we see evidence of power-law dependencies in both static and dynamic quantities as we vary the coarse-graining scale over two decades. Taken together, these results suggest that the collective behavior of the network is described by a non-trivial fixed point.

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Leenoy Meshulam, Jeffrey L. Gauthier, Carlos D. Brody, David W. Tank, William Bialek. 2018-12-31. Coarse--graining and hints of scaling in a population of 1000+ neurons. https://arxiv.org/abs/1812.11904

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