Statistical mechanics for networks of real neurons

Leenoy Meshulam; William Bialek

Statistical mechanics for networks of real neurons

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arxiv 2409.00412 v1 pith:7ZY3FGN2 submitted 2024-08-31 cond-mat.dis-nn q-bio.NC

Leenoy Meshulam , William Bialek This is my paper · ORCID

classification cond-mat.dis-nnq-bio.NC

keywords neuronsactivitybrainexperimentmechanicsnetworksrealstatistical

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Perceptions and actions, thoughts and memories result from coordinated activity in hundreds or even thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics description for these and other emergent phenomena of life. These aspirations appear in a new light because of developments in our ability to measure the electrical activity of the brain, sampling thousands of individual neurons simultaneously over hours or days. We review the progress that has been made in bringing theory and experiment together, focusing on maximum entropy methods and a phenomenological renormalization group. These approaches have uncovered new, quantitatively reproducible collective behaviors in networks of real neurons, and provide examples of rich parameter--free predictions that agree in detail with experiment.

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Direct estimates of irreversibility from time series
cond-mat.stat-mech 2024-12 conditional novelty 5.0 of 10

Direct estimates of trajectory irreversibility can be made by extrapolating plug-in D_KL estimates to infinite sample size, and single retinal neurons show nonzero irreversibility over 200 ms windows.

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