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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
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Interdependent scaling exponents in the human brain

    cond-mat.dis-nn 2024-11 conditional novelty 6.0 of 10

    Three scaling exponents measured in human brain fMRI activity follow two linear relations, derived from a mean-field model, echoing scaling relations near critical points.

  2. 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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