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Extended Mean-Field Theories for Networks of Real Neurons
Phys. Rev. Lett. 137, 018401 – Published 30 June, 2026
DOI: https://doi.org/10.1103/nr71-phqw
Abstract
If the behavior of a system with many degrees of freedom can be captured by a small number of collective variables, then plausibly there is an underlying mean-field theory. We show that simple versions of this idea fail to describe the patterns of activity in networks of real neurons. An extended mean-field theory that matches the distribution of collective variables is at least consistent, though shows signs that these networks are poised near a critical point, in agreement with other observations. These results suggest a path to analysis of emerging data on ever larger numbers of neurons.
Physics Subject Headings (PhySH)
synopsis
Rethinking Mean-Field Theory for Neural Networks
An extended version of mean-field theory accurately captures activity patterns seen in networks of biological neurons.
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