Extended Mean-Field Theories for Networks of Real Neurons
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.
Fields of Study & Subjects
Neural dynamics and brain function
stochastic dynamics and bifurcation
Neural Networks and Applications
Publication details
- Journal / source
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Volume
- 137
- Issue
- 1
- Pages
- 018401–018401
- Publication date
- 12 May 2026
- Language
- EN