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September 29, 2025Open Access

The Kinetic Limit of Balanced Neural Networks

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The study derives kinetic equations that effectively describe population density in balanced neural networks.
In the large n limit, fluctuations evolve according to a Fokker-Planck equation, highlighting stochastic behavior.
The method involves decomposing neuron states into average activity and fluctuations, facilitating insights into dynamics.
Assuming linear dynamics results in a spatially-distributed neural field equation, expanding the theoretical framework of neural networks.

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This investigation uncovers kinetic equations for population density in balanced neural networks, suggesting novel insights into neural activity.

MacLaurin et al. (2025) studied this question.

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This investigation uncovers kinetic equations for population density in balanced neural networks, suggesting novel insights into neural activity.