Beyond mean field theory: statistical field theory for neural networks
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Publication:3301539
DOI10.1088/1742-5468/2013/03/P03003zbMath1456.82408WikidataQ34212976 ScholiaQ34212976MaRDI QIDQ3301539
Carson C. Chow, Michael A. Buice
Publication date: 11 August 2020
Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)
Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20) Statistical thermodynamics (82B30) Applications of continuous-time Markov processes on discrete state spaces (60J28)
Related Items (7)
Misconceptions on effective field theories and spontaneous symmetry breaking: response to Ellis' article ⋮ Path integral methods for stochastic differential equations ⋮ Path-integral methods for analyzing the effects of fluctuations in stochastic hybrid neural networks ⋮ Numerical computation of the modified plasma dispersion function with curvature ⋮ Stability and instability of a neuron network with excitatory and inhibitory small-world connections ⋮ \(p\)-adic statistical field theory and deep belief networks ⋮ Large deviations for randomly connected neural networks: I. Spatially extended systems
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