Beyond mean field theory: statistical field theory for neural networks

被引：36

作者：

Buicel, Michael A. ^{[1
]}

Chow, Carson C. ^{[2
]}

机构：

[1] Univ Texas Austin, Ctr Learning & Memory, Austin, TX 78712 USA

[2] NIDDK, Lab Biol Modeling, NIH, Bethesda, MD USA

来源：

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT | 2013年

关键词：

finite-size scaling; dynamics (theory); neuronal networks (theory); Boltzmann equation; POPULATION-DENSITY APPROACH; ASYNCHRONOUS STATES; PATTERN-FORMATION; LOCKED STATE; DYNAMICS; NEURONS; CHAOS; REPRESENTATION; OSCILLATIONS; PROPAGATION;

D O I：

10.1088/1742-5468/2013/03/P03003

中图分类号：

O3 [力学];

学科分类号：

08 ; 0801 ;

摘要：

Mean field theories have been a stalwart for studying the dynamics of networks of coupled neurons. They are convenient because they are relatively simple and possible to analyze. However, classical mean field theory neglects the effects of fluctuations and correlations due to single neuron effects. Here, we consider various possible approaches for going beyond mean field theory and incorporating correlation effects. Statistical field theory methods, in particular the Doi-Peliti-Janssen formalism, are particularly useful in this regard.

引用

页数：21

共 50 条

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