Work

Beyond mean field theory: statistical field theory for neural networks

Year: 2013

Type: article

Abstract: 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.

Source: Journal of Statistical Mechanics Theory and Experiment

DOI: 10.1088/1742-5468/2013/03/p03003

Authors: Michael A Buice, Carson C Chow

Institutions: The University of Texas at Austin, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: bronze

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Award: Analysis of cortical function