超越平均场理论：神经网络的统计场理论。
Beyond mean field theory: statistical field theory for neural networks.
作者信息
Buice Michael A, Chow Carson C
机构信息
Center for Learning and Memory, University of Texas at Austin, Austin, TX, USA.
Laboratory of Biological Modeling, NIDDK, NIH, Bethesda, MD, USA.
出版信息
J Stat Mech. 2013 Mar;2013:P03003. doi: 10.1088/1742-5468/2013/03/P03003.
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.
摘要
平均场理论一直是研究耦合神经元网络动力学的中流砥柱。它们很方便，因为相对简单且易于分析。然而，经典平均场理论忽略了单个神经元效应所导致的涨落和关联效应。在此，我们考虑超越平均场理论并纳入关联效应的各种可能方法。统计场论方法，特别是多伊 - 佩利蒂 - 扬森形式体系，在这方面特别有用。
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