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Functional Methods for Disordered Neural Networks by Schuecker, Jannis; Goedeke, Sven; Dahmen, David; Helias, Moritz is a scholarly article available to read on EtoBox.

What is Functional Methods for Disordered Neural Networks about?

Neural networks of the brain form one of the most complex systems we know. Many qualitative features of the emerging collective phenomena, such as correlated activity, stability, response to inputs, chaotic and regular behavior, can, however, be understood in simple models that are accessible to a treatment in statistical mechanics, or, more precisely, classical statistical field theory. This tutorial presents the fundamentals behind contemporary developments in the theory of neural networks of rate units that are based on methods from statistical mechanics of classical systems with a large number of interacting degrees of freedom. In particular we will focus on a relevant class of systems that have quenched (time independent) disorder. In neural networks, the main source of disorder arises from random synaptic couplings between neurons. These systems are in many respects similar to spin glasses. The tutorial therefore also explains the methods for these disordered systems as far as they are applied in neuroscience. The presentation consists of two parts. In the first part we introduce stochastic differential equations in the Martin - Siggia - Rose - De Dominicis - Janssen path int

Author

Schuecker, Jannis; Goedeke, Sven; Dahmen, David; Helias, Moritz

Published

2016

Language

EN