Summary
International Symposium on Nonlinear Theory and its Applications
2005
Session Number:2-1-3
Session:
Number:2-1-3-3
Mean Field Theory for Random Recurrent Spiking Neural Networks
Bruno Cessac, Olivier Mazet, Manuel Samuelides, Hedi Soula,
pp.594-597
Publication Date:2005/10/18
Online ISSN:2188-5079
Summary:
Recurrent spiking neural networks can provide biologically inspired model of robot controller. We study here the dynamics of large size randomly connected networks thanks to "mean field theory". Mean field theory allows to compute their dynamics under the assumption that the dynamics of individual neuronsare stochastically independent. We restrict ourselves to the simple case of homogeneous centered gaussian independent synaptic weights. First a theoretical study allows to derive the mean-field dynamics using a large deviation approach. This dynamics is characterized in function of an order parameter which is the normalized variance of the coupling. Then various applications are reviewed which show the applicative potentiality of the approach.