title: Mutual information in a dilute, asymmetric neural network model.
abstract: Neural networks with asymmetric synaptic connections (w(ij) not equal to w(ji)) display a broad range of dynamical behavior including fixed point, periodic, and "chaotic" trajectories. Previous work has shown that such networks undergo an order-chaos phase transition as various network parameters, such as the connectivity or the degree of asymmetry, are changed. Here, using an information theoretic approach, we present results which suggest that neurons are able to communicate information to each other most effectively in networks that are near the order-chaos transition. We then extend the model to incorporate some biologically relevant features.
authors: Department of Physics, University of California at Berkeley, Berkeley, CA 94720, USA.
authors: E
authors: E
authors: Greenfield
authors: H
authors: H
authors: Lecar
mesh_terms: D000818:Animals; D001703:Biophysics / Q000379:methods*; D015233:Models, Statistical; D009415:Nerve Net; D017711:Nonlinear Dynamics; D013569:Synapses; D013997:Time Factors