Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Self-organized critical neural networks.

A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks,...

Phase Transitions of Neural Networks

The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: Perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayes...

Editorial: Artificial Neural Networks as Models of Neural Information Processing

In artificial intelligence (AI), new advances make it possible that artificial neural networks (ANNs) learn to solve complex problems in a reasonable amount of time (LeCun et al., 2015). To the computational neuroscientist, ANNs are theoretical vehicles that aid in the understanding of neural information processing (van Gerven). These networks can take the form of the rate-based models that are...

Self-organized criticality in adaptive neural networks

PHASE TRANSITIONS IN NEURAL NETWORKS Joshua

Various simulat.ions of cort.ical subnetworks have evidenced something like phase transitions with respect to key parameters. We demonstrate that. such transi t.ions must. indeed exist. in analogous infinite array models. For related finite array models classical phase transi t .ions (which describe steady-state behavior) may not. exist., but. there can be distinct. quali tative changes in ("me...