Nonequilibrium dynamics in neural networks consisting of excitatory and inhibitory populations

The dynamical behaviour of neural networks consisting of excitatory and inhibitory neuron population, influence of noise on their function and dynamics, and stochastic modelling of memory search are considered. The memory search based on the input-output interrelations in a network, is determined by the transitions between different stable states of a system. The transition probabilities between the neural network attractors are calculated using the algorithm based on the path integral method. In this work we study the processes of self-organization in neural networks which appear both in spatially homogeneous and heterogeneous structures of synaptic connections between neurons. Such self-organization arises due to the spatial and temporal character of nonlinear interaction between elements. The proposed scheme of network structural organization can provide generation of stable rhythmic activity