Effects of transmission delays and noise in recurrent excitatory neural networks

Abstract

Random perturbations, referred to as noise, are omnipresent in the nervous system. We investigate how noise modifies the dynamics of the neural networks according to the delay. In this report, we examine the effect of transmission delay on both the dynamics of a single neuron receiving a recurrent excitation and the dynamics of fully interconnected excitatory networks. In the case of the single neuron with a recurrent connection, depending on the value of the delay, the discharge pattern changes from regular to multiplets. More complicated patterns appears when noise is added, and depends on both the delay and the noise intensity, but classification can be described. In certain conditions, noise reduces the synchronization, whereas in others it increases the regularity of the network activity. Finally, the same network codes the input amplitude either using mean activity amplitude coding when short delays exist, and using frequency modulation when long delays exist.