Distributed deterministic temporal information propagated by feedforward neural networks

Abstract

A ten layers feedforward network characterized by diverging/converging patterns of projection between successive layers is activated by an external spatio-temporal input pattern fed to layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and two types of neuron models for the network, i.e. either a simple spiking neuron (SSN) or a multiple-timescale adaptive threshold neuron (MAT). We observed an unimodal integration effect as a function of the order of the layers and confirmed that the MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input. © 2011 Springer-Verlag.