Interaction between Feed-forward and Recurrent Random Networks

Cortical dynamics is frequently modeled on the basis of two extreme network architectures: recurrent random networks or feed-forward networks. The first exhibit a variety of states which resemble cortical in-vivo activity in a statistical sense. The latter highlight the potential functional relevance of the precise timing of individual spikes. Here, we show that under realistic conditions both scenarios are compatible and can be incorporated into a single network model.