Stimulus-induced sequential activity in supervisely trained recurrent networks of firing rate neurons

Abstract

In this work, we consider recurrent neural networks of firing rate neurons supervisely trained to generate multidimensional sequences of given configurations. We study dynamical objects in the network multidimensional phase space underlying successfully trained outputs and analyze spatiotemporal neural activity and its features in three cases. First, we consider autonomous generation of complex sequences by output units driven by a recurrent network. Second, we study how input pulses can trigger different output units. Third, we explore the case where input pulses allow us to switch between different sequential activities of output units.