Self-sustained non-periodic activity in networks of spiking neurons: The contribution of local and long-range connections and dynamic synapses

Abstract

Cortical dynamics show self-sustained activity which is complex and non-periodic. Assemblies of neurons show transient coupling exhibiting both integration and segregation without entering a seizure state. Models to date have demonstrated these properties but have required external input to maintain activity. Here we propose a spiking network model that incorporates a novel combination of both local and long-range connectivity and dynamic synapses (which we call the LLDS network) and we present explorations of the network's micro and macro behaviour. At the micro level, the LLDS network exhibits self-sustained activity which is complex and non-periodic and shows transient coupling between assemblies in different network regions. At the macro level, the power spectrum of the derived EEG, calculated from the summed membrane potentials, shows a power-law-like distribution similar to that recorded from human EEG. We systematically explored parameter combinations to map the variety of behavioural regimes and found that network connectivity and synaptic mechanisms significantly impact the dynamics. The complex sustained behaviour occupies a transition region in parameter space between two types of non-complex activity state, a synchronised high firing rate regime, resembling seizure, for low connectivity, and repetitive activation of a single network assembly for high connectivity. Networks without synaptic dynamics show only transient complex behaviour. We conclude that local and long-range connectivity and short-term synaptic dynamics are together sufficient to support complex persistent activity. The ability to craft such persistent dynamics in a spiking network model creates new opportunities to study neural processing, learning, injury and disease in nervous systems.