Functional Networks Based on Pairwise Spike Synchrony Can Capture Topologies of Synaptic Connectivity in a Local Cortical Network Model

Abstract

In order to develop a method to infer the underlying synaptic connectivity only from spatio-temporal patterns of spiking activity observed in a neuronal network, we here investigated characteristics of a network of functional connections defined by pairwise spike synchrony. We first conducted numerical simulations of a computational model of a local cortical network and constructed a functional network based on the obtained spike data. The proposed analysis with the optimal parameters defining functional connections showed that characteristics of connectivity of functional networks are in good agreement with those of synaptic connectivity in the computational model in terms of statistical indices such as the clustering coefficient and the shortest path length. The result suggests that it is possible to extract at least statistical characteristics of synaptic connectivity from spatio-temporal patterns of spiking activity.KeywordsSpike ActivityFunctional NetworkFunctional ConnectionShort Path LengthCoherence MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.