Structural Relationships between Spiking Neural Networks and Functional Samples

Abstract

Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface multichannel recordings is able to reproduce the main connectivity features of the underlying spatial neural network. In this work we address this problem through computational modeling using the integrate-and-fire spiking neuron model, which enabled us to relate neural connectivity and the respective mesoscopic dynamics. Functional samples were then compared to an idealized spatial neural network model in terms of established topological network measurements. Results show that some measurements (e.g., betweenness centrality) are able to fairly approximate functional and spatial networks. Therefore, under specific circumstances of sampling size and simulation approach, it is possible to say that functional networks are able to reproduce connectivity features of the underlying neural network.