Computational complexity reduction for functional connectivity estimation in large scale neural network

Abstract

Identification of functional connectivity between neurons is an important issue in computational neuroscience. Recently, the number of simultaneously recorded neurons is increasing, and computational complexity to estimate functional connectivity is exploding. In this study, we propose a two-stage algorithm to estimate spike response functions between neurons in a large scale network. We applied the proposed algorithm to various scales of neural networks and showed that the computational complexity is reduced without sacrificing estimation accuracy.