Identifying the pulsed neuron networks' structures by a nonlinear Granger causality method.

ABSTRACT:

Background

It is a crucial task of brain science researches to explore functional connective maps of Biological Neural Networks (BNN). The maps help to deeply study the dominant relationship between the structures of the BNNs and their network functions.

Results

In this study, the ideas of linear Granger causality modeling and causality identification are extended to those of nonlinear Granger causality modeling and network structure identification. We employed Radial Basis Functions to fit the nonlinear multivariate dynamical responses of BNNs with neuronal pulse firing. By introducing the contributions from presynaptic neurons and detecting whether the predictions for postsynaptic neurons' pulse firing signals are improved or not, we can reveal the information flows distri

SUBMITTER: Zhu MJ 

PROVIDER: S-EPMC7017568 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature