A systems identification approach to estimating the connectivity in a neuronal population model.

Abstract

Mapping the brain and its complex networked structure has been one of the most researched topics in the last decade and continues to be the path towards understanding brain diseases. In this paper we present a new approach to estimating the connectivity between neurons in a network model. We use systems identification techniques for nonlinear dynamic models to compute the synaptic connections from other pre-synaptic neurons in the population. We are able to show accurate estimation even in the presence of model error and inaccurate assumption of post-synaptic potential dynamics. This allows to compute the connectivity matrix of the network using a very small time window of membrane potential data of the individual neurons. The specificity and sensitivity measures for randomly generated networks are reported.