Tracking Plasticity in Probabilistic Spike Trains Models of Synaptically-Coupled Neural Population

Abstract

The problem of identifying plasticity in a recorded neural population has long been the subject of intense research. With the ability to simultaneously record large ensembles of single unit activity over extended periods of time, it is becoming central to the ability to efficiently decode neuronal responses. In a previous study, we demonstrated that a graph theoretic approach can identify functional interdependency between neurons responding to a common input over multiple time scales. In this paper, we investigate the performance of the technique when both functional and structural plasticity arise post stimulus presentation. Three types of interactions between neurons are considered; auto-inhibition, cross-inhibition, and excitation. We report the clustering performance of the approach applied to three distinct probabilistic models of networks with different topologies