Abstract:
In order to understand modifications in a neural network due to learning, it is paramount to develop an effective tool that is capable of mapping the circuit connectivity...Show MoreMetadata
Abstract:
In order to understand modifications in a neural network due to learning, it is paramount to develop an effective tool that is capable of mapping the circuit connectivity and its changes from large-scale recordings of neuronal activity patterns. In this paper, context tree maximizing (CTM) is used to estimate directed information (DI) to measure causal influences among neural spike trains. The method reliably identifies the circuit structures of realistic Hodgkin-Huxley simulated networks. It also produces a promising mapping of a small brain network.
Date of Conference: 06-09 November 2016
Date Added to IEEE Xplore: 06 March 2017
ISBN Information: