Summary
2007 International Symposium on Nonlinear Theory and its Applications
2007
Session Number:19AM2-B
Session:
Number:19AM2-B-1
New measures for estimating neural network structures only from multi-spike sequences
Tohru Ashizawa, Daisuke Haraki, Tohru Ikeguchi,
pp.417-420
Publication Date:2007/9/16
Online ISSN:2188-5079
DOI:10.34385/proc.41.19AM2-B-1
Summary:
In neural systems, a fundamental element of the systems, a neuron, interacts with other neurons, then they often produce very complicated behavior. To model, analyze, and predict such complicated behavior, it is important to understand interactions between neurons, namely, a neural network structure. In the present paper, to estimate such a neural network structure by using only observed multi-spike sequences, we propose two new measures, which are based on spike time metric and partialization analysis. To evaluate the validity of our proposed measures, we apply the proposed measures to multi-spike sequences which are produced by an electrotonic coupling of ?-models. As a result, the proposed measures can identify regular and random neural network structures in high performance.