Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:A4L-A

Session:

Number:142

Detection of learning in neural networks only from spike sequences

Kaori Kuroda,  Kantaro Fujiwara,  Tohru Ikeguchi,  

pp.142-145

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.142

PDF download (280KB)

Summary:
In this paper, we proposed a new method for estimating evolution of neural network structures only from multi-spike sequences. In the proposed method, we used a spike time metric which quantifies distance between two spike sequences and applied partialization analysis to the spike time metric. To check the validity of the proposed method, we conducted numerical experiments by using an evolving neural network model with spike-timing-dependent plasticity learning. As a result, we could detect existence of learning in the neural network and estimate how the neural network structure evolves.

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