Structural Analysis on STDP Neural Networks Using Complex Network Theory

Authors:
Hideyuki Kato;Tohru Ikeguchi;Kazuyuki Aihara
Affiliations:
Graduate School of Science and Engineering, Saitama University, Saitama, Japan 338---8570;Graduate School of Science and Engineering, Saitama University, Saitama, Japan 338---8570 and Aihara Complexity Modelling Project, ERATO, JST, Tokyo, Japan 153---8505;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan and Aihara Complexity Modelling Project, ERATO, JST, Tokyo, Japan 153---8505
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

Citing 2
Cited 1

Relating STDP to BCM

Neural Computation
Simple model of spiking neurons

IEEE Transactions on Neural Networks

Emergence of highly nonrandom functional synaptic connectivity through STDP

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Synaptic plasticity is one of essential and central functions for the memory, the learning, and the development of the brains. Triggered by recent physiological experiments, the basic mechanisms of the spike-timing-dependent plasticity (STDP) have been widely analyzed in model studies. In this paper, we analyze complex structures in neural networks evolved by the STDP. In particular, we introduce the complex network theory to analyze spatiotemporal network structures constructed through the STDP. As a result, we show that nonrandom structures emerge in the neural network through the STDP.