Diameter of the spike-flow graphs of geometrical neural networks

Authors:
Jaroslaw Piersa
Affiliations:
Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Torun, Poland
Venue:
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Year:
2011

Citing 3
Cited 1

Spontaneous scale-free structure of spike flow graphs in recurrent neural networks

Neural Networks
Spectra of the Spike Flow Graphs of Recurrent Neural Networks

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks

IEEE Transactions on Neural Networks

Spectra of the spike-flow graphs in geometrically embedded neural networks

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Average path length is recognised as one of the vital characteristics of random graphs and complex networks. Despite a rather sparse structure, some cases were reported to have a relatively short lengths between every pair of nodes, making the whole network available in just several hops. This small-worldliness was reported in metabolic, social or linguistic networks and recently in the Internet. In this paper we present results concerning path length distribution and the diameter of the spike-flow graph obtained from dynamics of geometrically embedded neural networks. Numerical results confirm both short diameter and average path length of resulting activity graph. In addition to numerical results, we also discuss means of running simulations in a concurrent environment.