DocumentCode :
1527426
Title :
Solving graph algorithms with networks of spiking neurons
Author :
Sala, Dorel M. ; Cios, Krzysztof J.
Author_Institution :
Toledo Univ., OH, USA
Volume :
10
Issue :
4
fYear :
1999
fDate :
7/1/1999 12:00:00 AM
Firstpage :
953
Lastpage :
957
Abstract :
Spatio-temporal coding that combines spatial constraints with temporal sequencing is of great interest to brain-like circuit modelers. In this paper we present some new ideas of how these types of circuits can self-organize. We introduce a temporal correlation rule based on the time difference between the firing of neurons. With the aid of this rule we show an analogy between a graph and a network of spiking neurons. The shortest path, clustering based on the nearest neighbor, and the minimal spanning tree algorithms are solved using the proposed approach
Keywords :
graph theory; mathematics computing; neural nets; optimisation; pattern recognition; graph algorithms; shortest path; spanning tree; spatial constraints; spatiotemporal coding; spiking neurons; temporal correlation learning; temporal sequencing; Artificial neural networks; Biomembranes; Brain modeling; Circuits; Clustering algorithms; Equations; Nearest neighbor searches; Neurons; Shape; Tree graphs;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.774270
Filename :
774270
Link To Document :
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