Self-organization of spiking neural network that generates autonomous behavior in a real mobile robot

Fady Alnajjar, Kazuyuki Murase

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


In this paper, we propose self-organization algorithm of spiking neural network (SNN) applicable to autonomous robot for generation of adoptive and goal-directed behavior. First, we formulated a SNN model whose inputs and outputs were analog and the hidden unites are interconnected each other. Next, we implemented it into a miniature mobile robot Khepera. In order to see whether or not a solution(s) for the given task(s) exists with the SNN, the robot was evolved with the genetic algorithm in the environment. The robot acquired the obstacle avoidance and navigation task successfully, exhibiting the presence of the solution. After that, a self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot. In the environment, the robot incrementally organized the network and the given tasks were successfully performed. The time needed to acquire the desired adoptive and goal-directed behavior using the proposed self-organization method was much less than that with the genetic evolution, approximately one fifth.

Original languageEnglish
Pages (from-to)229-239
Number of pages11
JournalInternational Journal of Neural Systems
Issue number4
Publication statusPublished - Aug 2006
Externally publishedYes


  • Hebbian rule, use-dependent synaptic modification
  • Spike response model
  • Spiking neural network

ASJC Scopus subject areas

  • Computer Networks and Communications


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