Spike-Timing dependent plasticity learning for visual-based obstacles avoidance

Authors:
Hédi Soula;Guillaume Beslon
Affiliations:
LBM/NIDDK, National Institutes of Health, Bethesda, MD;PRISMA, National Institute of Applied Sciences, Villeurbanne Cedex, France
Venue:
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Year:
2006

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Abstract

In this paper, we train a robot to learn online a task of obstacles avoidance The robot has at its disposal only its visual input from a linear camera in an arena whose walls are composed of random black and white stripes The robot is controlled by a recurrent spiking neural network (integrate and fire) The learning rule is the spike-time dependent plasticity (STDP) and its counterpart – the so-called anti-STDP Since the task itself requires some temporal integration, the neural substrate is the network's own dynamics The behaviors of avoidance we obtain are homogenous and elegant In addition, we observe the emergence of a neural selectivity to the distance after the learning process.