Abstract
We here introduce a novel biologically inspired adaptive controller for autonomous robot. The proposed controller binds N number of Aplysia-like spiking neural network each of which could interact with particular sensory information and produce various motors output. The post-synaptic weights in each model are gradually updated by the property of spike timing-dependent plasticity (STDP) and that of the presynaptic modulation signal (synapse-on-synapse contact) from the sensory neurons. Information from different types of sensors is bound at the motor neurons. Experimental results show that a physical robot Khepera with the proposed controller quickly adapted into an open environment by evolving obstacle avoidance behavior while tracking a target object. We believe that this approach could be applicable to an autonomous robot with various sensory and motor modalities.
