Integrating high-level sensor features via STDP for bio-inspired navigation

Correlation based algorithms have been found to explain many basic behaviors in simple animals. In this paper the authors investigate the problem of navigation control of a robot from the viewpoint of bio-inspired perception. In this paper the authors study how to go up, through learning, from the implementation of a reactive system, towards behaviors of increasing complexity. The whole control system is based on networks of spiking neurons. A correlation based rule, namely the spike timing dependent plasticity (STDP), is implemented for an efficient learning. The main interesting consequence is that the system is able to learn high-level sensor features, based on a set of basic reflexes, depending on some low-level sensor inputs. The whole methodology is presented through simulation results and also through its implementation on an FPGA based system for real time working on a roving robot.