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.

[1]  D. Whitteridge Lectures on Conditioned Reflexes , 1942, Nature.

[2]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[3]  Barbara Webb,et al.  A simple latency-dependent spiking-neuron model of cricket phonotaxis , 2000, Biological Cybernetics.

[4]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[5]  Jon Rigelsford,et al.  Behaviour‐based Robotics , 2001 .

[6]  P. Arena,et al.  Weak Chaos Control for Action-Oriented Perception: Real Time Implementation via FPGA , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[7]  Luigi Fortuna,et al.  A new simulation tool for action-oriented perception systems , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[8]  Paul F. M. J. Verschure,et al.  Environmentally mediated synergy between perception and behaviour in mobile robots , 2003, Nature.

[9]  Luigi Fortuna,et al.  Turing Patterns in RD-CNNS for the Emergence of Perceptual States in Roving Robots , 2007, Int. J. Bifurc. Chaos.

[10]  Florentin Wörgötter,et al.  Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms , 2005, Neural Computation.

[11]  Stephen Grossberg,et al.  Introduction: Spiking Neurons in Neuroscience and Technology , 2001, Neural Networks.

[12]  Jean-Arcady Meyer,et al.  BIOLOGICALLY BASED ARTIFICIAL NAVIGATION SYSTEMS: REVIEW AND PROSPECTS , 1997, Progress in Neurobiology.

[13]  Jan Wessnitzer,et al.  Multimodal sensory integration in insects—towards insect brain control architectures , 2006, Bioinspiration & biomimetics.

[14]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.