Case study on a self-organizing spiking neural network for robot navigation.
This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions are experienced and consequently knowledge about its environment is stored in the connectivity. Also a novel feature of the proposed SNN architecture is that it uses working memory, where present and previous sensor states are stored. Results are presented for a wall following application.
School of Computing and Intelligent Systems, University of Ulster Derry, BT48 7JL, Northern Ireland. nichols-e1@email.ulster.ac.uk
Eric
E
Nichols
L J
LJ
McDaid
N H
NH
Siddique
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D012371:Robotics; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*