Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks

Authors:
André Cyr;Mounir Boukadoum
Affiliations:
Computer Science Department, Université du Québec à Montréal, Montréal, Québec, Canada;Computer Science Department, Université du Québec à Montréal, Montréal, Québec, Canada
Venue:
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Year:
2012

Citing 12
Cited 1

Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
A spiking neuron model: applications and learning

Neural Networks
The evidence for neural information processing with precise spike-times: A survey

Natural Computing: an international journal
Modeling Synaptic Plasticity in Conjunction with the Timing of Pre- and Postsynaptic Action Potentials

Neural Computation
Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity

Neural Computation
A behavior controller based on spiking neural networks for mobile robots

Neurocomputing
A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
A Hierarchical Autonomous Robot Controller for Learning and Memory: Adaptation in a Dynamic Environment

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
AI-SIMCOG: a simulator for spiking neurons and multiple animats’ behaviours

Neural Computing and Applications
Synaptic plasticity in spiking neural networks (SP2INN): a system approach

IEEE Transactions on Neural Networks
Simple model of spiking neurons

IEEE Transactions on Neural Networks

Mobile robots' modular navigation controller using spiking neural networks

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work investigates adaptive behaviours for an intelligent robotic agent when subjected to temporal stimuli consisting of associations of contextual cues and simple reflexes. This is made possible thanks to a novel learning rule based on spike-timing-dependent plasticity and embedded in an artificial spiking neural network serving as a brain-like controller. The subsequent bio-inspired cognitive system carries out different classical conditioning tasks in a controlled virtual 3D-world while the timing and frequency of unconditioned and conditioned parameters are varied. The results of this simulated robotic environment are analysed at different stages from stimuli capture to neural spike generation and show extended behavioural capabilities by the robot in the temporal domain.