Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Computing with spiking neurons
Pulsed neural networks
Unsupervised learning and self-organization in networks of spiking neurons
Self-Organizing neural networks
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Using Humanoid Robots to Study Human Behavior
IEEE Intelligent Systems
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Evolution of a circuit of spiking neurons for phototaxis in a Braitenberg vehicle
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
From Wheels to Wings with Evolutionary Spiking Circuits
Artificial Life
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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In this article, we describe an adaptive controller for an autonomous mobile robot with a simple structure. Sensorimotor connections were made using a three-layered spiking neural network (SNN) with only one hidden-layer neuron and synapses with spike timing-dependent plasticity (STDP). In the SNN controller, synapses from the hidden-layer neuron to the motor neurons received presynaptic modulation signals from sensory neurons, a mechanism similar to that of the withdrawal reflex circuit of the sea slug, Aplysia. The synaptic weights were modified dependent on the firing rates of the presynaptic modulation signal and that of the hidden-layer neuron by STDP. In experiments using a real robot, which uses a similar simple SNN controller, the robot adapted quickly to the given environment in a single trial by organizing the weights, acquired navigation and obstacle-avoidance behavior. In addition, it followed dynamical changes in the environment. This associative learning scheme can be a new strategy for constructing adaptive agents with minimal structures, and may be utilized as an essential mechanism of an SNN ensemble that binds multiple sensory inputs and generates multiple motor outputs.