Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
1994 Special Issue: A model of hippocampal function
Neural Networks - Special issue: models of neurodynamics and behavior
A learning rule for dynamic recruitment and decorrelation
Neural Networks
An Behavior-based Robotics
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
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Movement Generation with Circuits of Spiking Neurons
Neural Computation
Local and Global Gating of Synaptic Plasticity
Neural Computation
Evolution of spiking neural circuits in autonomous mobile robots: Research Articles
International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
Imitation learning with spiking neural networks and real-world devices
Engineering Applications of Artificial Intelligence
Movement prediction from real-world images using a liquid state machine
Applied Intelligence
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
Emergence of perceptual states in nonlinear lattices: a new computational model for perception
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimization methods for spiking neurons and networks
IEEE Transactions on Neural Networks
Bio-inspired navigation of mobile robots
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Spike-timing-dependent construction
Neural Computation
| Hi-index | 0.00 |
In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.