DocumentCode :
1064587
Title :
Learning Anticipation via Spiking Networks: Application to Navigation Control
Author :
Arena, Paolo ; Fortuna, Luigi ; Frasca, Mattia ; Patané, Luca
Author_Institution :
Dipt. di Ing. Elettr. Elettron. e dei Sist., Univ. degli Studi di Catania, Catania
Volume :
20
Issue :
2
fYear :
2009
Firstpage :
202
Lastpage :
216
Abstract :
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.
Keywords :
collision avoidance; learning (artificial intelligence); mobile robots; neural nets; position control; anticipation learning; high-level stimuli; navigation control; obstacle avoidance; proximity target sensors; range finder sensors; simulated robot; spike-timing-dependent plasticity; spiking networks; spiking neurons; visual cues; visual input; visual target; Navigation control; spike-timing-dependent plasticity (STDP); spiking neurons; Action Potentials; Algorithms; Animals; Artificial Intelligence; Computer Simulation; Conditioning, Classical; Cues; Feedback; Learning; Memory; Neural Networks (Computer); Neurons; Robotics; Spatial Behavior; Synaptic Transmission; Visual Perception;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2008.2005134
Filename :
4749255
Link To Document :
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