Paper Info
Title
An Intrinsic Neuromodulation Model for Realizing Anticipatory Behavior in Reaching Movement under Unexperienced Force Fields
Abstract
Regardless of complex, unknown, and dynamically-changing environments, living creatures can recognize situated environments and behave adaptively in real-time. However, it is impossible to prepare optimal motion trajectories with respect to every possible situations in advance. The key concept for realizing the environment cognition and motor adaptation is a context-based elicitation of constraints which are canalizing well-suited sensorimotor coordination. For this aim, in this study, we propose a polymorphic neural networks model called CTRNN+NM (CTRNN with neuromodulatory bias). The proposed model is applied to two dimensional arm-reaching movement control under various viscous force fields. The parameters of the networks are optimized using genetic algorithms. Simulation results indicate that the proposed model inherits high robustness even though it is situated in unexperienced environments.
Year
DOI
Venue
2006
10.1007/978-3-540-74262-3_14
SAB ABiALS
Keywords
Field
DocType
key concept,polymorphic neural networks model,environment cognition,dynamically-changing environment,realizing anticipatory behavior,high robustness,genetic algorithm,context-based elicitation,unexperienced force fields,intrinsic neuromodulation model,motor adaptation,dimensional arm-reaching movement control,neural network model,real time,polymorphism,force field
Creatures,Situated,Computer science,Robustness (computer science),Artificial intelligence,Cognition,Artificial neural network,Synaptic weight,Genetic algorithm,Instrumental and intrinsic value,Machine learning
Conference
Volume
ISSN
Citations 
4520
0302-9743
1
PageRank 
References 
Authors
0.35
11
2
Name
Order
Citations
PageRank
Toshiyuki Kondo113128.57
Koji Ito2154.52