A new paper on mirror system modeling and robotics experiments

[Jun Tani]

We are pleased to announce the availability of the following paper,
which we hope will be of interest.

J. Tani, M. Ito, and Y. Sugita "Self-organization of distributedly
represented multiple behavior schemata in a mirror system: reviews of
robot experiments using RNNPB", Neural Networks, Vol.17, pp.1273-1289,
2004.

The preprint of the paper is available from:
http://www.bdc.brain.riken.go.jp/~tani/publications.htm

Abstract:
The current paper reviews a connectionist model, the recurrent neural
network with parametric biases (RNNPB), in which multiple behavior
schemata can be learned by the network in a distributed manner.
The parametric biases in the network play an essential role in both
generating and recognizing behavior patterns. They act as a mirror
system by means of self-organizing adequate memory structures. Three
different robot experiments are reviewed: robot and user interactions;
learning and generating different types of dynamic patterns; and
linguistic-behavior binding.
The hallmark of this study is explaining how self-organizing internal
structures can contribute to generalization in learning, and diversity
in behavior generation, in the proposed distributed representation
scheme.

Jun Tani, Ph.D
Lab. for Behavior and Dynamic Cognition
Brain Science Institute, RIKEN
2-1 Hirosawa, Wako-shi, Saitama, 351-0198 Japan
tani at brain.riken.go.jp
http://www.bdc.brain.riken.go.jp/~tani
Tel: +81-48-467-6467
Fax: +81-48-467-7248