NDLI: Relaxing in a warped space: an effect due to the cooperation of static and dynamical neurons

Content Provider	IEEE Xplore Digital Library
Author	Tsutsumi, K.
Copyright Year	2003
Description	Author affiliation: Dept. of Mech. & Syst. Eng., Ryukoku Univ., Japan (Tsutsumi, K.)
Abstract	This paper proposes a module-based neural network composed of static and dynamical neurons, and discusses what effect can be produces by the integration of mapping and relaxation. The proposed network can be obtained when a specially-designed total energy function is minimized. Although the derivation process is similar to the case of the well-known Hopfield network, state variables in the network included, in addition to the addition to the output and the potential of dynamical neurons, another type of state variable converted from the direct output of dynamical neurons using a mapping function. If we suppose a layered network with only static neurons corresponding to the mapping function, the layered network comprises a forward subnet and a backward subnet; connection weights in the forward and backward subnets are modified based on propagated error signals through the backward subnet, and, at the same time, the final output of the backward subnet is utilized for overall network-dynamics. As a result, the proposed network offers a framework in which relaxation can be carried out in a warped space due to the cooperation of static and dynamical neurons. Furthermore, it gives an interpretation for the backpropagated error signals in the case of delta-rule based learning; although the backward subnet for the calculating the delta values is usually assumed to be virtual, it must actually exist for network relaxation in the proposed network.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	897
Ending Page	901
File Size	259512
Page Count	5
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223809
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Biological neural networks Biological system modeling Neural networks Power engineering and energy Systems engineering and theory Signal mapping Brain modeling Transfer functions Robots
Content Type	Text
Resource Type	Article

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