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On Collective Computational Properties of T-Model and Hopfield Neural Networks
Okihiko ISHIZUKA Zheng TANG Akihiro TAKEI Hiroki MATSUMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1992/06/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section of Papers Selected from 1991 Joint Technical Conference on Circuits/Systems, Computers and Communications (JTC-CSCC '91))
Category: Neural Network Design
neural network, T-Model, Hopfield model, collective computation,
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This paper extends an earlier study on the T-Model neural network to its collective computational properties. We present arguments that it is necessary to use the half-interconnected T-Model networks rather than the fully-interconnected Hopfield model networks. The T-Model has been generated in response to a number of observed weaknesses in the Hopfield model. This paper identities these problems and show how the T-Model overcomes them. The T-Model network is essentially a feedforward network which does not produce a local minimum for computations. A concept for understanding the dynamics of the T-Model neural circuit is presented and its performance is also compared with the Hopfield model. The T-Model neural circuit is implemented and tested with standard CMOS technology. Simulations and experiments show that the T-Model allows immense collective network computations and does not produce a local minimum. High densities comparable to that of the Hopfield model implementations have also been achieved.