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Analysis of neural algorithms for parallel architectures

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5 Author(s)

The computational properties (global and local convergence, computational complexity and algorithm tuning) of neural algorithms are analyzed from a circuit perspective. Different equivalent algorithms are derived by applying various numerical techniques (multistep and relaxation methods) to the stationary and dynamic analysis of a common neural circuit model. Simulation experiments are presented for an 8-bit analog-to-digital-converter implementation of a Hopfield neural network. The implications for the simulation of these neural algorithms on parallel architectures are pointed out

Published in:

Circuits and Systems, 1989., IEEE International Symposium on

Date of Conference:

8-11 May 1989