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The following reprints are available from Center for Automation and
Intelligent Systems Research (CAISR) at Case Western Reserve
University. Send your request to Lawrence Boyd, CAISR, Case Western
Reserve University, Cleveland, OH 44106. Phone 216-368-6434.
Each paper may cost $2 including handling and mailing.

TR 91-131: Y. Takefuji and K. C. Lee, An artificial hysteresis binary 
neuron: a model suppressing the oscillatory behaviors of neural dynamics, 
published in Biological Cybernetics, 64, 353-356, 1991.

ABSTRACT
A hysteresis binary McCulloch-Pitts neuron model is proposed in order to
suppress the complicated oscillatory behaviors of neural dynamics. The
artificial hysteresis binary neural network is used for scheduling
time-multiplex crossbar switches in order to demonstrate the effects of
hysteresis. Time-multiplex crossbar switching system must control
traffic on demand such that packet blocking probability and packet
waiting time are minimized. The system using n x n processing elements
solves an n x n crossbar-control problem with O(1) time, while the best
existing parallel algorithm requires O(n) time. The hysteresis binary
neural network maximizes the throughput of packets through a crossbar
switch. The solution quality of our system does not degrade with the
problem size.