Abstract
The paper is focused on the functional possibilities (class of representable threshold functions), parameter stability and learnability of the artificial learnable neuron implemented on the base of CMOS β-driven threshold element. A neuron β-comparator circuit is suggested with a very high sensitivity to input current change that allows us to sharply increase the threshold value of the functions. The SPICE simulation results confirm that the neuron is learnable to realize threshold functions of 10, 11 and 12 variables with maximum values of threshold 89, 144 and 233 respectively. A number of experiments were conducted to determine the limits in which the working parameters of the neuron can change providing its stable functioning after learning to the functions for each of these threshold values. MOSIS BSIM3v3.1 0.8μm transistor models were used in the SPICE simulation.
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© 1999 Springer-Verlag Berlin Heidelberg
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Varshavsky, V., Marakhovsky, V. (1999). Beta-CMOS artificial neuron and implementability limits. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100478
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DOI: https://doi.org/10.1007/BFb0100478
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