Non-isotonous beta-driven artificial neuron
Victor I. Varshavsky, Vyacheslav B. Marakhovsky · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
In this paper we discuss variants of digital-analog CMOS implementation of artificial neuron taught to logical threshold functions. The implementation is based on earlier suggested (beta) -comparator and three output amplifiers. Such a circuit can be taught only to threshold functions with positive weights of variables, which belong to the class of isotonous Boolean functions. However, most problems solved by artificial neural networks either require inhibitory inputs. If the input type is known beforehand, the problem of inverting the weight sign is solved trivially by inverting the respective variable. Otherwise, the neural should have synapses capable of forming the weight and type of the input during the learning, using only increment and decrement signals. A neuron with such synapses can learn an arbitrary threshold function of a certain number of variables. Synapse circuits are suggested with two or one memory element for storing positive and negative input weights. The results of SPICe simulation prove that the problem of teaching non-isotonous threshold functions to a neuron has stable solutions.