Quasi Analog Formal Neuron and Its Learning Algorithm Hardware

  • Authors:

  • Karen Nazaryan

  • Affiliations:

  • -

  • Venue:
  • ICCS '01 Proceedings of the International Conference on Computational Science-Part II
  • Year:
  • 2001

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Abstract

A version of learning algorithm hardware implementation for a new neuron model -- quasi analog formal neuron (QAFN) is considered in this paper. Due to the presynaptic interaction of "AND" type, wide functional class (including all Boolean functions) for the QAFN operating is provided based on only one neuron. There exist two main approaches of neurons, neural networks (NN) and their learning algorithm hardware implementations: analog and digital. The QAFN and its learning algorithm hardware are based on those two approaches simultaneously. Weight reprogrammability is realized based on EEPROM technique that is compatible with CMOS technology. The QAFN and its learning algorithm hardware are suitable to implement in VLSI technology.