title: A feedforward artificial neural network based on quantum effect vector-matrix multipliers.
abstract: The vector-matrix multiplier is the engine of many artificial neural network implementations because it can simulate the way in which neurons collect weighted input signals from a dendritic arbor. A new technology for building analog weighting elements that is theoretically capable of densities and speeds far beyond anything that conventional VLSI in silicon could ever offer is presented. To illustrate the feasibility of such a technology, a small three-layer feedforward prototype network with five binary neurons and six tri-state synapses was built and used to perform all of the fundamental logic functions: XOR, AND, OR, and NOT.
authors.affiliation: Dept. of Appl. Phys., California Inst. of Technol., Pasadena, CA.
authors.forename: H J
authors.initials: HJ
authors.lastname: Levy
authors.forename: T C
authors.initials: TC
authors.lastname: McGill