US 5,706,403 AGranted

Semiconductor neural circuit device

OwnerTadashi Shibata, Tadahiro Ohmiinventors

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Abstract

A semiconductor neural circuit device having a very simple circuit and a self-teaching function, by which a neural network is allowed to learn. The device comprises synapse circuits which output weighted values, and neuron circuits which execute linear addition of the output signals from the synapse circuits, and output the signal voltages of high and low levels with respect to a given threshold value V.sub.TH. In the case of learning of increasing the total value Z, only when V.sub.TH -.epsilon.<Z<V.sub.TH +.alpha. with respect to two positive parameters .epsilon. and .alpha., the weighted value of predetermined synapse circuits which input signals to the neuron circuit is increased by a given positive value. Conversely, in the case of learning of the decreasing the total value Z, only when the V.sub.TH -.alpha.<Z<V.sub.TH +.epsilon., the weighted value of predetermined synapse circuit which input signals to the neuron circuit is decreased by a given positive value.

Claims6

Dependent claims

An artificial neural circuit comprising:

a first plurality of synapse circuits which output values of input signals multiplied by predetermined weighting values W.sub.i, and a plurality of neuron circuits which determine a total value Z representing the result of linear addition of outputted value signals of a second plurality of synapse circuits, and which output a high level signal voltage when, with respect to a predetermined threshold V.sub.TH, Z>V.sub.TH, and output a low level signal voltage when Z <V.sub.TH,
wherein during learning of a respective neuron circuit, with respect to learning of increasing the total value Z, only when
V.sub.TH -.epsilon.<Z<V.sub.TH +.alpha.
with respect to two positive parameters .epsilon. and .alpha. is the weighting value of predetermined synapse circuits which input signals into said respective neuron circuit increased by a first predetermined positive value, and
in the case of learning of decreasing the total value Z, only in the case in which
V.sub.TH -.alpha.<Z<V.sub.TH +.epsilon.
is the weighting value of predetermined synapse circuits which input signals into said respective neuron circuit decreased by a second predetermined positive value.

4 more elements

Dependencies are inferred from the claim text.