Neuron for use in self-learning neural network
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United States of America Patent
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May 2, 1995
Grant Date -
N/A
app pub date -
Jan 6, 1994
filing date -
Jan 6, 1994
priority date (Note) -
Expired
status (Latency Note)
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Abstract
A neuron for use in a self-learning neural network comprises a current input node at which a plurality of synaptic input currents are summed using Kirchoff's current law. The summed input currents are normalized using a coarse gain current normalizer. The normalized summed inputs current is then converted to a voltage using a current to voltage converter. This voltage is then amplified by a gain controlled cascode output amplifier. Gain control inputs are provided in the output amplifier so that the neuron can be settled by the Mean Field Approximation. A noise input stage is also connected to the output amplifier so that the neuron can be settled using simulated annealing. The resulting neuron is a variable gain, bi-directional current transimpedance neuron with a controllable noise input.
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