Neuron for use in self-learning neural network
US5412256A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jan 6, 1994 |
| Grant date | May 2, 1995 |
| Priority date | — |
| Expiry date | Jan 6, 2014 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/063
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.