Neuron for use in self-learning neural network
First Claim
1. A neuron for use in a self-learning neural network comprising,an input node for summing a plurality of bi-directional synaptic input currents to obtain a summed bi-directional input current,a current normalizer for normalizing said summed input current to obtain a normalized summed input current,a current to voltage converter for converting the normalized summed input current into a bi-directional voltage representative of the normalized summed bi-directional input current, andan output amplifier having a gain for generating bi-directional output voltage in response to said voltage representative of said normalized summed input current.
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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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Citations
14 Claims
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1. A neuron for use in a self-learning neural network comprising,
an input node for summing a plurality of bi-directional synaptic input currents to obtain a summed bi-directional input current, a current normalizer for normalizing said summed input current to obtain a normalized summed input current, a current to voltage converter for converting the normalized summed input current into a bi-directional voltage representative of the normalized summed bi-directional input current, and an output amplifier having a gain for generating bi-directional output voltage in response to said voltage representative of said normalized summed input current.
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13. A neuron for use in a neural network comprising:
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a current normalizer for normalizing an input current, a current to voltage converter for converting said input current into a voltage representative of said input current, and an output cascode mixing amplifier having a first input for receiving said voltage representative of said input current, a second input for receiving a gain anneal signal for settling said neuron using the Mean Field Approximation and a noise input for receiving a noise signal and a noise anneal input for providing a decaying envelope for the noise signal to settle the neuron using the simulated annealing. - View Dependent Claims (14)
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Specification