Method and apparatus for unsupervised training of input synapses of primary visual cortex simple cells and other neural circuits
First Claim
1. An electrical circuit, comprising:
- a plurality of Retinal Ganglion Cell (RGC) circuits, wherein each of the RGC circuits generates, at an output, a sum of weighted inputs from receptor circuits associated with that RGC circuit;
a plurality of primary visual cortex cell (V1) circuits, wherein each of the V1 circuits generates another sum of weighted outputs of a subset of the RGC circuits; and
a circuit configured to adjust weights applied on the outputs for generating the other sum, whereinthe adjustment of one of the weights is based on at least one of one of the outputs on which that weight is applied or the other sum.
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Abstract
Certain aspects of the present disclosure present a technique for unsupervised training of input synapses of primary visual cortex (V1) simple cells and other neural circuits. The proposed unsupervised training method utilizes simple neuron models for both Retinal Ganglion Cell (RGC) and V1 layers. The model simply adds the weighted inputs of each cell, wherein the inputs can have positive or negative values. The resulting weighted sums of inputs represent activations that can also be positive or negative. In an aspect of the present disclosure, the weights of each V1 cell can be adjusted depending on a sign of corresponding RGC output and a sign of activation of that V1 cell in the direction of increasing the absolute value of the activation. The RGC-to-V1 weights can be positive and negative for modeling ON and OFF RGCs, respectively.
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Citations
21 Claims
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1. An electrical circuit, comprising:
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a plurality of Retinal Ganglion Cell (RGC) circuits, wherein each of the RGC circuits generates, at an output, a sum of weighted inputs from receptor circuits associated with that RGC circuit; a plurality of primary visual cortex cell (V1) circuits, wherein each of the V1 circuits generates another sum of weighted outputs of a subset of the RGC circuits; and a circuit configured to adjust weights applied on the outputs for generating the other sum, wherein the adjustment of one of the weights is based on at least one of one of the outputs on which that weight is applied or the other sum. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for implementing a neural system, comprising:
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generating, at an output of each Retinal Ganglion Cell (RGC) circuit of a plurality of RGC circuits in the neural system, a sum of weighted inputs from receptor circuits associated with that RGC circuit; generating, by each primary visual cortex cell (V1) circuit of a plurality of V1 circuits in the neural system, another sum of weighted outputs of a subset of the RGC circuits; and adjusting weights applied on the outputs for generating the other sum, wherein the adjustment of one of the weights is based on at least one of one of the outputs on which that weight is applied or the other sum. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. An apparatus, comprising:
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means for generating, at an output of each Retinal Ganglion Cell (RGC) circuit of a plurality of RGC circuits in the apparatus, a sum of weighted inputs from receptor circuits associated with that RGC circuit; means for generating, by each primary visual cortex cell (V1) circuit of a plurality of V1 circuits in the apparatus, another sum of weighted outputs of a subset of the RGC circuits; and means for adjusting weights applied on the outputs for generating the other sum, wherein the adjustment of one of the weights is based on at least one of one of the outputs on which that weight is applied or the other sum. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification