Unsupervised, supervised, and reinforced learning via spiking computation
First Claim
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1. A method for feature extraction, comprising:
- providing sensory input to a first neural module in a neural network comprising a plurality of neural modules interconnected via a plurality of weighted synaptic connections;
extracting one or more input features from said sensory input as said sensory input propagates from said first neural module through said neural network in a first direction via at least one of said plurality of weighted synaptic connections;
providing motor output to a second neural module in said neural network;
extracting one or more output features from said motor output as said motor output propagates from said second neural module through said neural network in a second direction via at least one of said plurality of weighted synaptic connections, wherein said first direction is opposite of said second direction; and
associating said one or more input features with said one or more output features.
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Abstract
The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module.
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Citations
14 Claims
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1. A method for feature extraction, comprising:
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providing sensory input to a first neural module in a neural network comprising a plurality of neural modules interconnected via a plurality of weighted synaptic connections; extracting one or more input features from said sensory input as said sensory input propagates from said first neural module through said neural network in a first direction via at least one of said plurality of weighted synaptic connections; providing motor output to a second neural module in said neural network; extracting one or more output features from said motor output as said motor output propagates from said second neural module through said neural network in a second direction via at least one of said plurality of weighted synaptic connections, wherein said first direction is opposite of said second direction; and associating said one or more input features with said one or more output features. - View Dependent Claims (2, 3, 4, 5)
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6. A system, comprising:
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at least one processor; and a non-transitory processor-readable memory device storing instructions that when executed by said at least one processor causes said at least one processor to perform operations including; receiving, at a first neural module of a plurality of neural modules of a neural network, sensory input; receiving, at a second neural module of said plurality of neural modules, motor output; extracting one or more input features from said sensory input as said sensory input propagates from said first neural module through said neural network in a first direction via at least one of a plurality of weighted synaptic connections interconnecting said plurality of neural modules; extracting one or more output features from said motor output as said motor output propagates from said second neural module through said neural network in a second direction via at least one of said plurality of weighted synaptic connections, wherein said first direction is opposite of said second direction; and associating said one or more input features with said one or more output features. - View Dependent Claims (7, 8, 9, 10)
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11. A non-transitory computer program product on a computer-readable medium for feature extraction, the computer program product comprising instructions which when executed on a computer cause the computer to perform operations including:
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providing sensory input to a first neural module in a neural network comprising a plurality of neural modules interconnected via a plurality of weighted synaptic connections; extracting one or more input features from said sensory input as said sensory input propagates from said first neural module through said neural network in a first direction via at least one of said plurality of weighted synaptic connections; providing motor output to a second neural module in said neural network; extracting one or more output features from said motor output as said motor output propagates from said second neural module through said neural network in a second direction via at least one of said plurality of weighted synaptic connections, wherein said first direction is opposite of said second direction; and associating said one or more input features with said one or more output features. - View Dependent Claims (12, 13, 14)
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Specification