UNSUPERVISED, SUPERVISED, AND REINFORCED LEARNING VIA SPIKING COMPUTATION
First Claim
1. A neural network, comprising:
- a plurality of neural modules, wherein each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module; and
an interconnection network comprising a plurality of edges that interconnect the plurality of neural modules, wherein 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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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
30 Claims
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1. A neural network, comprising:
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a plurality of neural modules, wherein each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module; and an interconnection network comprising a plurality of edges that interconnect the plurality of neural modules, wherein 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20-29. -29. (canceled)
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30. A computer program product on a computer-readable medium for spiking computation in a neural network comprising a plurality of neural modules interconnected via weighted synaptic connections in an interconnection network, wherein each neural module comprises multiple digital neurons such that every neuron in a first neural module is connected to a corresponding neuron in a second neural module via a weighted synaptic connection, the computer program product comprising instructions which when executed on a computer cause the computer to perform operations including:
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generating signals which define a set of time steps for operation of the neurons; each neuron updating its operational state at each time step, and based on that operational state, said neuron determining whether to generate a firing event in response to firing events received as input signals from corresponding neurons in other neural modules, such that each said input signal is weighted by the weighted synaptic connection communicating said input signal to said neuron; adapting a synaptic weight associated with each weighted synaptic connection as a function of the firing events of the interconnected neurons; applying a first learning rule for learning false negatives to a first weighted synaptic connection when a neuron interconnected via the first weighted synaptic connection generates a firing event; applying a second learning rule for unlearning false positives to a second weighted synaptic connection when a neuron interconnected via the second weighted synaptic connection generates a firing event; a set of neurons generating output events to an evaluation module of the neural network, wherein, based on the type of the output events generated, the output events are fed to another set of neurons; and updating the learning rules such that the neural network operates as one or more of;
an auto-associative system, a hetero-associative system, and a reinforcement learning system.
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Specification