Multi-modal neural network for universal, online learning
First Claim
1. A method comprising:
- receiving motor output from one or more neurons along a top-down signaling pathway of a neuromorphic and synaptronic hardware architecture comprising electronic circuitry in response to the one or more neurons firing, wherein the one or more neurons correspond to one or more other neurons along a bottom-up signaling pathway of the neuromorphic and synaptronic hardware architecture;
determining whether the motor output is a first type of motor output or a second type of motor output based on firing events of a first neuron of the one or more neurons and firing events of a second neuron of the one or more other neurons, wherein the second neuron corresponds to the first neuron; and
training the one or more other neurons to approximate the one or more neurons by;
in response to determining the motor output is the first type of motor output, training the one or more other neurons to learn the motor output by routing the motor output and a first input to a third neuron and a fourth neuron of the neuromorphic and synaptronic hardware architecture, respectively; and
in response to determining the motor output is the second type of motor output, training the one or more other neurons to unlearn the motor output by routing the motor output and the first input to the fourth neuron and the third neuron, respectively;
wherein the neuromorphic and synaptronic hardware architecture resulting from the training is a single universal substrate for unsupervised learning, supervised learning, and reinforcement learning.
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Abstract
In one embodiment, the present invention provides a neural network comprising multiple modalities. Each modality comprises multiple neurons. The neural network further comprises an interconnection lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways. Action neurons along a number of top-down signaling pathways drive an output neuron population configured to generate motor output.
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Citations
20 Claims
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1. A method comprising:
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receiving motor output from one or more neurons along a top-down signaling pathway of a neuromorphic and synaptronic hardware architecture comprising electronic circuitry in response to the one or more neurons firing, wherein the one or more neurons correspond to one or more other neurons along a bottom-up signaling pathway of the neuromorphic and synaptronic hardware architecture; determining whether the motor output is a first type of motor output or a second type of motor output based on firing events of a first neuron of the one or more neurons and firing events of a second neuron of the one or more other neurons, wherein the second neuron corresponds to the first neuron; and training the one or more other neurons to approximate the one or more neurons by; in response to determining the motor output is the first type of motor output, training the one or more other neurons to learn the motor output by routing the motor output and a first input to a third neuron and a fourth neuron of the neuromorphic and synaptronic hardware architecture, respectively; and in response to determining the motor output is the second type of motor output, training the one or more other neurons to unlearn the motor output by routing the motor output and the first input to the fourth neuron and the third neuron, respectively; wherein the neuromorphic and synaptronic hardware architecture resulting from the training is a single universal substrate for unsupervised learning, supervised learning, and reinforcement learning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising a computer processor, a computer-readable hardware storage device, and program code embodied with the computer-readable hardware storage device for execution by the computer processor to implement a method comprising:
receiving motor output from one or more neurons along a top-down signaling pathway of a neuromorphic and synaptronic hardware architecture comprising electronic circuitry in response to the one or more neurons firing, wherein the one or more neurons correspond to one or more other neurons along a bottom-up signaling pathway of the neuromorphic and synaptronic hardware architecture; determining whether the motor output is a first type of motor output or a second type of motor output based on firing events of a first neuron of the one or more neurons and firing events of a second neuron of the one or more other neurons, wherein the second neuron corresponds to the first neuron; and training the one or more other neurons to approximate the one or more neurons by; in response to determining the motor output is the first type of motor output, training the one or more other neurons to learn the motor output by routing the motor output and a first input to a third neuron and a fourth neuron of the neuromorphic and synaptronic hardware architecture, respectively; and in response to determining the motor output is the second type of motor output, training the one or more other neurons to unlearn the motor output by routing the motor output and the first input to the fourth neuron and the third neuron, respectively; wherein the neuromorphic and synaptronic hardware architecture resulting from the training is a single universal substrate for unsupervised learning, supervised learning, and reinforcement learning. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer program product comprising a computer-readable hardware storage device having program code embodied therewith, the program code being executable by a computer to implement a method comprising:
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receiving motor output from one or more neurons along a top-down signaling pathway of a neuromorphic and synaptronic hardware architecture comprising electronic circuitry in response to the one or more neurons firing, wherein the one or more neurons correspond to one or more other neurons along a bottom-up signaling pathway of the neuromorphic and synaptronic hardware architecture; determining whether the motor output is a first type of motor output or a second type of motor output based on firing events of a first neuron of the one or more neurons and firing events of a second neuron of the one or more other neurons, wherein the second neuron corresponds to the first neuron; and training the one or more other neurons to approximate the one or more neurons by; in response to determining the motor output is the first type of motor output, training the one or more other neurons to learn the motor output by routing the motor output and a first input to a third neuron and a fourth neuron of the neuromorphic and synaptronic hardware architecture, respectively; and in response to determining the motor output is the second type of motor output, training the one or more other neurons to unlearn the motor output by routing the motor output and the first input to the fourth neuron and the third neuron, respectively; wherein the neuromorphic and synaptronic hardware architecture resulting from the training is a single universal substrate for unsupervised learning, supervised learning, and reinforcement learning. - View Dependent Claims (18, 19, 20)
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Specification