IP Library › Granted Patent US 10,445,642

Granted Patent B2

US 10,445,642 · App. 15/162,361 · Granted Oct 15, 2019

Unsupervised, supervised and reinforced learning via spiking computation

Inventor: Dharmendra S. Modha (San Jose, CA)

Assignee: International Business Machines Corporation

G06N3/08 G06N3/04 G06N3/049 G06N3/063 G06N3/088

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Quick Facts

Patent No.: US 10,445,642
App. No.: 15/162,361
Granted: Oct 15, 2019
Kind: B2

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USPTO Patent Center↗ Full Patent PDF↗

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.

Claims (45)

1. A method, comprising:

training a neural network for reinforcement learning by:

receiving output comprising firing events generated by a neuron population of the neural network;

determining a type of the output; and

propagating the output through one or more other neural populations of the neural network based on the type of the output, wherein the output is copied and propagated through a first set of neuron populations for the neural network to learn the output in response to determining the type of the output is a first type, and the output is propagated through a second set of neuron populations different from the first set of populations for the neural network to unlearn the output in response to determining the type of the output is a second type different from the first type.

2. The method of claim 1 ,

wherein the first type represents positive output including false negatives for the neural network to learn, and the second type represents negative output including false positives for the neural network to unlearn.

3. The method of claim 2 , wherein the propagating comprises:

in response to determining the type of the output is the first type, providing a copy of the output to the first set of neuron populations, wherein the output is learned by the neural network as the copy of the output propagates through the first set of neuron populations via a first set of pathways.

4. The method of claim 3 , wherein the propagating further comprises:

in response to determining the type of the output is the first type, applying Hebb learning based on firing activity of the first set of neuron populations to adjust synaptic weights of the first set of pathways.

5. The method of claim 2 , wherein the propagating comprises:

in response to determining the type of the output is the second type, providing a copy of the output to the second set of neuron populations, wherein the output is unlearned by the neural network as the copy of the output propagates through the second set of neuron populations via a second set of pathways.

6. The method of claim 5 , wherein the propagating further comprises:

in response to determining the type of the output is the second type, applying anti-Hebb learning based on firing activity of the second set of neuron populations to adjust synaptic weights of the second set of pathways.

7. A system comprising a computer processor, a computer-readable hardware storage medium, and program code embodied with the computer-readable hardware storage medium for execution by the computer processor to implement a method comprising:

training a neural network for reinforcement learning by:

receiving output comprising firing events generated by a neuron population of the neural network;

determining a type of the output; and

8. The system of claim 7 ,

9. The system of claim 8 , wherein the propagating comprises:

10. The system of claim 9 , wherein the propagating further comprises:

11. The system of claim 8 , wherein the propagating comprises:

12. The system of claim 11 , wherein the propagating further comprises:

13. 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:

training a neural network for reinforcement learning by:

receiving output comprising firing events generated by a neuron population of the neural network;

determining a type of the output; and

14. The computer program product of claim 13 ,

15. The computer program product of claim 14 , wherein the propagating comprises:

16. The computer program product of claim 15 , wherein the propagating further comprises:

17. The computer program product of claim 14 , wherein the propagating comprises:

18. The computer program product of claim 17 , wherein the propagating further comprises:

Assignments (1)

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 23, 2016

From: MODHA, DHARMENDRA S.

To: INTERNATIONAL BUSINESS MACHINES CORPORATION

Reel/Frame 038794/0723 →

Continuity (3)

Continuation 14494372 · Sep 23, 2014

Continuation 13235342 · Sep 16, 2011

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