TEMPORAL SPIKE ENCODING FOR TEMPORAL LEARNING

• US 20150317557A1
• Filed: 06/26/2014
• Published: 11/05/2015
• Est. Priority Date: 05/01/2014
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: JULIAN, David Jonathan, ANNAPUREDDY, Venkata Sreekanth Reddy, PETERSON, Kristopher David
• Application Number:
  US14/315,531
• Publication Number:
  US 20150317557A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

Certain aspects of the present disclosure support methods and apparatus for temporal spike encoding for temporal learning in an artificial nervous system. The temporal spike encoding for temporal learning can comprise obtaining sensor data being input into the artificial nervous system, processing the sensor data to generate feature vectors, converting element values of the feature vectors into delays, and causing at least one artificial neuron of the artificial nervous system to spike at times based on the delays.

Claims

1. A method for operating an artificial nervous system, comprising:

obtaining sensor data being input into the artificial nervous system;

processing the sensor data to generate feature vectors;

converting element values of the feature vectors into delays; and

causing at least one artificial neuron of the artificial nervous system to spike at times based on the delays.

2. The method of claim 1, wherein processing the sensor data to generate the feature vectors comprises performing Scale Invariant Feature Transform (SIFT) on the sensor data.

3. The method of claim 1, wherein processing the sensor data further comprises selecting a subset of the feature vectors for converting into the delays.

4. The method of claim 3, wherein the subset of feature vectors comprises a portion of the feature vectors associated with features of the sensor data ranked above a threshold according to specific criteria.

5. The method of claim 3, wherein the subset of feature vectors comprises a portion of the feature vectors associated with frequency bands of the sensor data ranked above a threshold according to specific criteria.

6. The method of claim 1, wherein processing the sensor data comprises using prior learned sensor categories to map the sensor data to the feature vectors.

7. The method of claim 6, wherein the sensor categories comprise histograms related to the sensor data.

8. The method of claim 1, wherein processing the sensor data further comprises pre-distorting the element values of the feature vectors to map the element values to an implicit temporal learning distance metric.

9. The method of claim 1, wherein processing the sensor data further comprises pre-distorting the element values of the feature vectors to achieve a specific distance metric related to the feature vectors.

10. The method of claim 1, wherein converting the element values of feature vectors into the delays comprises linear mapping of the element values into the delays.

11. The method of claim 1, wherein converting the element values of feature vectors into the delays comprises logarithmic mapping of the element values into the delays.

12. The method of claim 1, wherein converting the element values of feature vectors into the delays comprises inverse mapping of the element values into the delays.

13. The method of claim 1, wherein converting the element values of feature vectors into the delays comprises mapping of less frequent and larger of the element values into smaller of the delays.

14. The method of claim 1, further comprising:

mapping of two or more values of the sensor data into spiking of the at least one artificial neuron of the artificial nervous system.

15. The method of claim 1, wherein converting the element values of feature vectors into the delays comprises mapping two or more of the element values into spiking of the at least one artificial neuron of the artificial nervous system.

16. The method of claim 1, further comprising:

learning, based on the at least one artificial neuron spiking, multiple parallel structures of the feature vectors using synapse weight sharing so that the structures learn same weights and are order invariant.

17. An apparatus for operating an artificial nervous system, comprising:

a sensor configured to obtain sensor data being input into the artificial nervous system;

a first circuit configured to process the sensor data to generate feature vectors;

a second circuit configured to convert element values of the feature vectors into delays; and

a third circuit configured to cause at least one artificial neuron of the artificial nervous system to spike at times based on the delays.

18. The apparatus of claim 17, wherein the first circuit is also configured to perform Scale Invariant Feature Transform (SIFT) on the sensor data.

19. The apparatus of claim 17, wherein the first circuit is also configured to select a subset of the feature vectors for converting into the delays.

20. The apparatus of claim 19, wherein the subset of feature vectors comprises a portion of the feature vectors associated with features of the sensor data ranked above a threshold according to specific criteria.

21. The apparatus of claim 19, wherein the subset of feature vectors comprises a portion of the feature vectors associated with frequency bands of the sensor data ranked above a threshold according to specific criteria.

22. The apparatus of claim 17, wherein the first circuit is also configured to use prior learned sensor categories to map the sensor data to the feature vectors.

23. The apparatus of claim 22, wherein the sensor categories comprise histograms related to the sensor data.

24. The apparatus of claim 17, wherein the first circuit is also configured to pre-distort the element values of the feature vectors to map the element values to an implicit temporal learning distance metric.

25. The apparatus of claim 17, wherein the first circuit is also configured to pre-distort the element values of the feature vectors to achieve a specific distance metric related to the feature vectors.

26. The apparatus of claim 17, wherein the second circuit configured to convert the element values of feature vectors into the delays is also configured to perform linear mapping of the element values into the delays.

27. The apparatus of claim 17, wherein the second circuit configured to convert the element values of feature vectors into the delays is also configured to perform logarithmic mapping of the element values into the delays.

28. The apparatus of claim 17, wherein the second circuit configured to convert the element values of feature vectors into the delays is also configured to perform inverse mapping of the element values into the delays.

29. The apparatus of claim 17, wherein the second circuit configured to convert the element values of feature vectors into the delays is also configured to perform mapping of less frequent and larger of the element values into smaller of the delays.

30. The apparatus of claim 17, wherein the third circuit is also configured to map two or more values of the sensor data into spiking of the at least one artificial neuron of the artificial nervous system.

31. The apparatus of claim 17, wherein the third circuit is also configured to map two or more of the element values into spiking of the at least one artificial neuron of the artificial nervous system.

32. The apparatus of claim 17, further comprising:

a fourth circuit configured to learn, based on the at least one artificial neuron spiking, multiple parallel structures of the feature vectors using synapse weight sharing so that the structures learn same weights and are order invariant.

33. An apparatus for operating an artificial nervous system, comprising:

means for obtaining sensor data being input into the artificial nervous system;

means for processing the sensor data to generate feature vectors;

means for converting element values of the feature vectors into delays; and

means for causing at least one artificial neuron of the artificial nervous system to spike at times based on the delays.

34. A computer-readable medium having instructions executable by a computer stored thereon for:

obtaining sensor data being input into an artificial nervous system;

processing the sensor data to generate feature vectors;

converting element values of the feature vectors into delays; and

causing at least one artificial neuron of the artificial nervous system to spike at times based on the delays.