SENSORY EVENT RECODING AND DECODING

• US 20090012581A1
• Filed: 05/01/2008
• Published: 01/08/2009
• Est. Priority Date: 05/01/2007
• Status: Abandoned Application

• Current Assignee: Evolved Machines, Inc.
• Original Assignee: Evolved Machines, Inc.
• Inventors: Rhodes, Paul A.
• Application Number:
  US12/113,837
• Publication Number:
  US 20090012581A1
• Time in Patent Office: Days
• US Class Current: 607/59
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/063 using electronic means

Abstract

A first pattern of sensory activity is input to a simulated neural circuit on a set of sensor inputs. The simulated neural circuit includes an array of branched neural elements that each have at least one output and one or more branches that are impinged by subsets of the set of sensor inputs. Activity is generated in the branches based on the first pattern of activity input to the simulated neural circuit, and a second pattern of activity is generated in the outputs of the array of branched neural elements based on the activity in their branches. The second pattern of activity represents a recoding of the first pattern of sensory activity.

Claims

1. A method for recoding a pattern of sensory activity comprising:

inputting a first pattern of sensory activity on a set of sensor inputs to a simulated neural circuit that includes an array of branched neural elements, the branched neural elements each having at least one output and one or more input branches that are impinged by subsets of the set of sensor inputs;

generating activity in the branches based on the first pattern of activity input to the simulated neural circuit; and

generating a second pattern of activity in the outputs of the array of branched neural elements based on the activity in their branches, the second pattern of activity representing a recoding of the first pattern of sensory activity.

2. The method of claim 1 wherein:

generating activity in the branches comprises activating one or more branches impinged by one or more of the activated sensor inputs; and

generating a second pattern of activity in the outputs of the array of branched neural elements comprises activating a subset of the array of branched neural elements, the activated branched neural elements having at least one activated branch.

3. The method of claim 2 wherein the first pattern of sensory activity reflects one or more sensory features.

4. The method of claim 2 wherein:

activating one or more branches impinged by one or more of the activated sensor inputs comprises activating only those branches for which the activity in the subsets of sensor inputs that impinge the branches exceeds a first threshold activity level during a first specified window of time; and

activating a subset of the array of branched neural elements comprises activating only those neural elements for which the activity in the neural elements'"'"' input branches exceeds a second threshold activity level during a second specified window of time.

5. The method of claim 4 wherein:

the second threshold activity level is a threshold number of activated branches; and

activating a subset of the array of branched neural elements comprises activating only those branched neural elements that have a number of activated branches during the second specified window of time that exceeds the threshold number of activated branches.

6. The method of claim 5 wherein the threshold number of activated branches is different for different branched neural elements.

7. The method of claim 5 wherein the threshold number of activated branches is the same for each branched neural element.

8. The method of claim 1 wherein the first pattern of sensory activity includes different non-binary signal levels in the set of sensor inputs.

9. The method of claim 8 wherein:

generating activity in the branches comprises generating non-binary signal levels in the branches in response to the non-binary signal levels in the sensor inputs that impinge the branches; and

generating a second pattern of activity in the array of branched neural elements comprises activating branched neural elements as a function of the activity levels in the branches of the branched neural elements.

10. The method of claim 9 wherein generating activity in the branches in response to the non-binary signal levels in the sensor inputs that impinge the branches comprises activating branches in which, during a specified window in time, a magnitude of the activity levels in the sensor inputs that impinge the branches exceeds a threshold branch activation level.

11. The method of claim 1 wherein:

inputting the first pattern of sensory activity comprises inputting a sensory event vector, and

elements of the sensory event vector correspond to individual sensory features of a sensory event.

12. The method of claim 1 further comprising:

detecting one or more sensory features associated with a sensory event; and

generating the first pattern of sensory activity based on the detected sensory features such that the first pattern of sensory activity reflects the detected sensory features.

13. The method of claim 12 wherein detecting one or more sensory features associated with a sensory event comprises using an array of sensory feature sensors to detect the sensory features.

14. The method of claim 1 further comprising decoding the second pattern of activity to identify a particular sensory event.

15. The method of claim 14 wherein:

the first pattern of sensory activity reflects one or more olfactory sensory features associated with an odorant such that the second pattern of activity represents a recoding of the sensory features associated with the odorant; and

decoding the second pattern of activity to identify a particular sensory event comprises decoding the second pattern of activity to identify the odorant.

16. The method of claim 14 wherein:

the first pattern of sensory activity reflects one or more visual sensory features associated with an object such that the second pattern of activity represents a recoding of the visual sensory features associated with the object; and

decoding the second pattern of activity to identify a particular sensory event comprises decoding the second pattern of activity to identify the object.

17. The method of claim 1 wherein:

the first pattern of sensory activity represents a set of sensory features associated with a sensory event; and

generating a second pattern of activity in the array of branched neural elements that represents a recoding of the first pattern of sensory activity comprises generating a second pattern of activity in the array of branched neural elements that represents a combination of higher order features associated with the sensory event, the higher order features associated with the sensory event signifying the joint presence of one or more subsets of sensory features within the set of sensory features associated with the sensory event.

18. A computer program for recoding a first pattern of sensory activity, the computer program being embodied on a tangible, computer-readable medium and including instructions that, when executed, cause a processor to:

input a first pattern of sensory activity to a simulated neural circuit that includes a set of sensor inputs and an array of branched neural elements, the branched neural elements each having at least one output and one or more branches that are impinged by subsets of the sensor inputs;

generate activity in the branches based on the first pattern of activity input to the simulated neural circuit; and

generate a second pattern of activity in the outputs of the array of branched neural elements based on the activity in their branches, the second pattern of activity representing a recoding of the first pattern of sensory activity.

19. The computer program of claim 18 wherein:

the instructions that, when executed, cause a processor to generate activity in the branches comprise instructions that, when executed, cause a processor to activate one or more branches impinged by one or more of the activated sensor inputs; and

the instructions that, when executed, generate a second pattern of activity in the outputs of the array of branched neural elements comprise instructions that, when executed, cause a processor to activate a subset of the array of branched neural elements, the activated branched neural elements having at least one activated branch.

20. The computer program of claim 19 wherein the first pattern of sensory activity reflects one or more sensory features.

21. The computer program of claim 19 wherein:

the instructions that, when executed, cause a processor to activate one or more branches impinged by one or more of the activated sensor inputs comprise instructions that, when executed, cause a processor to activate only those branches for which the activity in the subsets of sensor inputs that impinge the branches exceeds a first threshold activity level during a first specified window of time; and

the instructions that, when executed, cause a processor to activate a subset of the array of branched neural elements comprise instructions that, when executed, cause a processor to activate only those neural elements for which the activity in the neural elements'"'"' input branches exceeds a second threshold activity level during a second specified window of time.

22. The computer program of claim 21 wherein:

the second threshold activity level is a threshold number of activated branches; and

the instructions that, when executed, cause a processor to activate a subset of the array of branched neural elements comprise instructions that, when executed, cause a processor to activate only those branched neural elements that have a number of activated branches during the second specified window of time that exceeds the threshold number of activated branches.

23. The computer program of claim 18 wherein:

the first pattern of sensory activity includes different non-binary signal levels in the set of sensor inputs;

the instructions that, when executed, cause a processor to generate activity in the branches comprise instructions that, when executed, cause a processor to generate non-binary signal levels in the branches in response to the non-binary signal levels in the sensor inputs that impinge the branches; and

the instructions that, when executed, cause a processor to generate a second pattern of activity in the array of branched neural elements comprise instructions that, when executed, cause a processor to activate branched neural elements as a function of the activity levels in the branches of the branched neural elements.

24. The computer program of claim 23 wherein the instructions that, when executed, generate activity in the branches in response to the non-binary signal levels in the sensor inputs that impinge the branches comprise instructions that, when executed, cause a processor to activate branches in which, during a specified window in time, a magnitude of the activity levels in the sensor inputs that impinge the branches exceeds a threshold branch activation level.

25. The computer program of claim 18 wherein:

the instructions that, when executed, cause a processor to input the first pattern of sensory activity comprise instructions that, when executed, cause a processor to input a sensory event vector; and

elements of the sensory event vector correspond to individual sensory features of a sensory event.

26. The computer program of claim 18 further comprising instructions that, when executed, cause a processor to:

detect one or more sensory features associated with a sensory event; and

generate the first pattern of sensory activity based on the detected sensory features such that the first pattern of sensory activity reflects the detected sensory features.

27. The computer program of claim 26 wherein the instructions that, when executed, cause a processor to detect one or more sensory features associated with a sensory event comprise instructions that, when executed, cause a processor to use an array of sensory feature sensors to detect the sensory features.

28. The computer program of claim 18 further comprising instructions that, when executed, cause a processor to decode the second pattern of activity to identify a particular sensory event.

29. The computer program of claim 28 wherein:

the first pattern of sensory activity reflects one or more olfactory sensory features associated with an odorant such that the second pattern of activity represents a recoding of the sensory features associated with the odorant; and

the instructions that, when executed, cause a processor to decode the second pattern of activity to identify a particular sensory event comprise instructions that, when executed, cause a processor to decode the second pattern of activity to identify the odorant.

30. The computer program of claim 28 wherein:

the first pattern of sensory activity reflects one or more visual sensory features associated with an object such that the second pattern of activity represents a recoding of the visual sensory features associated with the object; and

the instructions that, when executed, cause a processor to decode the second pattern of activity to identify a particular sensory event comprise instructions that, when executed, cause a processor to decode the second pattern of activity to identify the object.

31. The computer program of claim 18 wherein:

the first pattern of sensory activity represents a set of sensory features associated with a sensory event; and

the instructions that, when executed, cause a processor to generate a second pattern of activity in the array of branched neural elements that represents a recoding of the first pattern of sensory activity comprise instructions that, when executed, cause a processor to generate a second pattern of activity in the array of branched neural elements that represents a combination of higher order features associated with the sensory event, the higher order features associated with the sensory event signifying the joint presence of one or more subsets of sensory features within the set of sensory features associated with the sensory event.

32. A sensory event recoding system comprising:

sensor inputs that are configured to transmit a first pattern of sensory activity; and

simulated neural elements having one or more branches that are impinged by different subsets of the sensor inputs, the branches being configured to activate in response to activity in the impinging sensor inputs, and the simulated neural elements being configured to generate a second pattern of activity that represents a recoding of the first pattern of sensory activity by activating in response to activity in their input branches.

33. The system of claim 32 further comprising sensory feature sensors configured to detect different sensory features and generate the first pattern of sensory activity, wherein:

the first pattern of sensory activity is representative of detected sensory features, and

the sensor inputs are further configured to receive the first pattern of sensory activity from the sensory feature sensors.

34. The system of claim 32 further comprising a sensory event decoder configured to recognize the second pattern of activity in the simulated neural elements and to identify a particular sensory event that corresponds to the recognized second pattern of activity.

35. The system of claim 34 wherein:

the branches configured to activate in response to activity in the impinging sensor inputs comprise branches configured to activate in response to a combination of activity in the impinging sensor inputs exceeding a first threshold activity level during a first specified window of time; and

the simulated neural elements configured to generate a second pattern of activity comprise simulated neural elements configured to activate in response to a combination of activity in their branches exceeding a second threshold activity level during a second specified window of time.

36. The system of claim 35 wherein the first threshold activity level is the same for every branch and the second threshold activity level is the same for every simulated neural element.

37. The system of claim 35 wherein the first threshold activity level varies for different branches and the second threshold activity level varies for different simulated neural elements.

38. The system of claim 34 wherein:

the simulated neural elements configured to activate in response to activity in their input branches comprise simulated neural elements configured to determine a number of their branches that are active during a specified window of time and to activate in response to the number of their branches that are active during the specified window of time exceeding a threshold number of active branches.

39. The system of claim 38 wherein the threshold number of active branches is the same for every simulated neural element.

40. The system of claim 38 wherein the threshold number of active branches varies for different simulated neural elements.

41. The system of claim 34 wherein:

the decoder comprises a plurality of output units, wherein each output unit corresponds to a known category of sensory objects, is coupled to the simulated neural elements known to activate in response to the known category of objects, and is configured to be activated by activity in the simulated neural elements to which it is coupled; and

the decoder is configured to identify the particular category of sensory objects by identifying a particular output unit with the greatest response to the second pattern of activity in the plurality of simulated neural elements.

42. The system of claim 34 wherein the sensory feature sensors comprise olfactory sensory feature sensors configured to detect different olfactory sensory features and to generate a first pattern of sensory activity that is representative of olfactory features detected in response to exposing an odorant to the sensory feature sensors;

and

the sensory event decoder is configured to recognize the second pattern of activity in the simulated neural elements and to identify the odorant based on the second pattern of activity.

43. The system of claim 34 wherein the sensory feature sensors comprise visual sensory feature sensors configured to detect different visual sensory features and to generate a first pattern of sensory activity that is representative of an object exposed to the sensory feature sensors;

and

the sensory event decoder is configured to recognize the second pattern of activity in the simulated neural elements and to identify the object based on the second pattern of activity.

44. The system of claim 34 wherein:

the sensor inputs are configured to transmit a first pattern of sensory activity that represents a set of sensory features associated with a sensory event; and

the neural elements are configured to generate a second pattern of activity that represents a recoding of the first pattern of sensory activity based on higher order features associated with the sensory event, the higher order features associated with the sensory event signifying the joint presence of one or more subsets of sensory features within the set of sensory features associated with the sensory event.

45. A method for generating a neural circuit for recoding sensory events, the method comprising:

generating sensor inputs, some subset of which is activated by each sensory event;

generating simulated neurons having branches;

connecting different subsets of sensor inputs to individual branches of the simulated neurons;

configuring each branch to activate in response to one or more combinations of activity in the sensor inputs that impinge the branch; and

configuring each simulated neuron to activate in response to one or more combinations of activity in the simulated neuron'"'"'s branches.

46. The method of claim 45 wherein connecting different subsets of sensor inputs to the individual branches of the simulated neurons comprises randomly connecting individual sensor inputs to individual branches of the simulated neurons.

47. The method of claim 45 wherein connecting different subsets of sensor inputs to the individual branches of the simulated neurons comprises connecting the sensor inputs to the individual branches of the simulated neurons in a self-organizing, activity-dependent manner.

48. A sensory event recognition system comprising:

sensors configured to transmit a first pattern of sensory activity; and

simulated neural elements incorporating branches that are coupled to subsets of the sensors, the simulated neural elements being configured to receive the first pattern of sensory activity from the sensors at the branches and to recode the first pattern of sensory activity into a second pattern of active and inactive simulated neural elements.

49. A method for recoding a sensory event based on higher order features of the sensory event, the method comprising:

receiving, at a neural circuit that includes one or more branched neuronal units, an input pattern of sensory activity, the input pattern of sensory activity representing sensory features associated with a sensory event;

inputting the received input pattern of sensory activity to the neural circuit; and

using the neural circuit to recode the input pattern of sensory activity into a neural representation of the input pattern of sensory activity, the neural representation of the input pattern representing a combination of higher order features associated with the sensory event, the higher order features associated with the sensory event signifying the joint presence of particular sensory features associated with the sensory event.

50. The method of claim 49 wherein using the neural circuit to recode the input pattern of sensory activity into a neural representation of the input pattern of sensory activity comprises recognizing the higher order features associated with the sensory event by identifying concurrent activity in subsets of branches of the branched neural elements, the concurrent activity in the subsets of branches of the branched neural elements signifying the joint presence of the particular sensory features associated with the sensory event.