METHOD FOR INCLUSION OF PSYCHOLOGICAL TEMPERAMENT IN AN ELECTRONIC EMULATION OF THE HUMAN BRAIN

• US 20080300841A1
• Filed: 06/21/2006
• Published: 12/04/2008
• Est. Priority Date: 01/06/2004
• Status: Active Grant

• Current Assignee: Neuric Technologies, LLC
• Original Assignee: Neuric Technologies, LLC
• Inventors: Visel, Thomas A.
• Granted Patent: US 7,849,034 B2
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/105 Shells for specifying net layout
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G10L 17/26 Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices

Abstract

A method of emulating the human brain with its thought and rationalization processes is presented here, as well as a method of storing human-like thought. The invention provides for inclusion of psychological profiles, experience and societal position in an electronic emulation of the human brain. This permits a realistic human-like response by that emulation to the people and the interactive environment around it.

Claims

1-11. -11. (canceled)

12. A method of emulating human cognition in a brain model containing a plurality of electronically represented neurons, comprising:

providing a context pool memory having a set of neurons corresponding to a present situational context;

providing a reinforcement memory having a set of neurons corresponding to experiential relationships; and

providing a long term memory having a set of neurons corresponding to experiential relationships and committed to long-term storage;

wherein the neurons of the reinforcement memory are reflective of the neurons presently or formerly contained in the context pool and the neurons of the reinforcement memory are placed in the long term memory based upon continued activity in the reinforcement memory.

13. The method of claim 12, further comprising providing a list memory containing a set of neurons corresponding to completion of motor skills tasks.

14. The method of claim 12, further comprising copying the contents of the long term memory and reinforcement memory.

15. The method of claim 14, further comprising placing the copied contents of the long term memory and the reinforcement memory into a second brain model having a substantially similar structure to that of the first including a context pool memory, a reinforcement memory, and a long term memory.

16. The method of claim 12, further comprising holding neurons in the reinforcement memory for a predetermined time period to determine a reinforcement of the neuron.

17. The method of claim 16, further comprising selecting at least one neuron based upon a level of a reinforcing activity of the at least one neuron in the reinforcement memory during the predetermined time period and placing the at least one neuron into long term memory.

18. The method of claim 17, wherein the reinforcing activity includes at least one of an emotional event, a stressful event, a traumatic event, and a repeated reference to the at least one neuron over a predetermined period of time.

19. The method of claim 12, further comprising removing a neuron from the context pool when a firing of the neuron falls below a predetermined level.

20. An electronic brain model based upon a set of electronic neurons, each neuron optionally associated with a parameter, and taking on a parameter value from 0 to 100%, the model comprising:

a context pool memory containing contextual data and a set of neuron pointers, each neuron pointer addressing a neuron corresponding to a present situational context;

a reinforcement memory containing a set of neurons corresponding to experiential relationships;

a long term memory containing a set of neurons corresponding to experiential relationships and committed to long-term storage; and

an analyzer and correlator that creates new neurons in the reinforcement memory and promotes at least one neuron in the reinforcement memory to permanent memory based upon a predetermined activity level in the at least one neuron.

21. The electronic brain model of claim 20, wherein the analyzer and correlator removes a neuron pointer from the context pool memory after a firing level of the neuron addressed by the pointer falls below a predetermined level.

22. The electronic brain model of claim 21, wherein the analyzer and correlator removes contextual data from the context pool corresponding to neurons whose associated neuron pointers have been removed from the context pool memory.

23. The electronic brain model of claim 21, wherein the context pool memory is sized to accommodate the number of neurons and an amount of contextual data required for operation for approximately one day.

24. The electronic brain model of claim 20, wherein the analyzer and correlator reduces a firing level of a neuron substantially continuously with time and increases a firing level of a neuron when the neuron is referenced.

25. The electronic brain model of claim 24, wherein the analyzer and correlator removes data from the context pool memory that is associated with a neuron whose firing level has been reduced below a predetermined threshold.

26. The electronic brain model of claim 20, wherein the analyzer and correlator reduces a firing level of a neuron as a function of time and increases a firing level of a neuron when the neuron is referenced.

27. The electronic brain model of claim 20, wherein the context pool has a size corresponding to a number of neurons firing and an amount of contextual data in a wake/sleep cycle, the wake/sleep cycle being about one day long.

28. The electronic brain model of claim 20, further comprising an index containing pointers to all electronic neurons of the brain model and operable to reuse indices when a neuron is permanently deleted.

29. The electronic brain model of claim 28:

wherein the index comprises a set of unique serial numbers; and

wherein each neuron in the reinforcement memory has a unique serial number having a first sign associated therewith and each neuron in the long term memory has a unique serial number with a second sign associated therewith, the first and second signs alternately occupying a bit collocated with the unique serial number.

30. The electronic brain model of claim 20, further comprising a clutter filter operable to accept input from multiple sources and determine whether such input is placed into the context pool based upon predetermined criteria.

31. The electronic brain model of claim 20, further comprising a clutter filter operable to accept input from multiple sources and determine whether such input is placed into the context pool based upon configurable criteria.

32. The electronic brain model of claim 20, further comprising a clutter filter operable to accept input from multiple sources and determine whether such input is placed into the context pool based upon criteria expressed as a parameter associated with one or more neurons of the brain model.

33. The electronic brain model of claim 20, further comprising a clutter filter operable to accept input from multiple sources and determine whether such input is placed into the context pool based upon a temperament associated with the brain model.

34. The electronic brain model of claim 20, further comprising a clutter filter operable to accept input from multiple sources and determine whether such input is placed into the context pool based upon personal preferences associated with the brain model.

35. The electronic brain model of claim 20, further comprising an event memory containing electronic neurons held in waiting for a predetermined event.

36. The electronic brain model of claim 20:

wherein a plurality of propensities to a plurality of traits are specified based upon at least one of choleric, melancholy, sanguine, and phlegmatic temperaments, and combinations thereof, each temperament being specified by at least one parameter in the electronic brain model; and

wherein upon occurrence of a predetermined, traumatic event, the plurality of propensities reverts to a predetermined temperament, the predetermined temperament including at least one of choleric, melancholy, sanguine, and phlegmatic temperaments, and combinations thereof.

37. The electronic brain model of claim 20, wherein the parameter value associated with at least one of the electronic neurons is derived from the parameter values of a plurality of other electronic neurons.

38. The electronic brain model of claim 20, wherein the parameter value of at least one of the electronic neurons is directly accessible from outside of the electronic brain model.

39. A method of emulating human cognition in a brain model containing a plurality of electronically represented neurons, comprising:

providing a context pool memory containing contextual data and set of neuron pointers, each neuron pointer addressing a neuron corresponding to a present situational context;

providing a reinforcement memory containing a set of neurons corresponding to experiential relationships;

providing a long term memory containing a set of neurons corresponding to experiential relationships and committed to long-term storage; and

providing an analyzer and correlator that creates new neurons in the reinforcement memory and promotes at least one neuron in the reinforcement memory to permanent memory based upon a predetermined activity level in the at least one neuron.

40. The method of claim 39, further comprising providing a clutter filter for determining which inputs from a plurality of inputs will be placed into the context.

41. The method of claim 40, further comprising updating a state of the brain model by providing a set of inputs directly into the context pool, bypassing the clutter filter.

42. The method of claim 40, wherein the clutter filter determines which inputs from a plurality of inputs will be placed into the context pool based upon a set of predefined brain parameter neurons with preset weights and connections.

43. The method of claim 40, further comprising copying the long term memory into an archival medium

44. The method of claim 40, further comprising removing neurons from the reinforcement memory when activity in the neurons has fallen below a predetermined average level for nominally 21 days.

45. A system for storage of a set of electronic neurons in an electronic memory, the structure comprising:

an identification table containing a list of pointers as table elements, each of the pointers having a resolution address of an electronic neuron in a neuron table; and

a neuron table containing at least one electronic neuron having a neuron record, the neuron record being locatable in the neuron table through its associated resolution address and an offset;

wherein the identification table occupies a predetermined and finite logical space in the electronic memory and is sized to have one table element for each electronic neuron in the neuron table.

46. The system of claim 45, wherein the neuron record further comprises:

a fixed length information block; and

at least one relational connection record that references at least one of a second electronic neuron and at least one of a state parameter and a predefined brain parameter neuron with preset weights and connections.

47. The system of claim 46, wherein the relational connection record is weighted.

48. The system of claim 46, wherein the relational connection record is uni-directional.

49. The system of claim 46, wherein the relational connection record is bi-directional.

50. The system of claim 46, wherein the at least one electronic neuron is activated based upon at least one of a second neuron, a state parameter referenced in the at least one relational connection record, and a predefined brain parameter neuron with preset weights and conditions.

51. The system of claim 46, wherein the at least one electronic neuron is activated based upon a relative ranking of the at least one of a second neuron and a state parameter referenced in the at least one relational connection record as compared to a rank associated with the at least one electronic neuron.

52. The system of claim 45, wherein the at least one electronic neuron corresponds to one fact.

53. The system of claim 45, wherein the at least one electronic neuron corresponds to one experience.

54. A method of using a plurality of electronic brain models to emulate one or more teams of human minds, the method comprising:

uploading intelligence into each of the plurality of electronic brain models to emulate members of a team;

providing access between each of the plurality of electronic brain models via a local network thereby forming a first team;

providing a gateway for the first team, the gateway provides access to a second team via a remote network; and

enabling a simulation director to coordinate between the first and second team, the simulation director communicating with the first and second team via first and second network ports, respectively, on the remote network.

55. A method of operating an electronic brain model, the electronic brain model having a plurality of electronic neurons each associated with a parameter value from 0 to 100%, each of the plurality of neurons residing in either a reinforcement memory or a long term memory, the neurons of the reinforcement memory corresponding to experiential relationships, the neurons of the long term memory corresponding to experiential relationships and being committed to long-term storage, the electronic brain model also having a context pool memory containing contextual data and plurality of neuron pointers, each neuron pointer addressing one of the plurality of neurons, the method comprising:

promoting a neuron in the reinforcement memory to the permanent memory based upon a predetermined activity level in the neuron;

removing any one of the plurality of neurons when the any one neuron has had reinforcement falling below a predetermined level for a predetermined period of time;

removing a neuron pointer from the context pool memory after a neuron addressed by the neuron pointer has a firing level that has fallen below a predetermined threshold; and

removing contextual data from the context pool corresponding to the neuron whose associated neuron pointer has been removed from the context pool memory.

56. The method of claim 55, further comprising creating a new neuron in the reinforcement memory.