Cerebral programming
View Patent ↗A method of training a biological neural network using a controller, comprising: applying a cycle comprising: stimulating a neural network by said controller applying at least an input signal to the network; detecting an output response of the network by said controller; and modifying said stimulation by said controller for at least a period of time if said response matches a desired at least approximate response; and repeating said cycle of stimulation, detection and modification at least one more time until said neural network is trained to generate a desired output response for said input signal.
1. A method of training an in-vivo biological neural network in a living human using a controller, comprising:
applying a cycle comprising:
stimulating a neural network by said controller applying at least an input signal to the network;
detecting an output response of the network by said controller; and
modifying said stimulation by said controller for at least a period of time if said response matches a desired at least approximate response; and
repeating said cycle of stimulation, detection and modification at least one more time until said neural network is trained to generate a desired output response for said input signal.
2. A method according to claim 1 , wherein said input signal is a specific signal and wherein stimulating comprises applying a less specific stimulation.
3. A method according to claim 1 , wherein said modifying comprises modifying said input signal.
4. A method according to claim 1 , wherein said modifying comprises stopping said stimulation.
5. A method according to claim 1 , wherein said modifying is applied in a manner known to affect a stability of connections in said network.
6. A method according to claim 1 , wherein said modifying is applied in a manner known to affect a learning behavior of said neurons of said network.
7. A method according to claim 1 , wherein said stimulating comprises stimulating using a magnetic field stimulator.
8. A method according to claim 1 , wherein said stimulating comprises stimulating using an electric current.
9. A method according to claim 1 , wherein said modifying comprises applying a chemical agent.
10. A method according to claim 1 , wherein said modifying comprises applying an electric field.
11. A method according to claim 1 , comprising modifying said method by said controller in response to a response of said network to said method.
12. A method according to claim 1 , wherein said desired output response is a particular defined response.
13. A method according to claim 1 , wherein said desired output response is a random response pattern indicative of an unlearning by said network.
14. A method according to claim 1 , wherein said desired output response is a spatial shift in a portion of said network that responses to said input, from a previous responding portion to a shifted responding portion.
15. A method according to claim 1 , comprising changing said input pattern during a repetition cycle.
16. A method according to claim 1 , comprising increasing a resolution of said input pattern between repetition cycles.
17. A method according to claim 1 , comprising requiring an output response with a reduced range of acceptable values in subsequent repetition cycles.
18. A method according to claim 1 , wherein repeating comprises repeating until an area of said network is organized to act as an input interface for said network for direct setting of network values not via a network's standard inputs.
19. A method according to claim 1 , wherein repeating comprises repeating until an area of said network is organized to act as an output interface for said network for direct receiving of network values not via a network's standard outputs.
20. A method according to claim 1 , comprising:
providing a complex output response pattern to inputs to be trained into said network;
dividing said response pattern into sub-patterns;
training said sub-patterns individually; and
linking said sub-patterns.
21. A method according to claim 1 , comprising training a desired output response pattern to inputs as a whole into said network.
22. A method according to claim 1 , wherein said network is a GI motor complex.
23. A method according to claim 1 , wherein said network is a nervous plexus.
24. A method according to claim 1 , wherein said network is comprised in a brain.
25. A method according to claim 24 , wherein said controller replaces at least one natural learning related function of said brain.
26. A method according to claim 24 , wherein said output is measured on said brain.
27. A method according to claim 24 , wherein said output is measured as a response of said human.
28. A method according to claim 24 , wherein said input is provided directly to said brain.
29. A method according to claim 24 , wherein said stimulation is provided directly to said brain.
30. A method according to claim 24 , wherein said stimulation is provided to said brain via natural senses.
31. A method according to claim 24 , wherein said input is provided to said brain via natural senses.
32. A method according to claim 24 , wherein modifying said stimulation comprising controlling the propagation of a signal inside said brain.
33. A method according to claim 24 , wherein said method is used to rehabilitate an old function of said network.
34. A method according to claim 24 , wherein said method is used to teach a new function to said network.
35. A method according to claim 24 , wherein said method is used to teach new motor programs to said network.
36. A method according to claim 24 , wherein said method is used to create a new pathway in said brain.
37. A method according to claim 24 , wherein said method is used to create a new function area in said brain.
38. A method according to claim 24 , wherein said method is used to remap a function from one brain area to another in said brain.
39. A method according to claim 24 , wherein said method is used to erase a learned pattern from said brain.
40. A method according to claim 24 , wherein said method is used to create a digital logic functioning area in said brain.
41. A method according to claim 24 , wherein said method is used to train the brain in the use of an artificial organ.
42. A method according to claim 41 , wherein said organ is a replacement organ.
43. A method according to claim 41 , wherein said organ is a new organ not corresponding to a previous organ controlled by said brain.
44. A method according to claim 24 , wherein said human is not conscious during the application of said method.
45. A method according to claim 24 , wherein said human is not in control of learning processes imposed by said method.
46. A method according to claim 24 , comprising not motivating said human in response to the output.
47. A method according to claim 24 , wherein said human reports said output.
48. A method according to claim 24 , wherein said human generates said input.
49. A method according to claim 24 , comprising performing actions by said human to capture previously trained un-associated input-output response patterns.
50. A method according to claim 24 , wherein said input is an input internal to said brain.
51. A method according to claim 24 , wherein said output is an output internal to said brain.
52. A method according to claim 24 , wherein said output comprises controlling an epilepsy attack.
53. A method according to claim 24 , wherein said output comprises reducing a sensation of pain.
54. A method of training a biological neural network using a controller, comprising:
applying a cycle comprising:
stimulating a neural network by said controller applying at least an input signal, which is a localized signal, to the network, wherein said stimulating comprises applying a less localized stimulation;
detecting an output response of the network by said controller, and
modifying said stimulation by said controller for at least a period of time if said response matches a desired at least approximate response; and
repeating said cycle of stimulation, detection and modification at least one more time until said neural network is trained to generate a desired output response for said input signal.
55. A method according to claim 54 , wherein said modifying comprises reducing said less localized stimulation.
56. A method according to claim 54 , wherein said modifying comprises increasing said less localized stimulation.
57. A method according to claim 54 , wherein said network is an in-vivo network in a living non-human animal.
58. A method according to claim 57 , comprising:
providing a potential environmental contaminant;
repeating said method under at least two conditions of said contaminant; and
comparing a response of said network to said method to determine an effect of said contaminant on training of said animal.
59. A method according to claim 57 , comprising:
selecting a network portion of said animal that is coupled to a sensing ability of said animal; and
training said network portion to output at least an indication of a sensing by said animal.
60. A method according to claim 57 , wherein said input is a command that can be sensed by said animal and wherein said desired output is a behavioral response of said animal.
61. A method according to claim 60 , wherein said input and said output are inter-related using a complex logic, including at least two logic steps.
62. An animal trained according to the method of claim 57 .
63. A method of training an in vitro biological neural network using a controller, comprising:
applying a cycle comprising:
stimulating a neural network by said controller applying at least an input signal to the network;
detecting an output response of the network by said controller; and
modifying said stimulation by said controller for at least a period of time if said response matches a desired at least approximate response; and
repeating said cycle of stimulation, detection and modification at least one more time until said neural network is trained to generate a desired output response for said input signal.
64. A method according to claim 63 , wherein said network is grown in a container.
65. A method according to claim 63 , wherein said network is excised from a living body to a container.
66. A method according to claim 63 , comprising:
providing a potential environmental contaminant;
repeating said method under at least two conditions of said contaminant; and
comparing a response of said network to said method to determine an effect of said contaminant on training of said network.