Method, System and Computer Program Product for Controlling Complex Rhythmic Systems

• US 20100198090A1
• Filed: 06/27/2008
• Published: 08/05/2010
• Est. Priority Date: 06/28/2007
• Status: Active Grant

• Current Assignee: University of Virginia
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Kiss, István Z., Hudson, John L., Kori, Hiroshi, Rusin, Craig G.
• Granted Patent: US 8,463,374 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/4041 Evaluating nerves condition
• CPC Class Codes: A61N 1/36025 for treating a mental or cerebral condition
• CPC Class Codes: A61N 1/36031 using physiological parameters for adjustment
• CPC Class Codes: A61N 1/3605 Implantable neurostimulators for stimulating central or peripheral nerve system
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: A61N 1/36135 using physiological parameters
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method, apparatus, and computer program product for controlling the behavior of complex rhythmic systems, such as the nervous system, is disclosed. Robust, engineering based measurements of the rhythmic system are taken and used to generate feedback that guides the system towards a desired state. This methodically designed feedback allows the system to maintain the normal behavior of individual elements, thereby achieving high effectiveness, while at the same time minimizing side effects. It therefore becomes possible to move the system towards the desired state in a non-destructive manner. This approach also allows for increased flexibility and applicability because the feedback is generated in a manner that is tailored to the unique variables of the particular system.

Claims

1. A method of engineering the emergent behavior of a target system having a population of rhythmic elements, comprising:

measuring the dynamic properties of the rhythmic elements;

using a reduced model to determine the target dynamical behavior of the system; and

determining the perturbation that produces said target behavior.

2. The method of claim 1, wherein said perturbation comprises feedback, coupling, or forcing, or any combination thereof.

3. The method of claim 1, wherein said dynamic properties are comprised of the phase behavior of said target system.

4. The method of claim 1, wherein said emergent behavior may comprise, but is not limited to, the following behaviors:

synchronization, desynchronization, phase clustering, propagating waves, multi-cluster states, itinerant clustering, or other spatio-temporal phenomena.

5. The method of claim 1, wherein said measuring of the dynamic properties of the rhythmic elements could be performed through perturbation.

6. The method of claim 5, wherein said perturbation comprises using a pulse at every point along a rhythmic element'"'"'s cycle.

7. The method of claim 5, wherein said perturbation comprises using feedback on one or more elements of the population.

8. The method of claim 1, wherein said measuring the dynamic properties of the rhythmic elements could be performed using two or more elements that are desynchronized but interacting and measuring their frequency shift as a function of their phase difference.

9. The method of claim 1, wherein said measuring the dynamic properties of the rhythmic elements could be performed using a single oscillator.

10. The method of claim 9, wherein said oscillator is used with delayed self-feedback.

11. The method of claim 1, wherein said engineering method is performed using an externally, internally, or any combination thereof, relative to the target system.

12. The method of claim 11, wherein said external performance is performed by external devices and said internal performance is performed by internal devices.

13. The method of claim 1, wherein said reduced model comprises at least one of the following phase models:

general dynamical models, Kuramoto Phase models, Winfree models, finite pulse models, integrate-and-fire models, and quadratic-integrate-and-fire models, or any combination thereof.

14. The method of claim 1, where said perturbation comprises taking in a set of individual or aggregated signal measurements in order to construct a feedback loop.

15. The method of claim 14, wherein said determining said feedback loop comprises at least one of pre-processing, processing, and post processing steps.

16. The method of claim 15, wherein said determining said preprocessing comprises using data filters and smoothing algorithms, said processing comprises using a linear or nonlinear transformation function with adjustable parameters, and said post-processing comprises adjusting the output of the processing step to match the conditions of the target system.

17. The method of claim 15, where said preprocessing comprises a nonlinear mathematical transformation.

18. The method of claim 17, where said nonlinear mathematical transformation is a time-delayed polynomial.

19. The method of claim 18, wherein said time-delayed polynomial is of the form

$f$

( x _ ) = K N 

∑

j = 1 N 

∑

i = 0 S 

k i 

x j 

( t - τ

i ) i

where x_j is the j^th measured signal at time t such as potential vs. time (EEG/EKG), N is the number of signals measured, S is the feedback order, k_i is the i^th polynomial coefficient, τ

_i is the time-delay of the i^th polynomial term, and K is the overall gain.

20. The method of claim 14, where said feedback loop comprises one or more signals that are applied in either a uniform or non-uniform manor to the targeted area of the population.

21. The method of claim 1, wherein said rhythmic elements comprises oscillators.

22. The method of claim 21, wherein said oscillators comprise at least one of the following:

electrochemical oscillators, general chemical oscillators, pH oscillators, Coherent EM sources or sinks, self-sustaining physical resonators, radio antenna arrays, electron spin, neurons, general biological cells, cardiac muscle, or any combination or grouping thereof.

23. The method of claim 22, wherein said Coherent EM sources or sinks comprise lasers or microwave sources.

24. The method of claim 22, wherein said general biological cells comprise protein and gene expression within those cells.

25. The method of claim 22, wherein said oscillators comprise a number of individual oscillators.

26. The method of claim 25, wherein said individual oscillators comprise a neural cluster (i.e. a group of individual neurons).

27. The method of claim 21, wherein said oscillators comprise, but are not limited to, elements which undergo periodic or aperiodic fluctuations of physical quantities

28. The method of claim 27, wherein said fluctuations of physical quantities comprise those found in:

brain waves, laser elements, microwave elements, electrochemical elements, neurons, and cardiac muscle.

29. The method of claim 1, wherein said rhythmic elements are components of rhythmic systems.

30. The method of claim 29, wherein said rhythmic systems comprise those involved in the collective behavior of the target system.

31. The method of claim 30, wherein said collective behavior comprises at least one of the following:

circadian rhythms, olfactory sensations, cardiac fibrillation, and cognitive processes, and tremors.

32. The method of claim 31, where said cognitive processes comprise those related to memory and visual perception.

33. The method of claim 31, where said tremors comprise Parkinson'"'"'s disease-related tremors, epileptic seizures, and essential tremors.

34. An engineering system for engineering the emergent behavior of a target system having a population of rhythmic elements, comprising:

a measuring means for measuring the dynamic properties of the rhythmic elements;

a computer processor, wherein said computer processor is adapted for;

using a reduced model to determine the target dynamical behavior of the system, and

determining the perturbation that produces said target behavior.

35. The engineering system of claim 34, wherein said processor comprises either an ASIC processor or FPGA.

36. The engineering system of claim 34, wherein said measuring means comprises optical sensors, voltage meters, current meters, pH meters, EEG, EKG, MEG, Ohm meters, magnetic flux meters, electric field meters, thermometers, patch clamps, neural electrodes, electrode arrays, or any combination thereof.

37. The engineering system of claim 34, wherein said emergent behavior may comprise, but is not limited to, the following behaviors:

synchronization, desynchronization, phase clustering, propagating waves, multi-cluster states, itinerant clustering, or other spatio-temporal phenomena.

38. The system of claim 34, wherein said measuring of the dynamic properties of the rhythmic elements could be performed through perturbation.

39. The system of claim 38, wherein said perturbation comprises using a pulse at every point along a rhythmic element'"'"'s cycle.

40. The system of claim 38, wherein said perturbation comprises using feedback on one or more elements of the population.

41. The system of claim 34, wherein said measuring the dynamic properties of the rhythmic elements could be performed using two or more elements that are desynchronized but interacting and measuring their frequency shift as a function of their phase difference.

42. The system of claim 34, wherein said engineering system uses an external device, internal device, or any combination thereof.

43. The system of claim 42, wherein said external or internal device comprises:

one or more input electrodes, one or more output electrodes, embedded control logic and memory, power management systems, and one or more external interfaces.

44. The system of claim 43, where said external interfaces are comprised of onboard interfaces, wired interfaces, or wireless interfaces.

45. The system of claim 34, wherein said reduced model comprises at least one of the following phase models:

general dynamical models, Kuramoto Phase models, Winfree models, finite pulse models, integrate-and-fire models, and quadratic-integrate-and-fire models, or any combination thereof.

46. The system of claim 34, where said feedback comprises taking in a set of individual or aggregated signal measurements in order to construct a feedback loop.

47. The system of claim 46, wherein said determining said feedback further comprises preprocessing said taken in set of individual or aggregated signal measurements if necessary for the construction of said feedback.

48. The system of claim 47, where said preprocessing comprises a nonlinear mathematical transformation.

49. The system of claim 48, where said nonlinear mathematical transformation is a time-delayed polynomial.

50. The system of claim 49, wherein said time-delayed polynomial is of the form

$f$

( x _ ) = K N 

∑

j = 1 N 

∑

i = 0 S 

k i 

x j 

( t - τ

i ) i

where x_j is the j^th measured signal at time t such as potential vs. time (EEG/EKG), N is the number of signals measured, S is the feedback order, k_i is the i^th polynomial coefficient, τ

_i is the time-delay of the i^th polynomial term, and K is the overall gain.

51. The system of claim 46, where said feedback loop comprises one or more signals that are applied in either a uniform or non-uniform manor to the targeted area of the population.

52. The system of claim 34, wherein said rhythmic elements comprises oscillators.

53. The system of claim 52, wherein said oscillators comprise at least one of the following:

circadian rhythms, olfactory sensations, cardiac fibrillation, and cognitive processes, and tremors.

54. The system of claim 53, where said cognitive processes comprise those related to memory and visual perception.

55. The system of claim 54, where said tremors comprise Parkinson'"'"'s disease-related tremors, epileptic seizures, and essential tremors.

56. The system of claim 52, wherein said oscillators comprise, but are not limited to, elements which undergo periodic or aperiodic fluctuations of physical quantities

57. The system of claim 56, wherein said fluctuations of physical quantities comprise those found in:

brain waves, laser elements, microwave elements, electrochemical elements, neurons, and cardiac muscle.

58. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system for engineering the emergent behavior of a target system having a population of rhythmic elements, said engineering the emergent behavior method of said computer program logic comprising:

receiving data from measuring means for measuring the dynamic properties of the rhythmic elements;

using a reduced model to determine the target dynamical behavior of the system, and

determining the perturbation that produces said target behavior.