Dynamical brain model for use in data processing applications

• US 20040073415A1
• Filed: 09/23/2003
• Published: 04/15/2004
• Est. Priority Date: 02/23/2001
• Status: Active Grant

• Current Assignee: Pennsylvania University of The Trustees of The
• Original Assignee: Pennsylvania University of The Trustees of The
• Inventors: Farhat, Nabil H
• Granted Patent: US 7,398,256 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06N 3/02 Neural networks

Abstract

A system and methods offering a dynamical model of cortical behavior is provided. In an illustrative implementation, the present invention offers a corticonic network comprising at least one parametrically coupled logistic map network (PCLMN)(205). The PCLMN offers a non-linear iterative map of cortical modules (or netlets) that when executed exhibit substantial cortical behaviors. The PCLNM accepts dynamic and/or static spatio-temporal input (210) and determines a fixed point attractor in state-space for that input. The PCLM (205) operates such that if the same or similar dynamic and/or static spatio-temporal input is offered over several iterations, the PCLMN converges to the same fixed point attractor is provided rendering adaptive learning. Further, the present invention contemplates the memorization or association of inputs using the corticonic network in a configuration where the PCLNM cooperates with another cortical module model (e.g. another PCLMN, associative memory module, etc.)(215).

Claims

1. A method to simulate cortical processing comprising the steps of:

segmenting the cortex into cortical columns, said cortical columns communicating with each other via short-range and longer-range communications paths; and

generating at least one parametrically coupled logistic map network (PCLMN) to describe these communication paths, said PCLMN comprising one or more parametrically couple logistic maps.

2. The method as recited in claim 1, further comprising:

configuring the PCLMN to perform acts comprising any of;

(a) handling dynamic and/or static input patterns, (b) handling large numbers of sensory stimuli, and (c) providing a solution to the plasticity-stability problem to memory formation.

3. The method as recited in claim 1, further comprising:

providing at least one dynamic and/or static input to said at least one PCLMN; and

processing said at least one dynamic and/or static input by said PCLMN to determine a fixed-point attractor for said at least one dynamic and/or static input in state-space.

4. The method as recited in claim 3, further comprising:

offering said at least said attractor by said PCLMN for similar or same said at least one dynamic and/or static input, said PCLMN engaging in adaptive learning to offer said attractor.

5. The method as recited in claim 4, further comprising:

providing at least one additional cortical model module to cooperate with said at least one PCLMN to perform memorization and/or association operations, said at least one additional cortical module comprising any of a PCLMN and associative memory.

6. The method as recited in claim 5, further comprising:

providing a feedback loop to allow for the design and implementation of desired state-space trajectories, said feedback loop originating from the output of said at least one additional cortical model module and terminating at the input of said at least one PCLMN.

7. The method as recited in claim 6 further comprising:

labeling said determined attractor by said at least one additional cortical model module for association and storage.

8. The method as recited in claim 4, wherein said at least dynamic and/or static inputs comprise dynamic and/or static spatio-temporal stimuli.

9. The method as recited in claim 8, wherein said dynamic and/or static spatio-temporal stimuli originate from at least one filter, said filter comprising any of an electronic filter, a partially-electronic filter, and a biological filter.

10. A computer readable medium having computer readable instructions to perform the steps recited in claim 1.

11. A system to model cortical processing comprising:

at least one parametrically coupled logistic map network (PCLMN), said PCLMN providing a dynamic and/or static non-linear representation of cortical connections as observed in a biological cortex,

wherein said at least one PCLMN accepts at least one dynamic and/or static input and processes said dynamic and/or static input to identify a fixed-point attractor for said at least one dynamic and/or static input in state space,

and wherein said at least one PCLMN operates to adapt providing the same output to similar or same inputs thereby engaging in learning.

12. The system as recited in claim 11, wherein said at least one dynamic and/or static input comprises at least one spatio-temporal stimulus.

13. The system as recited in claim 11, wherein said adaptation of said at least one PCLMN occurs through few iterations of inputs.

14. The system as recited in claim 11, further comprising:

at least one additional cortical model module, said at least one additional cortical model module accepting the output of said at least one PCLMN as input and is capable of classifying in real time said output of said at least one PCLMN as being novel simulating memorization and/or association cortical functions.

15. The system as recited in claim 14, wherein said at least one additional cortical model module comprises any of a PCLMN and associative memory

16. The system as recited in claim 15, wherein said at least one PCLMN and said at least one additional cortical model module are tandemly coupled.

17. The system as recited in claim 16, wherein said at least one PCLMN and said at least one additional cortical model operate synchronously.

18. The system as recited in claim 16 further comprising:

a feedback loop originating from the output of said at least one additional cortical model module and terminating at the input of said at least one PCLMN, wherein said feed back loop allowing for the design and implementation of desired state-space trajectories.

19. A method to model the cortex comprising:

providing a corticonic network, said corticonic comprising at least one PCLMN operating on at least one dynamic and/or static input, said at least one dynamic and/or static input comprising saptio-temporal stimulus; and

processing said at least one dynamic and/or static input by said corticonic network to provide a fixed point attractor in state space for said at least one dynamic and/or static input.

20. The method as recited in claim 19, further comprising:

performing memorization and/or association on said outputs of said corticonic network, said corticonic network engaging in memorization through the use of at least one additional PCLMN tandemly coupled to said at least one PCLMN and operating to classify novel output from said at least one PCLMN.

21. A system modeling the cortex comprising:

a corticonic network,

wherein said corticonic network comprises a first PCLMN, said first PCLMN accepting dynamic and/or static inputs and determining a fixed point attractor in state-space to describe at least one dynamic and/or static input, and

wherein said corticonic network comprises a second PCLMN, said second PCLMN tandemly coupled to said first PCLMN to accept the output of said first PCLMN as input and operates to identify non-novel output of said first PCLMN, said identification of non-novel output representative of memorization and/or association cortical functions, and

wherein a feedback loop operates between the output of said second PCLMN and the input of said first PCLMN.

22. The system as recited in claim 21, wherein said corticonic network comprises a computing environment.

23. The method as recited in claim 21, wherein said second PCLMN exhibits associative memory attributes to label and associate attractors resulting from said first PCLMN.

24. A method to apply a dynamical brain model to data processing applications comprising the steps of:

identifying a data processing problem;

modeling the data processing problem in a corticonic network;

configuring said corticonic network to the parameters of said data processing problem; and

executing said corticonic network.

25. The method as recited in claim 24, further comprising:

obtaining results from said execution of said corticonic network; and

verifying said results of said execution of said corticonic network by comparing said results with a comparable real-world model.

26. A computer readable medium comprising computer readable instructions to perform the steps recited in claim 24.