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Unsupervised Agent Based Artificial Intelligence Learning System (Ref # WFU 11-62)
Wake Forest Innovations USA flag USA
Abstract ID:
The Agent Based Brain Model (ABBM) is an artificial intelligence system that mimics the problem-solving pattern of the human brain....
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Value Proposition

Current artificial intelligence systems are developed based on previously learned or programmed responses to specific stimuli that are different from the methods used by the human brain. These systems preclude the use of emergent behavior in problem solving. Researchers have developed a device with a form of strong artificial intelligence that has demonstrated the ability to solve computational problems and exhibit emergent behaviors. This computer-based system remembers the behaviors that it has produced before and the benefits of those behaviors, while being able to interact within an environment.

Invention Summary

The Agent Based Brain Model, or ABBM, uses brain connectivity information generated from imaging modalities such as functional magnetic resonance imaging (fMRI) that enable the interference of functional interdependence between brain regions. Functional connectivity is used in establishing brain networks comprising the links between specific brain regions called nodes. Interactions between various nodes reflect human brain architecture. This innovation inputs human imaging data into the system and results in a computer-based artificial intelligence system that has the potential to produce emergent solutions in a fashion similar to the human brain.

Competitive Benefits

• ABBM develops emergent behaviors dependent on environmental experience.

• ABBM uses emergent behavior in problem solving, a characteristic essential to mimicking human brain function.

• ABBM adapts to new environments independently of external control and performs unsupervised learning without external algorithms.

Application Fields

• In a clinical research program to model brain behavior in disease states, in interaction with potential drugs, in neurological conditions, in injury and in psychological therapy

• Applications in personalized medicine for deducing expected prognosis and guiding treatment planning

• A research tool to allow the modeling of how cognitive intervention such as “brain training/altered decision making/game playing” changes brain function

Stage of Development

• ABBM has successfully utilized input connectivity data generated from fMRI data and independent decision-making tasks

• Working software for computation algorithms to run the ABBM model

• Provisional patent application filed

Background

Weak artificial intelligence systems are developed based on previously learned or programmed responses to specific stimuli, which are different from the methods used by the human brain. These systems preclude the use of emergent behavior in problem solving. The ABBM is a form of strong artificial intelligence that has demonstrated the ability to solve computational problems and exhibit emergent behaviors

Inventors

• Paul J. Laurienti, MD, PhD

• Satoru Hayasaka, PhD

• Karen Joyce

Publications

• Joyce KE, et al. A genetic algorithm for controlling an agent-based model of the functional human brain. Proceedings of the Rocky Mountain Bioengineering Symposium, Mar. 2012

• Moussa MN, et al. Changes in cognitive state alter human functional brain networks. Front Hum Neurosci.2011;5:83

• Steen M, et al. Assessing the consistency of community structure in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84: 016111

Key Words:

• Artificial Intelligence (AI)

• Agent based brain model

• Brain networks

• fMRI
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Type of Business Relationship Sought
Exclusive by Fields Licensing Arrangement
Possibility of a parallel Sponsored Research Agreement
FEATURED
Last Updated Jan 2014
Technology Type
Phase of Development PRECLINICAL
CORPORATION