The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 24, 2017

Filed:

Oct. 14, 2014
Applicant:

University of Tennessee Research Foundation, Knoxville, TN (US);

Inventors:

J. Douglas Birdwell, Oak Ridge, TN (US);

Catherine Schuman, Knoxville, TN (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/30 (2006.01); G06N 3/02 (2006.01); G06N 3/08 (2006.01); G06N 3/10 (2006.01); G06N 3/063 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06F 17/30312 (2013.01); G06N 3/02 (2013.01); G06N 3/049 (2013.01); G06N 3/063 (2013.01); G06N 3/0635 (2013.01); G06N 3/08 (2013.01); G06N 3/086 (2013.01); G06N 3/10 (2013.01);
Abstract

A method and apparatus for constructing a neuroscience-inspired dynamic architecture (NIDA) for an artificial neural network is disclosed. The method comprises constructing, in one embodiment, an artificial neural network embodiment in a multi-dimensional space in memory such that a neuron is connected by a synapse to another neuron. The neuron and the synapse each have parameters and have features of long-term potentiation and long-term depression. Furthermore, crossover and mutation are employed to select children of parents. Through learning, an initial network may evolve into a different network when NIDA is applied to solve different problems of control, anomaly detection and classification over selected time units. The apparatus comprises in one embodiment a computational neuroscience-inspired artificial neural network with at least one affective network coupled to receive input data from an environment and to output data to the environment.


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