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:
Dec. 27, 2022

Filed:

Aug. 08, 2019
Applicant:

Applied Brain Research Inc., Waterloo, CA;

Inventors:

Benjamin Jacob Morcos, Waterloo, CA;

Christopher David Eliasmith, Waterloo, CA;

Nachiket Ganesh Kapre, Waterloo, CA;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/16 (2006.01); G06N 3/063 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06N 3/063 (2013.01); G06F 17/16 (2013.01); G06N 3/08 (2013.01);
Abstract

The present invention relates to the digital circuits for evaluating neural engineering framework style neural networks. The digital circuits for evaluating neural engineering framework style neural networks comprised of at least one on-chip memory, a plurality of non-linear components, an external system, a first spatially parallel matrix multiplication, a second spatially parallel matrix multiplication, an error signal, plurality of set of factorized network weight, and an input signal. The plurality of sets of factorized network weights further comprise a first set factorized network weights and a second set of factorized network weights. The first spatially parallel matrix multiplication combines the input signal with the first set of factorized network weights called the encoder weight matrix to produce an encoded value. The non-linear components are hardware simulated neurons which accept said encoded value to produce a distributed neural activity. The second spatially parallel matrix multiplication combines said distributed neural activity with said second set of factorized network weights called the decoder weight matrix to produce an output signal.


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