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:
Jan. 10, 2023

Filed:

Dec. 05, 2017
Applicant:

Tsinghua University, Beijing, CN;

Inventors:

Luping Shi, Beijing, CN;

Jing Pei, Beijing, CN;

Lei Deng, Beijing, CN;

Zhenzhi Wu, Beijing, CN;

Guoqi Li, Beijing, CN;

Assignee:

TSINGHUA UNIVERSITY, Beijing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/063 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/0635 (2013.01); G06N 3/049 (2013.01);
Abstract

The disclosure relates to a self-adaptive leakage value neuron information processing method and system. The method includes: receiving front end pulse neuron output information; reading current pulse neuron information, wherein the current pulse neuron information includes self-adaptive membrane potential leakage information; calculating current pulse neuron output information according to the front end pulse neuron output information and the current pulse neuron information; updating the self-adaptive membrane potential leakage information according to the current pulse neuron output information; outputting the current pulse neuron output information. The self-adaptive leakage value neuron information processing system utilizes self-adaptive membrane potential leakage information to participate in calculation of current pulse neuron output information, and utilizes the calculated current pulse neuron output information to update the self-adaptive membrane potential information to participate in calculation of a next time step, and a good balance between sensitivity and stability of a neural network is achieved.


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