RAPID LEARNING WITH HIGH LOCALIZED SYNAPTIC PLASTICITY

A method includes selecting artificial neural network parameters; sampling the parameters; selecting connection weights; initializing the artificial neural networks; running the artificial neural networks on cognitive tasks; and determining whether activity is within an acceptable range. A computing...

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Bibliographic Details
Main Authors Freedman, David J, Masse, Nicolas Y, Rosen, Matthew C
Format Patent
LanguageEnglish
Published 16.05.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A method includes selecting artificial neural network parameters; sampling the parameters; selecting connection weights; initializing the artificial neural networks; running the artificial neural networks on cognitive tasks; and determining whether activity is within an acceptable range. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to select artificial neural network parameters; sample the parameters; select connection weights; initialize the artificial neural networks; run the artificial neural networks on cognitive tasks; and determine whether activity is within an acceptable range. A non-transitory computer-readable medium containing program instructions that when executed, cause a computer to select artificial neural network parameters; sample the parameters; select connection weights; initialize the artificial neural networks; run the artificial neural networks on cognitive tasks; and determine whether activity is within an acceptable range.
Bibliography:Application Number: US202318138015