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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| creator | Freedman, David J Masse, Nicolas Y Rosen, Matthew C |
| description | 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. |
| format | Patent |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | RAPID LEARNING WITH HIGH LOCALIZED SYNAPTIC PLASTICITY |
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