Optimizing BCPNN Learning Rule for Memory Access.

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作者： Yu,Yang ^[1] ; Dimitrios,Stathis ^[1] ; Rodolfo,Jordão ^[1] ; Ahmed,Hemani ^[1] ; Anders,Lansner ^[2]

作者单位： Division of Electronics and Embedded Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. ^[1] Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. ^[2] Department of Mathematics, Stockholm University, Stockholm, Sweden. ^[3]

关键词 Bayesian Confidence Propagation Neural Network (BCPNN)DRAM Hebbian learning cache digital neuromorphic hardware memory optimization neuromorphic computing spiking neural networks

DOI 10.3389/fnins.2020.00878

PMID 32982673

发布时间 2020-10-01

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Frontiers in neuroscience

2020年14卷

878页

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Optimizing BCPNN Learning Rule for Memory Access.

Frontiers in neuroscience

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Optimizing BCPNN Learning Rule for Memory Access.

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Frontiers in neuroscience

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