Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks-Reference-Cited by-同舟云学术

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Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks

Published:2021-10-22 Issue:43 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Zhang Tielin¹²^ORCID,Cheng Xiang¹²^ORCID,Jia Shuncheng¹²^ORCID,Poo Mu-ming²³⁴⁵,Zeng Yi¹²⁴^ORCID,Xu Bo¹²⁴^ORCID

Affiliation:

1. Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

3. Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China.

4. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.

5. Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China.

Abstract

A mesoscale plasticity rule improves AI learning.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference49 articles.

1. Learning representations by back-propagating errors

2. Deep learning

3. The "Wake-Sleep" Algorithm for Unsupervised Neural Networks

4. Continual Learning in a Multi-Layer Network of an Electric Fish

5. Deep neural reasoning

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