Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains-Reference-Cited by-同舟云学术

Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains

Published:2023-07-24 Issue: Volume: Page:
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Container-title:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Liu Tengjun¹,Ning Yuxiao¹,Liu Pengfu¹,Zhang Yiwei¹,Chua Yansong²,Chen Weidong³,Zhang Shaomin⁴

Affiliation:

1. Zhejiang University,Qiushi Academy for Advanced Studies,Department of Biomedical Engineering,Hangzhou,China

2. China Nanhu Academy of Electronics and Information Technology (CNAEIT),Jiaxing,China

3. Zhejiang University,Qiushi Academy for Advanced Studies,Department of Computer Science and Technology,Hangzhou,China

4. Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology Effectiveness Appraisal, Zhejiang University,Qiushi Academy for Advanced Studies,Department of Biomedical Engineering,Hangzhou,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339936/10339939/10340358.pdf?arnumber=10340358

Reference22 articles.

1. A million spiking-neuron integrated circuit with a scalable communication network and interface

2. Supervised learning in spiking neural networks with FORCE training

3. Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks

4. Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

5. Recurrent architecture for adaptive regulation of learning in the insect brain