Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations-Reference-Cited by-同舟云学术

Task-Adaptive Neuromorphic Computing Using Reconfigurable Organic Neuristors with Tunable Plasticity and Logic-in-Memory Operations

Published:2024-02-22 Issue:9 Volume:15 Page:2301-2310
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Jiang Sai¹²^ORCID,Peng Lichao¹,Li Longfei²,Dai Qinyong²,Pei Mengjiao²,Wu Chaoran¹,Su Jian¹^ORCID,Gu Ding¹,Zhang Han¹,Guo Huafei¹^ORCID,Qiu Jianhua¹,Li Yun²^ORCID

Affiliation:

1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, P. R. China

2. National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China

Funder

National Key Research and Development Program of China

Natural Science Foundation of Jiangsu Province

National Natural Science Foundation of China

the Changzhou Sci & Tech Program

the Leading Innovative Talents Introduction and Cultivation Project of Changzhou

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.4c00284

Reference44 articles.

1. Shifting attention to dynamics: Self-reconfiguration of neural networks

2. Principles of dynamic network reconfiguration across diverse brain states

3. Informing deep neural networks by multiscale principles of neuromodulatory systems

4. Mapping and interpreting the dynamic connectivity of the brain

5. Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO₂ Memcapacitive Synapse Arrays

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