Bio-plausible reconfigurable spiking neuron for neuromorphic computing.

Journal: Science advances
Published Date: Feb 5, 2025

Abstract

Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.

Authors

  • Yu Xiao
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Yize Liu
    State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China.
  • Bihua Zhang
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Peng Chen
  • Huaze Zhu
    School of Engineering, Westlake University, Hangzhou, China.
  • Enhui He
    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Jiayi Zhao
    School of Psychology, Shanghai University of Sport, Shanghai, China.
  • Wenju Huo
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Xiaofei Jin
    College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
  • Xumeng Zhang
    Department of Electrical and Computer Engineering, University of Massachusetts, 100 Natural Resources Road, Amherst, Massachusetts, 01003, USA.
  • Hao Jiang
    Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , 555 Zuchongzhi Road, Shanghai 201203, China.
  • De Ma
    Zhejiang University, Hangzhou, 310027, China.
  • Qian Zheng
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Huajin Tang
  • Peng Lin
    Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
  • Wei Kong
    School of Engineering, Westlake University, Hangzhou, China.
  • Gang Pan
    College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.

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