中国物理B ›› 2022, Vol. 31 ›› Issue (4): 40702-040702.doi: 10.1088/1674-1056/ac380b

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Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Wenwu Jiang(蒋文武)1, Jie Li(李杰)1, Hongbo Liu(刘洪波)1, Xicong Qian(钱曦聪)1, Yuan Ge(葛源)1, Lidan Wang(王丽丹)1,2,3,4, and Shukai Duan(段书凯)1,2,3,4,†   

  1. 1 College of Artificial Intelligence, Southwest University, Chongqing 400715, China;
    2 National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing 400715, China;
    3 Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory, Chongqing 400715, China;
    4 Chongqing Brain Science Collaborative Innovation Center, Chongqing 400715, China
  • 收稿日期:2021-06-25 修回日期:2021-08-12 接受日期:2021-11-10 出版日期:2022-03-16 发布日期:2022-03-25
  • 通讯作者: Shukai Duan E-mail:duansk@swu.edu.cn
  • 基金资助:
    Project supported by the National Key Research and Development Program of China (Grant No. 2018YFB1306600), the National Natural Science Foundation of China (Grant Nos. 62076207, 62076208, and U20A20227), and the Science and Technology Plan Program of Yubei District of Chongqing (Grant No. 2021-17).

Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Wenwu Jiang(蒋文武)1, Jie Li(李杰)1, Hongbo Liu(刘洪波)1, Xicong Qian(钱曦聪)1, Yuan Ge(葛源)1, Lidan Wang(王丽丹)1,2,3,4, and Shukai Duan(段书凯)1,2,3,4,†   

  1. 1 College of Artificial Intelligence, Southwest University, Chongqing 400715, China;
    2 National&Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology, Chongqing 400715, China;
    3 Brain-inspired Computing and Intelligent Control of Chongqing Key Laboratory, Chongqing 400715, China;
    4 Chongqing Brain Science Collaborative Innovation Center, Chongqing 400715, China
  • Received:2021-06-25 Revised:2021-08-12 Accepted:2021-11-10 Online:2022-03-16 Published:2022-03-25
  • Contact: Shukai Duan E-mail:duansk@swu.edu.cn
  • Supported by:
    Project supported by the National Key Research and Development Program of China (Grant No. 2018YFB1306600), the National Natural Science Foundation of China (Grant Nos. 62076207, 62076208, and U20A20227), and the Science and Technology Plan Program of Yubei District of Chongqing (Grant No. 2021-17).

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摘要： Spiking neural networks (SNNs) are widely used in many fields because they work closer to biological neurons. However, due to its computational complexity, many SNNs implementations are limited to computer programs. First, this paper proposes a multi-synaptic circuit (MSC) based on memristor, which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals. The synapse circuit participates in the calculation of the network while transmitting the pulse signal, and completes the complex calculations on the software with hardware. Secondly, a new spiking neuron circuit based on the leaky integrate-and-fire (LIF) model is designed in this paper. The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required. The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron (MSSN). The MSSN was simulated in PSPICE and the expected result was obtained, which verified the feasibility of the circuit. Finally, a small SNN was designed based on the mathematical model of MSSN. After the SNN is trained and optimized, it obtains a good accuracy in the classification of the IRIS-dataset, which verifies the practicability of the design in the network.

关键词: memristor, multi-synaptic circuit, spiking neuron, spiking neural network (SNN)

Abstract: Spiking neural networks (SNNs) are widely used in many fields because they work closer to biological neurons. However, due to its computational complexity, many SNNs implementations are limited to computer programs. First, this paper proposes a multi-synaptic circuit (MSC) based on memristor, which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals. The synapse circuit participates in the calculation of the network while transmitting the pulse signal, and completes the complex calculations on the software with hardware. Secondly, a new spiking neuron circuit based on the leaky integrate-and-fire (LIF) model is designed in this paper. The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required. The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron (MSSN). The MSSN was simulated in PSPICE and the expected result was obtained, which verified the feasibility of the circuit. Finally, a small SNN was designed based on the mathematical model of MSSN. After the SNN is trained and optimized, it obtains a good accuracy in the classification of the IRIS-dataset, which verifies the practicability of the design in the network.

Key words: memristor, multi-synaptic circuit, spiking neuron, spiking neural network (SNN)

中图分类号:  (Circuits and circuit components)

  • 07.50.Ek
07.05.Mh (Neural networks, fuzzy logic, artificial intelligence) 84.32.-y (Passive circuit components)