Design of Artificial Neurons of Memristive Neuromorphic Networks Based on Biological Neural Dynamics and Structures

Xiaosong Li, Jingru Sun, Yichuang Sun, Chunhua Wang, Qinghui Hong , Sichun Du, Jiliang Zhang

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Memristive neuromorphic networks have great potentialand advantage in both technology and computationalprotocols for artificial intelligence. Efficient hardware design ofbiological neuron models forms the core of research problems inneuromorphic networks. However, most of the existing researchhas been based on logic or integrated circuit principles, limitedto replicating simple integrate-and-fire behaviors, while morecomplex firing characteristics have relied on the inherent propertiesof the devices themselves, without support from biologicalprinciples. This paper proposes a memristor-based neuron circuitsystem (MNCS) according to the microdynamics of neuronsand complex neural cell structures. It leverages the nonlinearityand non-volatile characteristics of memristors to simulate thebiological functions of various ion channels. It is designed basedon the Hodgkin-Huxley (HH) model circuit, and the parametersare adjusted according to each neuronal firing mechanism. BothPSpice simulations and practical experiments have demonstratedthat MNCS can replicate 24 types of repeating biological neuronalbehaviors. Furthermore, the results from the Joint Inter-spikeInterval(JISI) experiment indicate that as the background noiseincreases, MNCS exhibits pulse emission characteristics similarto those of biological neurons.
Original languageEnglish
Pages (from-to)2320-2333
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number5
Early online date20 Feb 2024
Publication statusPublished - 1 May 2024


  • Biological system modeling
  • Biology
  • Firing
  • Hodgkin Huxley model
  • Integrated circuit modeling
  • Ions
  • Memristor
  • Memristors
  • Neurons
  • ion channel
  • neurodynamics
  • neuromorphic networks


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