An memristor-based synapse implementation using BCM learning rule

Yongchuang Huang, Junxiu Liu, Jim Harkin, Liam McDaid, Yuling Luo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
3 Downloads (Pure)


A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.
Original languageEnglish
Pages (from-to)336-342
Number of pages7
Early online date16 Nov 2020
Publication statusPublished - 29 Jan 2021


  • BCM theory
  • Learning rule
  • Memristor
  • Spiking neural networks


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