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.
Bibliographical noteFunding Information:
This research is supported by the National Natural Science Foundation of China under Grant 61976063 and the funding of Overseas 100 Talents Program of Guangxi Higher Education.
Liam J. McDaid received the B.Eng. (Hons.) degree in Electrical and Electronics Engineering and the Ph.D. degree in solid-state devices from the University of Liverpool, Liverpool, U.K., in 1985 and 1989, respectively. He is currently a Professor of Computational Neuroscience with Ulster University, U.K., and leads the Computational Neuroscience and Neural Engineering Research Team. His current research interests include modeling the role of glial cells in the functional and dysfunctional brain and he is also involved in the development of software/hardware models of neural-based computational systems, with particular emphasis on the mechanisms that underpin self-repair in the human brain. Dr. McDaid received several research grants in this domain and is currently a Collaborator on an HFSP and EPSRC funded project.
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- BCM theory
- Learning rule
- Spiking neural networks