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 potential
and advantage in both technology and computational
protocols for artificial intelligence. Efficient hardware design of
biological neuron models forms the core of research problems in
neuromorphic networks. However, most of the existing research
has been based on logic or integrated circuit principles, limited
to replicating simple integrate-and-fire behaviors, while more
complex firing characteristics have relied on the inherent properties
of the devices themselves, without support from biological
principles. This paper proposes a memristor-based neuron circuit
system (MNCS) according to the microdynamics of neurons
and complex neural cell structures. It leverages the nonlinearity
and non-volatile characteristics of memristors to simulate the
biological functions of various ion channels. It is designed based
on the Hodgkin-Huxley (HH) model circuit, and the parameters
are adjusted according to each neuronal firing mechanism. Both
PSpice simulations and practical experiments have demonstrated
that MNCS can replicate 24 types of repeating biological neuronal
behaviors. Furthermore, the results from the Joint Inter-spike
Interval(JISI) experiment indicate that as the background noise
increases, MNCS exhibits pulse emission characteristics similar
to those of biological neurons.
Original languageEnglish
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Publication statusAccepted/In press - 7 Nov 2023


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