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
Abstract
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.