Inspired by the human brain, the spike-based neuromorphic system has attracted strong research enthusiasm because of the high energy efficiency and powerful computational capability, in which the spiking neurons and plastic synapses are two fundamental building blocks. Recently, two-terminal threshold switching (TS) devices have been regarded as promising candidates for building spiking neurons in hardware. However, how circuit parameters affect the spiking behavior of TS-based neurons is still an open question. Here, based on a leaky integrate-and-fire (LIF) neuron circuit, we systematically study the effect of both the extrinsic and intrinsic factors of NbOx -based TS neurons on their spiking behaviors. The extrinsic influence factors contain input intensities, connected synaptic weights, and parallel capacitances. To illustrate the effect of intrinsic factors, including the threshold voltage, holding voltage, and high/low resistance states of NbOx devices, we propose an empirical model of the fabricated NbOx devices, fitting well with the experimental results. The results indicate that with enhancing the input intensity, the spiking frequency increases first then decreases after reaching a peak value. Except for the connected synaptic weights, all other parameters can modulate the spiking peak frequency under high enough input intensity. Also, the relationship between energy consumption per spike and frequency of the neuron cell is further studied, leading guidance to design neuron circuits in a system to obtain the lowest energy consumption. At last, to demonstrate the practical applications of TS-based neurons, we construct a spiking neural network (SNN) to control the cart-pole using reinforcement learning, obtaining a reward score up to 450. This work provides valuable guidance on building compact LIF neurons based on TS devices and further bolsters the construction of high-efficiency neuromorphic systems.

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The data throughput in the von Neumann architecture based computing system is limited by its separated processing and memory structure and the mismatching speed between the two units. As a result, it’s quite difficult to improve the energy efficiency in conventional computing system, especially for dealing with unstructured data. Meanwhile, artificial intelligence and robotics nowadays still behave poorly in autonomy, creativity and sociality, which has been considered as the unimaginable computational requirement for sensorimotor skills. These two plights have urged the imitation and replication of the biological systems in terms of computing, sensing, and even motoring. Hence, the so-called neuromorphic system has drawn world-wide attention in recent decade, which is aimed at addressing the aforementioned needs from the mimicking of neural system. The recent development on emerging memory devices, nanotechnologies, and materials science have provided an unprecedented opportunity for this aim. This roadmap profiles the potential trend in building neuromorphic systems from the view of Chinese scientists. The content of this roadmap will cover some core topics from multidisciplinary researchers including electronics, computer science, materials, physics, and so on. The perspectives and challenges are also discussed in partly, which may serve as the guidance for the pursuing of high energy-efficient and/or bio-realistic systems that can compute, sense, and act as our human beings. This will also give birth to some excited paradigm breakers and advanced technologies in a wide spectrum of areas.

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