A Stacked Memristive Device Enabling Both Analog and Threshold Switching Behaviors for Artificial Leaky Integrate and Fire Neuron

Leaky integrate and fire (LIF) neurons are critical units for constructing a spiking neural network, in which neurons communicate with each other using spikes via synapses. Memristors, due to its specific nonlinear characteristics, are frequently introduced to emulate partial functions of LIF neurons for simplifying the circuit complexity, either the integration process or the fire action. Usually, a relatively complicated peripheral circuit needs to be engineered to assist the memristive device for complete emulation for biological neurons, which certainly would hinder the integration potential. Herein, we fabricated a stacked memristive device possessing both analog and threshold switching behaviors for constructing an artificial LIF neuron. Thus, the integration and fire functions were both accomplished within this single nanoscale device. In addition, the key neuronic functional of a biological neuron, including all-or-nothing spiking, threshold spiking, a refractory period, and strength-modulated frequency response were all successfully mimicked. The results demonstrate that the fabricated stacked memristor-based LIF neurons have great potential to construct high-density spiking neural network for neuromorphic computing.

Automated summary

This paper introduces a stacked memristor-based LIF neuron that can accomplish integration and firing functions and successfully mimics key functions of biological neurons. The fabricated neurons have great potential for constructing high-density spiking neural networks for neuromorphic computing.