Artificial Neurons Using Ag-In-Zn-S/Sericin Peptide-Based Threshold Switching Memristors for Spiking Neural Networks

Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate-and-fire memristive neuron is realized in one single cell with Ag/Ag-In-Zn-S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (approximate to 0.4 V), and relatively small device-to-device variation. In addition, multiple neural features, such as leaky integrate-and-fire neuron functionality and strength-modulated spike frequency characteristic, have been successfully emulated owing to the forming-free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate-and-fire behavior of sericin-based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation.

Automated summary

In this study, memristive devices with threshold switching characteristics were used to emulate leaky integrate-and-fire neurons. The devices demonstrated excellent electrical properties and were able to successfully emulate multiple neural features. The study also achieved a high accuracy in pattern recognition, highlighting the potential of using biomaterials in advanced neuromorphic computation.