A magnetic-field-driven neuristor for spiking neural networks

Abstract

Artificial intelligence has been widely deployed in many fields with remarkable success. Among various artificial neural network structures in artificial intelligence, the spiking neural network, as the next-generation artificial neural network, closely mimics the natural neural networks. It contains the all-or-nothing and diverse periodic spiking, which is an analogy to the behavior of natural neurons. Artificial devices that perform the function of neurons are called neuristors. Most existing neuristors are driven by electrical signals, which suffer the problem of impedance mismatch between input and output neuristors. By exploiting the S-shape negative differential resistances element that is sensitive to the external magnetic field, we constructed a magnetic-field-driven neuristor. Magnetic fields can stimulate all-or nothing spiking, and its shape and frequency can be modulated through capacitances in the circuit. As magnetic fields serve as the information carrier, the cascading of our neuristors can get rid of the electrical impedance mismatch, promising a scalable hardware platform for spiking neural networks.