Spiking neural networks encode information in spike timing and offer a pathway toward energy efficient artificial intelligence. However, a key challenge in spiking neural networks is realizing nonlinear and expressive computation in compact, energy-efficient hardware without relying on additional circuit complexity. In this work, we examine nonlinear computation in a CMOS+X spiking neuron implemented with a magnetic tunnel junction connected in series with an NMOS transistor. Circuit simulations of a multilayer network solving the XOR classification problem show that three intrinsic neuronal properties enable nonlinear behavior: threshold activation, response latency, and absolute refraction. Threshold activation determines which neurons participate in computation, response latency shifts spike timing, and absolute refraction suppresses subsequent spikes. These results show that magnetization dynamics of MTJ devices can support nonlinear computation in compact neuromorphic hardware.

翻译：脉冲神经网络通过脉冲时序编码信息，为发展节能型人工智能提供了可行路径。然而，脉冲神经网络的核心挑战在于：如何在无需额外电路复杂度的前提下，在紧凑的节能硬件中实现非线性与高表达能力的计算。本研究分析了由磁隧道结与NMOS晶体管串联构成的CMOS+X脉冲神经元中的非线性计算机制。针对多层网络求解XOR分类问题的电路仿真表明，三种内在神经元特性可激发非线性行为：阈值激活、响应延迟与绝对不应期。阈值激活决定参与计算的神经元群体，响应延迟改变脉冲时序，而绝对不应期则抑制后续脉冲发放。这些结果证明，磁隧道结的磁化动力学可在紧凑型神经形态硬件中支持非线性计算。