Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications

Abstract

Self-sustained oscillators are emerging as key physical elements for neuromorphic electronics, providing a hardware route to emulate the spiking dynamics of biological neurons. As conventional computing architectures struggle with power dissipation and parallel processing limitations, oscillatory devices offer a means to reproduce the brain’s remarkable efficiency–performing adaptive and nonlinear tasks with minimal energy consumption. This review provides a unified synthesis of the diverse families of self-oscillating systems developed across physics, chemistry, and electronic engineering. We classify oscillators according to their operational mechanisms, distinguishing those driven by negative differential resistance (NDR) instabilities from those sustained by active-feedback amplifiers. Their common behavior is described within a nonlinear dynamical framework that links materials, electronic response, and the emergence of limit cycles in phase space. We discuss how these devices–ranging from electrochemical and memristive oscillators to transistor-based and hybrid architectures–can be modeled, measured, and coupled to form complex networks. Particular attention is given to the experimental identification of active elements and impedance signatures that reveal self-oscillation. By bridging device physics, nonlinear dynamics, and neuromorphic computing, this review outlines a coherent foundation for designing scalable, energy-efficient oscillatory systems that connect the physical principles of chemical and electronic oscillators with the computational logic of the brain.