  While the complementary metal-oxide semiconductor (CMOS) technology is the
mainstream for the hardware implementation of neural networks, we explore an
alternative route based on a new class of spiking oscillators we call thermal
neuristors, which operate and interact solely via thermal processes. Utilizing
the insulator-to-metal transition in vanadium dioxide, we demonstrate a wide
variety of reconfigurable electrical dynamics mirroring biological neurons.
Notably, inhibitory functionality is achieved just in a single oxide device,
and cascaded information flow is realized exclusively through thermal
interactions. To elucidate the underlying mechanisms of the neuristors, a
detailed theoretical model is developed, which accurately reflects the
experimental results. This study establishes the foundation for scalable and
energy-efficient thermal neural networks, fostering progress in brain-inspired
computing.
