Heat dissipation is a natural consequence of operating any electronic system. In nearly all computing systems, such heat is usually minimized by design and cooling. Here, we show that the temporal dynamics of internally produced heat in electronic devices can be engineered to both encode information within a single device and process information across multiple devices. In our demonstration, electronic NbO_x Mott neurons, integrated on a flexible organic substrate, exhibit 18 biomimetic neuronal behaviours and frequency-based nociception within a single component by exploiting both the thermal dynamics of the Mott transition and the dynamical thermal interactions with the organic substrate. Further, multiple interconnected Mott neurons spatiotemporally communicate purely via heat, which we use for graph optimization by consuming over 10⁶ times less energy when compared with the best digital processors. Thus, exploiting natural thermal processes in computing can lead to functionally dense, energy-efficient and radically novel mixed-physics computing primitives.

散热是任何电子系统运行的自然结果。在几乎所有计算系统中，通常通过设计和冷却来最大限度地减少此类热量。在这里，我们展示了电子设备内部产生的热量的时间动态可以被设计为在单个设备内编码信息并跨多个设备处理信息。在我们的演示中，电子 NbO _x Mott 神经元集成在柔性有机基板上，通过利用 Mott 转变的热动力学和与有机基板的动态热相互作用，在单个组件内表现出 18 种仿生神经元行为和基于频率的伤害感受。此外，多个互连的莫特神经元纯粹通过热量进行时空通信，与最好的数字处理器相比，我们将其用于图形优化，消耗的能量减少了 10 ⁶倍以上。因此，在计算中利用自然热过程可以带来功能密集、节能和全新的混合物理计算原语。