Spike integration and threshold processing are the basic signal processing in brain-inspired computing, such as deep learning, reservoir computing etc. In such processes, analog technology is essential for suppressing energy consumption. However, analog technology often faces problems in miniaturization due to deteriorated noise tolerance by scaling and intrinsically large analog elements such as capacitors. Here, we propose to exploit a thermal degree of freedom in phase transition materials for scalable and noise-tolerant analog spike processing. We focus on a two-terminal metal-insulator-transition VO2 device, where quasi-adiabatic Joule heating enables efficient spike integration, and metal-insulator transition implements threshold processing. This VO2 device is highly scalable, consuming only ∼1fJ/spike (smallest so far) according to the simulation. By using this device, fully autonomous spike integration and threshold processing are also demonstrated. Exploiting the quasi-adiabatic thermal degree of freedom will facilitate scalable and energy-efficient analog implementation for a wide range of brain-inspired computing.