Research efforts towards novel and more energy efficient computing architectures have recently increased in order to help alleviate the high computational demands imposed by Artificial Intelligence (AI). One of the proposed solutions is to mimic the brain using neuromorphic devices, such as artificial neurons, that have potential to execute AI tasks more efficiently.  Recently, artificial neurons based on electrothermally-induced insulator metal transitions (IMT) have been demonstrated to mimic neuron-like behaviors, but typically rely upon first order phase transitions[¹]. On the other hand, LaCoO₃ (LCO) has recently been demonstrated with neuron-like firing and axon-like signal transmission[^2,3], and is based upon a second order phase transition associated with a spin crossover at technologically relevant temperatures[^4–6]. We constructed LCO artificial neurons and used a combination of Raman spectroscopy, electronic and thermal measurements to study the nature of neuronal spiking. Surprisingly, many signatures observed in first order materials such as VO₂ exist, despite the fundamental difference in the nature of the transition. For example, we present images of current collapse and evidence of filament formation by electrical triggering which has also been observed in first order phase transition materials.  These observables are coupled to a rich electronic behavior as a function of applied bias, including self-oscillating and static regimes with volatile and non-volatile effects observed. Non-volatile effects are hypothesized to be related to strain and defects. Using a material-based approach, and a new way to directly visualize local properties concurrently to device behavior, we address the controversial spin and electronic transitions and explore how the electrothermally driven transition differs from the conventional thermally driven case. Our results suggest that abrupt gradients in temperature and strain may determine the macroscopic characteristics of devices as well as define the spatial probability of filament formation, which could be exploited for memory applications[⁷]. Finite element modeling of the current collapse yields similarly sized filaments and validates the universality of the phenomenon between first order and second order transitions. Our results can be applied to a broader range of materials exhibiting IMT and spin-crossover and provides new tools to explore promising neuromorphic candidates. Broadly, we demonstrate that developing device physics models is crucial to realizing neuromorphic computing architectures where device properties must be engineered from the materials level.