Spiking neural networks are expected to play a vital role in realizing ultra-low power hardware for computer vision applications . While the algorithmic efficiency is promising, their solid-state implementation with traditional CMOS transistors lead to area expensive solutions. Transistors are typically designed and optimized to perform as switches and do not naturally exhibit the dynamical properties of neurons. In this work, we harness the abrupt insulator-to-metal transition (IMT) in a prototypical IMT material, vanadium dioxide (VO2) , to experimentally demonstrate a compact integrate and fire spiking neuron . Further, we show multiple spiking dynamics of the neuron relevant to implementing 'winner take all' max pooling layers employed in image processing pipelines.