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Spiking Neural Networks (SNNs) offer a biologically inspired approach tocomputer vision that can lead to more efficient processing of visual data withreduced energy consumption. However, maintaining homeostasis within thesenetworks is challenging, as it requires continuous adjustment of neuralresponses to preserve equilibrium and optimal processing efficiency amidstdiverse and often unpredictable input signals. In response to these challenges,we propose the Asynchronous Bioplausible Neuron (ABN), a dynamic spike firingmechanism to auto-adjust the variations in the input signal. Comprehensiveevaluation across various datasets demonstrates ABN's enhanced performance inimage classification and segmentation, maintenance of neural equilibrium, andenergy efficiency.

Asynchronous Bioplausible Neuron for SNN for Event Vision