  Spiking Neural Networks (SNNs) offer a biologically inspired approach to
computer vision that can lead to more efficient processing of visual data with
reduced energy consumption. However, maintaining homeostasis within these
networks is challenging, as it requires continuous adjustment of neural
responses to preserve equilibrium and optimal processing efficiency amidst
diverse and often unpredictable input signals. In response to these challenges,
we propose the Asynchronous Bioplausible Neuron (ABN), a dynamic spike firing
mechanism to auto-adjust the variations in the input signal. Comprehensive
evaluation across various datasets demonstrates ABN's enhanced performance in
image classification and segmentation, maintenance of neural equilibrium, and
energy efficiency.
