Asynchronous Bioplausible Neuron for SNN for Event Vision
This work addresses a specific problem in biologically inspired computer vision for researchers and practitioners, offering an incremental improvement in SNN design.
The paper tackles the challenge of maintaining homeostasis in Spiking Neural Networks (SNNs) for event vision by proposing the Asynchronous Bioplausible Neuron (ABN), a dynamic spike firing mechanism that auto-adjusts to input variations, resulting in enhanced performance in image classification and segmentation, neural equilibrium maintenance, and energy efficiency.
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.