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.

翻译：脉冲神经网络提供了一种受生物启发的计算机视觉方法，可更高效地处理视觉数据并降低能耗。然而，维持这些网络的内稳态具有挑战性，因为需要持续调整神经元响应，以在多元且往往不可预测的输入信号中保持平衡与最优处理效率。针对这些挑战，我们提出异步生物可解释神经元——一种动态脉冲发放机制，能够自动调节输入信号的变化。跨多个数据集的综合评估表明，ABN在图像分类与分割任务中性能增强，能维持神经平衡并提升能量效率。