Schoenberg Kernel Loss for Spiking Neural Network Training

Abstract

In this paper, we propose the Schoenberg kernel loss, a loss function that measures the divergence between spike trains for spiking neural network training. Our method addresses the challenges of non-differentiable spiking neurons and leverages both spatial and temporal dynamics in information processing. We evaluated our approach on the neuromorphic benchmark datasets, N-MNIST, and DVS Gesture, demonstrating our method outperforms previously published loss functions on our network architecture. In addition, we extend our investigation to real-time classification simulations in real-world scenarios. This work contributes to the advancement of direct SNN training methods, offering improved performance, particularly on low spike density data, and potential applications in neuromorphic classification.