One of the main broad applications of deep learning is function regression. However, despite their demonstrated accuracy and robustness, modern neural network architectures require heavy computational resources to train. One method to mitigate or even resolve this inefficiency has been to draw further inspiration from the brain and reformulate the learning process in a more biologically-plausible way, developing what are known as Spiking Neural Networks (SNNs), which have been gaining traction in recent years. In this paper we present an SNN-based method to perform regression, which has been a challenge due to the inherent difficulty in representing a function's input domain and continuous output values as spikes. We use a DeepONet - neural network designed to learn operators - to learn the behavior of spikes. Then, we use this approach to do function regression. We propose several methods to use a DeepONet in the spiking framework, and present accuracy and training time for different benchmarks.

翻译：深层学习的主要广泛应用之一是功能回归。然而,现代神经网络结构尽管显示出准确性和稳健性,但需要大量的计算资源来培训。一个缓解甚至解决这种低效率的方法是进一步从大脑中汲取灵感,以更生物可变的方式重新塑造学习过程,开发近年来不断获得牵引的所谓“Spiking神经网络”(SNN)。在本文件中,我们提出了一个基于SNN的回归方法,由于在代表函数输入域和持续输出值方面固有的困难,这是一个挑战。我们用DeepONet-神经网络来学习操作者的行为。然后,我们用这个方法来进行功能回归。我们提出了在跳动框架中使用DeepONet(DeepONet)的几种方法,并提出了不同基准的准确性和培训时间。