-- A theoretical framework that subsumes conventional deterministic spiking neural networks and surrogate gradients, facilitating more efficient and effective employment of various neuromorphic hardware developments in real-world applications. -- Scalable spiking neural models that incorporate noisy neuronal dynamics for implicit regularization, improved robustness, and computational accounts of biological neural computation, revealing that unreliable neural substrates yield reliable computation and learning. Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have emerged as pillar models in neuromorphic intelligence. Despite extensive research on spiking neural networks (SNNs), most are established on deterministic models. Integrating noise into SNNs leads to biophysically more realistic neural dynamics and may benefit model performance. This work presents the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by introducing a spiking neuron model incorporating noisy neuronal dynamics. Our approach shows how noise may serve as a resource for computation and learning and theoretically provides a framework for general SNNs. We show that our method exhibits competitive performance and improved robustness against challenging perturbations than deterministic SNNs and better reproduces probabilistic neural computation in neural coding. This study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.

翻译：——一个统一常规确定性脉冲神经网络与代理梯度的理论框架，促进各类神经形态硬件开发在实际应用中的高效运用。——融合噪声神经元动力学的可扩展脉冲神经网络模型，能够实现隐式正则化、增强鲁棒性，并解释生物神经计算过程，揭示不可靠的神经基质如何产生可靠的计算与学习。脉冲神经元网络支撑着大脑非凡的信息处理能力，并已成为神经形态智能中的支柱模型。尽管脉冲神经网络（SNN）研究广泛，但多数模型基于确定性假设。将噪声引入SNN可形成更符合生物物理特性的神经动力学，并可能提升模型性能。本研究通过引入融合噪声神经元动力学的脉冲神经元模型，提出了噪声脉冲神经网络（NSNN）与噪声驱动学习规则（NDL）。我们的方法展示了噪声如何作为计算与学习的资源，并在理论上为通用SNN提供了框架。实验表明，相比确定性SNN，该方法在保持竞争性性能的同时，对挑战性扰动具有更强鲁棒性，并能更准确地复现神经编码中的概率性神经计算。本研究为机器学习、神经形态智能实践者及计算神经科学研究者提供了一种强大且易用的工具。