Inspired by the highly irregular spiking activity of cortical neurons, stochastic neural computing is an attractive theory for explaining the operating principles of the brain and the ability to represent uncertainty by intelligent agents. However, computing and learning with high-dimensional joint probability distributions of spiking neural activity across large populations of neurons present as a major challenge. To overcome this, we develop a novel moment embedding approach to enable gradient-based learning in spiking neural networks accounting for the propagation of correlated neural variability. We show under the supervised learning setting a spiking neural network trained this way is able to learn the task while simultaneously minimizing uncertainty, and further demonstrate its application to neuromorphic hardware. Built on the principle of spike-based stochastic neural computing, the proposed method opens up new opportunities for developing machine intelligence capable of computing uncertainty and for designing unconventional computing architectures.

翻译：受皮层神经元高度不规则脉冲活动的启发，随机神经计算为解释大脑工作原理及智能体表征不确定性的能力提供了有吸引力的理论框架。然而，在大规模神经元群体中，基于脉冲神经活动的高维联合概率分布进行计算与学习构成了重大挑战。为克服这一难题，我们开发了一种新颖的矩嵌入方法，使脉冲神经网络在考虑相关神经变异性传播的前提下实现基于梯度的学习。实验表明，在监督学习设置下，经此方法训练的脉冲神经网络能够在学习任务的同时最小化不确定性，并进一步展示了其在神经形态硬件上的应用。基于脉冲随机神经网络计算原理，所提方法为发展具备不确定性计算能力的机器智能及设计非常规计算架构开辟了新途径。