Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biologically plausible and neuromorphic-hardware-friendly way is still challenging. Most recent methods leverage spatial and temporal backpropagation (BP), not adhering to neuromorphic properties. Despite the efforts of some online training methods, tackling spatial credit assignments by alternatives with comparable performance as spatial BP remains a significant problem. In this work, we propose a novel method, online pseudo-zeroth-order (OPZO) training. Our method only requires a single forward propagation with noise injection and direct top-down signals for spatial credit assignment, avoiding spatial BP's problem of symmetric weights and separate phases for layer-by-layer forward-backward propagation. OPZO solves the large variance problem of zeroth-order methods by the pseudo-zeroth-order formulation and momentum feedback connections, while having more guarantees than random feedback. Combining online training, OPZO can pave paths to on-chip SNN training. Experiments on neuromorphic and static datasets with fully connected and convolutional networks demonstrate the effectiveness of OPZO with similar performance compared with spatial BP, as well as estimated low training costs.

翻译：受大脑启发的神经形态计算与脉冲神经网络（SNNs）是一种前景广阔的节能计算方法。然而，以更具生物合理性和神经形态硬件友好的方式成功训练SNNs仍具挑战性。大多数近期方法利用空间和时间反向传播（BP），并未遵循神经形态特性。尽管一些在线训练方法做出了努力，但通过性能与空间BP相当的替代方案来解决空间信用分配问题，仍然是一个重要难题。在本工作中，我们提出了一种新方法——在线伪零阶（OPZO）训练。我们的方法仅需单次前向传播（伴随噪声注入）和直接自上而下的信号进行空间信用分配，避免了空间BP存在的权重对称以及逐层前向-反向传播分阶段的问题。OPZO通过伪零阶公式和动量反馈连接解决了零阶方法方差大的问题，同时比随机反馈具有更多保证。结合在线训练，OPZO可为片上SNN训练开辟道路。在神经形态和静态数据集上，使用全连接网络和卷积网络进行的实验证明了OPZO的有效性，其性能与空间BP相当，且训练成本估计较低。