Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time. These problems become roadblocks for AI systems where online training capabilities are vital. Recently, researchers have developed biologically-inspired training algorithms, addressing a subset of those problems. In this work, we propose a novel learning algorithm called online spatio-temporal learning with target projection (OSTTP) that resolves all aforementioned issues of BPTT. In particular, OSTTP equips a network with the capability to simultaneously process and learn from new incoming data, alleviating the weight symmetry and update-locking problems. We evaluate OSTTP on two temporal tasks, showcasing competitive performance compared to BPTT. Moreover, we present a proof-of-concept implementation of OSTTP on a memristive neuromorphic hardware system, demonstrating its versatility and applicability to resource-constrained AI devices.

翻译：基于时间反向传播（BPTT）算法训练的循环神经网络在各类时序任务中取得了显著成功。然而，BPTT算法存在严重局限性，例如需要沿时间方向反向传播信息、权重对称性要求，以及空间与时间上的更新锁定问题。这些缺陷成为在线训练能力至关重要的AI系统的关键障碍。近年来，研究人员开发了受生物启发的训练算法，但仅能部分解决上述问题。本研究提出一种名为"在线时空学习与目标投影"（OSTTP）的新型学习算法，该算法能完全解决BPTT的所有上述问题。具体而言，OSTTP使网络具备同时处理和学习新输入数据的能力，有效缓解了权重对称性和更新锁定问题。我们在两项时序任务上评估了OSTTP，展示了其与BPTT相比具有竞争力的性能。此外，我们还在忆阻神经形态硬件系统上实现了OSTTP的概念验证，证明了其在资源受限AI设备中的适用性和多功能性。