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：通过时间反向传播（BPTT）算法训练的循环神经网络在各种时序任务中取得了惊人成功。然而，BPTT引入了严重限制，例如需要沿时间反向传播信息、权重对称性要求，以及时空更新的锁定问题。这些缺陷成为在线学习能力至关重要的AI系统的瓶颈。近年来，研究人员已开发出受生物启发的训练算法，解决了其中部分问题。本研究提出一种新颖的学习算法——在线时空学习与目标投影（OSTTP），该算法解决了BPTT的所有上述问题。具体而言，OSTTP赋予网络同时处理和学习新传入数据的能力，缓解了权重对称性和更新锁定问题。我们在两个时序任务上评估了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，证明了其多功能性及对资源受限AI设备的适用性。