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.

提出了一种新的在线时空学习算法 OSTTP，它解决了 BPTT 算法所遇到的种种问题，并在两个时态任务中证明了其竞争力；另外，该算法在记忆电阻神经形态硬件系统上得到了实现和展示其灵活性和适用性。