The drive to develop artificial neural networks that efficiently utilize
resources has generated significant interest in bio-inspired Spiking Neural
Networks (SNNs). These networks are particularly attractive due to their
potential in applications requiring low power and memory. This potential is
further enhanced by the ability to perform online local learning, enabling them
to adapt to dynamic environments. This requires the model to be adaptive in a
self-supervised manner. While self-supervised learning has seen great success
in many deep learning domains, its application for online local learning in
multi-layer SNNs remains underexplored. In this paper, we introduce the
"EchoSpike Predictive Plasticity" (ESPP) learning rule, a pioneering online
local learning rule designed to leverage hierarchical temporal dynamics in SNNs
through predictive and contrastive coding. We validate the effectiveness of
this approach using benchmark datasets, demonstrating that it performs on par
with current state-of-the-art supervised learning rules. The temporal and
spatial locality of ESPP makes it particularly well-suited for low-cost
neuromorphic processors, representing a significant advancement in developing
biologically plausible self-supervised learning models for neuromorphic
computing at the edge.

通过引入「EchoSpike Predictive Plasticity」（ESPP）学习规则，本文在多层 SNN 中探索在线本地学习的自监督学习应用，展示其与当前最先进的有监督学习规则相媲美的效果，从而为边缘神经形态计算开发出具有生物可行性的自监督学习模型，为低成本的神经形态处理器提供时空局部性突出的解决方法。