The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates an extended memory capacity. To address this challenge, we propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our model incorporates carefully designed somatic and dendritic compartments that are tailored to retain short- and long-term memories. The theoretical analysis further confirms its effectiveness in addressing the notorious vanishing gradient problem. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.

翻译：摘要：与潜在机遇和危险相关的感官线索识别，常因无关事件造成的长时间延迟而变得复杂。因此，最先进的脉冲神经网络（SNN）在识别长期时间依赖关系方面仍是一项挑战，因为跨越时间间隔需要扩展的记忆容量。为解决这一问题，我们提出了一种新颖的、受生物启发的长短期记忆漏积分放电脉冲神经元模型，命名为LSTM-LIF。该模型精心设计了分别用于保留短期和长期记忆的体细胞和树突室。理论分析进一步证实了其在解决棘手的梯度消失问题中的有效性。我们在多种时间分类任务上的实验结果表明，所提出的LSTM-LIF模型具有优越的时间分类能力、快速的训练收敛性、强大的网络泛化能力以及高能效。因此，这项工作为在新型神经形态计算设备上解决具有挑战性的时间处理任务开辟了众多可能性。