为脉冲神经网络引入可学习延迟机制，提升时序建模能力且不增加计算负担。
Delays in Spiking Neural Networks: A State Space Model Approach
- 通过新增状态变量实现神经元对历史输入的有限记忆。
- 在SHD数据集上性能与现有方法相当，小模型收益更明显。
- 兼容主流脉冲神经元模型，适合低功耗时序任务场景。
脉冲神经网络（SNN）是受生物启发的事件驱动模型，适用于时序数据处理和低功耗类脑计算。丰富的神经元动态有助于捕捉复杂时序依赖，其中延迟机制使过去输入能直接影响当前放电行为。本文提出一种通用框架，通过引入额外状态变量将延迟融入SNN，使每个神经元可访问有限的历史输入。该机制与神经元模型无关，可无缝集成至标准的漏电积分-放电（LIF）和自适应漏电积分-放电（adLIF）模型中。我们分析了延迟持续时间及可学习参数对性能的影响，并研究了延迟机制带来的架构权衡。在Spiking Heidelberg Digits（SHD）数据集上的实验表明，该方法在性能上达到现有基于延迟的SNN水平，同时保持计算高效，尤其在小型网络中表现更优。
原文摘要 · Abstract (English)
Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing. In SNNs, richer neuronal dynamic allows capturing more complex temporal dependencies, with delays playing a crucial role by allowing past inputs to directly influence present spiking behavior. We propose a general framework for incorporating delays into SNNs through additional state variables. The proposed mechanism enables each neuron to access a finite temporal input history. The framework is agnostic to neuron models and hence can be seamlessly integrated into standard spiking neuron models such as Leaky Integrate-and-Fire (LIF) and Adaptive LIF (adLIF). We analyze how the duration of the delays and the learnable parameters associated with them affect the performance. We investigate the trade-offs in the network architecture due to additional state variables introduced by the delay mechanism. Experiments on the Spiking Heidelberg Digits (SHD) dataset show that the proposed mechanism matches existing delay-based SNNs in performance while remaining computationally efficient, with particular gains in smaller networks.
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