A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics

Liangwei Fan

doi:10.1038/s41467-025-62251-6

2025-08-04

Guoqi Li (Guoqi Li) *

Dewen Hu *

DOI：10.1038/s41467-025-62251-6

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脉冲神经网络（SNNs）由于固有的时间动力学，比人工神经网络在生物学上更合理，在计算上更强大。然而，传统的脉冲神经元难以同时编码输入的空间-时间动力学。受生物多突触连接的启发，我们提出了多突触放电（MSF）神经元，其中轴突可以在突触后神经元上建立具有不同阈值的多个突触。MSF神经元通过放电率联合编码空间强度，并通过放电时间编码时间动力学，并将漏电整合-放电（LIF）和ReLU神经元作为特例进行推广。我们推导了替代梯度的最优阈值选择和参数优化标准，使得可扩展的深度MSF基SNN在不降低性能的情况下实现。在广泛的基准测试中，MSF神经元在精度上显著优于LIF神经元，同时保持低功耗、低延迟和高执行效率，并在事件驱动任务中超越ReLU神经元。总体而言，这项工作推进了神经形态计算向现实世界空间-时间应用的发展。脉冲神经网络难以有效编码空间-时间输入特征。这里，作者引入了一种多突触放电神经元，这是一种神经元模型，能够通过不同的突触阈值同时编码空间强度和时间动力学。

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期刊

Nature Communications

IF:

15.7

论文数:

9.4W

被引数:

91.2W

机构

College of Intelligence Science and Technology

学者数:

105

论文数: 29

被引数: 0

Institute of Automation

学者数:

539

论文数: 287

被引数: 220

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