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带有自适应膜时间常数的尖端神经网络用于基于事件的跟踪

Jiqing Zhang, Malu Zhang, Yuanchen Wang

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|March 3, 2025

概括

这项研究引入了由大脑启发的尖端神经网络 (SNN),用于基于事件的跟踪. 一个新的自适应神经元和时间特征聚合器提高了时间数据流的性能.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
人工智能的人工智能

背景情况:

尖端神经网络 (SNN) 是由大脑启发的模型,以事件驱动的方式处理数据.
SNN具有固有的时间记忆能力,适合基于事件的数据流.
目前的SNN应用主要集中在分类上,跟踪任务的探索有限.

研究的目的:

调查SNN对于基于事件的追踪任务的有效性.
提出新的组件,以提高SNN在时间数据处理中的性能.
证明拟议的SNN方法的适应性和通用性.

主要方法:

引入一个灵感来自大脑的自适应性泄漏整合与火 (BA-LIF) 神经元.
BA-LIF神经元根据输入而自适应地调整其膜时间常数.
整合一个时间特征聚合器 (TFA),在时间维度中分配注意力权重.

主要成果:

拟议的BA-LIF神经元有效过噪音并保存有价值的信息.
使用BA-LIF神经元的SNN可以在没有广泛的参数调的情况下实现高性能.
该TFA进一步增强了网络适应能力,用于时间特征提取.
基于事件的追踪数据集的实验证实了该方法的有效性.

结论:

开发的SNN方法,包括BA-LIF神经元和TFA,显示了基于事件的跟踪的重大前景.
拟议的神经元的适应性降低了手动参数优化需求.
该方法表现出强大的概括能力,适用于跟踪和分类任务.

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