As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term potentiation (ALTP) phenomenon suggests that in addition to learning mechanisms between neurons, there are associative effects within neurons. However, most existing methods only focus on the former and lack exploration of the internal association effects. In this paper, we propose a novel Adaptive Internal Association~(AIA) neuron model to establish previously ignored influences within neurons. Consistent with the ALTP phenomenon, the AIA neuron model is adaptive to input stimuli, and internal associative learning occurs only when both dendrites are stimulated at the same time. In addition, we employ weighted weights to measure internal associations and introduce intermediate caches to reduce the volatility of associations. Extensive experiments on prevailing neuromorphic datasets show that the proposed method can potentiate or depress the firing of spikes more specifically, resulting in better performance with fewer spikes. It is worth noting that without adding any parameters at inference, the AIA model achieves state-of-the-art performance on DVS-CIFAR10~(83.9\%) and N-CARS~(95.64\%) datasets.

翻译：作为第三代神经网络，脉冲神经网络致力于探索更具洞察力的神经机制以实现类生物智能。直观而言，仿生机制对于理解和改进脉冲神经网络至关重要。例如，关联长时程增强现象表明，除神经元间的学习机制外，神经元内部也存在关联效应。然而，现有方法大多仅关注前者，缺乏对内部关联效应的探索。本文提出一种新型自适应内部关联神经元模型，以建立此前被忽视的神经元内部影响机制。与ALTP现象一致，AIA神经元模型对输入刺激具有适应性，且仅当树突同时受到刺激时才发生内部关联学习。此外，我们采用加权权重衡量内部关联，并引入中间缓存以减少关联波动性。在主流神经形态数据集上的大量实验表明，所提方法能更特异性地增强或抑制脉冲发放，从而以更少脉冲实现更优性能。值得关注的是，AIA模型在推理阶段无需增加任何参数，即在DVS-CIFAR10（83.9%）和N-CARS（95.64%）数据集上达到当前最优性能。