  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.
