Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators. However, biological neurons have an internal state governed by complex dynamics that plays a crucial role in learning, adaptation and the overall network activity and behaviour. This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model, which combines several biologically inspired mechanisms to efficiently simulate internal neuron dynamics with a single parameter analogous to the membrane time constant in biological neurons. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with fluctuating input. One consequence of the MPATH model is that it imbues neurons with a sense of time without recurrent connections, paving the way for modelling processes that depend on temporal aspects of neuron activity. Experiments demonstrate the model's ability to adapt to and continually learn from its input.

中文翻译：

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持续学习和适应与膜电位和激活阈值稳态。

大多数经典（非加标）神经网络模型都忽略内部神经元动力学，并将神经元视为简单的输入积分器。然而，生物神经元具有受复杂动力学控制的内部状态，该复杂状态在学习，适应以及整个网络活动和行为中起着至关重要的作用。本文介绍了膜电位和激活阈值稳态（MPATH）神经元模型，该模型结合了多种生物学启发的机制，可以有效地模拟内部神经元动力学，并且具有类似于生物神经元膜时间常数的单个参数。当出现波动的输入时，该模型允许神经元通过自动调节其活动来维持动态平衡的形式。MPATH模型的一个结果是，它使神经元充满时间感，而无需反复连接，从而为依赖于神经元活动的时间方面的建模过程铺平了道路。实验证明了该模型具有适应和不断学习其输入的能力。