The modeling of single neurons has proven to be an indispensable tool in deciphering the mechanisms underlying neural dynamics and signal processing. In that sense, two types of single-neuron models are extensively used: the conductance-based models (CBMs) and the so-called phenomenological models, which are often opposed in their objectives and their use. Indeed, the first type aims to describe the biophysical properties of the neuron cell membrane that underlie the evolution of its potential, while the second one describes the macroscopic behavior of the neuron without taking into account all of its underlying physiological processes. Therefore, CBMs are often used to study “low-level” functions of neural systems, while phenomenological models are limited to the description of “high-level” functions. In this letter, we develop a numerical procedure to endow a dimensionless and simple phenomenological nonspiking model with the capability to describe the effect of conductance variations on nonspiking neuronal dynamics with high accuracy. The procedure allows determining a relationship between the dimensionless parameters of the phenomenological model and the maximal conductances of CBMs. In this way, the simple model combines the biological plausibility of CBMs with the high computational efficiency of phenomenological models, and thus may serve as a building block for studying both high-level and low-level functions of nonspiking neural networks. We also demonstrate this capability in an abstract neural network inspired by the retina and C. elegans networks, two important nonspiking nervous tissues.

单个神经元的建模已被证明是破译神经动力学和信号处理机制不可或缺的工具。从这个意义上说，两种类型的单神经元模型被广泛使用：基于电导的模型（CBM）和所谓的现象学模型，它们的目标和用途通常是相反的。事实上，第一类旨在描述神经元细胞膜的生物物理特性，这是其潜力进化的基础，而第二类则描述神经元的宏观行为，而不考虑其所有潜在的生理过程。因此，CBM 通常用于研究神经系统的“低级”功能，而现象学模型仅限于描述“高级”功能。在这封信中，我们开发了一种数值程序，赋予无量纲且简单的现象学非尖峰模型，能够高精度地描述电导变化对非尖峰神经元动力学的影响。该过程允许确定唯象模型的无量纲参数与 CBM 的最大电导之间的关系。通过这种方式，简单的模型将 CBM 的生物学合理性与现象学模型的高计算效率结合起来，因此可以作为研究非尖峰神经网络的高级和低级功能的构建块。我们还在受视网膜启发的抽象神经网络中展示了这种能力 这个简单的模型结合了 CBM 的生物学合理性和现象学模型的高计算效率，因此可以作为研究非尖峰神经网络的高级和低级功能的构建块。我们还在受视网膜启发的抽象神经网络中展示了这种能力 这个简单的模型结合了 CBM 的生物学合理性和现象学模型的高计算效率，因此可以作为研究非尖峰神经网络的高级和低级功能的构建块。我们还在受视网膜启发的抽象神经网络中展示了这种能力线虫网络，两种重要的非尖峰神经组织。