The dominant modeling framework for understanding cortical computations are heuristic firing rate models. Despite their success, these models fall short to capture spike synchronization effects, to link to biophysical parameters and to describe finite-size fluctuations. In this opinion article, we propose that the refractory density method (RDM), also known as age-structured population dynamics or quasi-renewal theory, yields a powerful theoretical framework to build rate-based models for mesoscopic neural populations from realistic neuron dynamics at the microscopic level. We review recent advances achieved by the RDM to obtain efficient population density equations for networks of generalized integrate-and-fire (GIF) neurons - a class of neuron models that has been successfully fitted to various cell types. The theory not only predicts the nonstationary dynamics of large populations of neurons but also permits an extension to finite-size populations and a systematic reduction to low-dimensional rate dynamics. The new types of rate models will allow a re-examination of models of cortical computations under biological constraints.

中文翻译：

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注意最后一个峰值-尖峰神经元介观群体的放电速率模型。

用于理解皮层计算的主要建模框架是启发式发射速率模型。尽管取得了成功，但这些模型仍不足以捕获峰值同步效应，链接到生物物理参数并描述有限大小的波动。在这篇观点文章中，我们提出了耐火材料密度法（RDM），也称为年龄结构的种群动力学或准更新理论，提供了一个强大的理论框架，可根据实际的神经元动力学在介观神经种群中建立基于速率的模型。微观层面。我们回顾了RDM取得的最新进展，以获取有效的广义集成和发射（GIF）神经元网络的种群密度方程，GIF神经元是一类成功地适合各种细胞类型的神经元模型。该理论不仅可以预测大数量神经元的非平稳动力学，而且可以扩展到有限大小的种群，并可以系统地降低低维速率动力学。新型的费率模型将允许在生物学限制下重新检查皮质计算模型。