Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany; Institut für Mathematik, Technische Universität Berlin, 10623 Berlin, Germany.
Ioffe Institute, 194021 Saint-Petersburg, Russia; Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, 194223 Saint-Petersburg, Russia.
Curr Opin Neurobiol. 2019 Oct;58:155-166. doi: 10.1016/j.conb.2019.08.003. Epub 2019 Oct 4.
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.
理解皮层计算的主要建模框架是启发式发放率模型。尽管这些模型取得了成功,但它们无法捕捉尖峰同步效应,无法与生物物理参数联系起来,也无法描述有限大小的波动。在这篇观点文章中,我们提出, refractory density method(RDM),也称为年龄结构群体动力学或准更新理论,为基于率的模型提供了一个强大的理论框架,可从微观水平上的现实神经元动力学构建介观神经群体的模型。我们回顾了 RDM 最近取得的进展,这些进展为广义积分-点火(GIF)神经元网络获得了有效的群体密度方程,GIF 神经元模型已成功应用于各种细胞类型。该理论不仅预测了大神经元群体的非平稳动力学,而且允许扩展到有限大小的群体,并系统地简化为低维率动力学。新型的率模型将允许在生物约束下重新检验皮层计算模型。