Zhang J W, Rangan A V
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
J Comput Neurosci. 2015 Apr;38(2):355-404. doi: 10.1007/s10827-014-0543-3. Epub 2015 Jan 21.
In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.
在本文中,我们提供了一种通用方法,用于系统地将一类积分发放网络的动力学简化为一个扩充的四维常微分方程组。我们所关注的积分发放网络类是结构均匀、强耦合且由波动驱动的。我们的简化方法在大多数当前的发放率和群体动力学模型失败的地方取得了成功,因为我们考虑了涉及许多神经元半同步发放的“多次发放事件”的出现。这些多次发放事件在很大程度上导致了网络产生的波动,因此,我们的简化方法如实地描述了从均匀态到同步态的许多动态状态。我们的简化基于第一性原理,并在积分发放网络参数与四维扩充常微分方程所基于的相对低维动力学之间提供了一个可分析的联系。