期刊
NETWORK-COMPUTATION IN NEURAL SYSTEMS
卷 17, 期 4, 页码 373-418出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/09548980601069787
关键词
network models
An outstanding problem in computational neuroscience is how to use population density function (PDF) methods to model neural networks with realistic synaptic kinetics in a computationally efficient manner. We explore an application of two-dimensional (2-D) PDF methods to simulating electrical activity in networks of excitatory integrate-and-fire neurons. We formulate a pair of coupled partial differential-integral equations describing the evolution of PDFs for neurons in non-refractory and refractory pools. The population firing rate is given by the total flux of probability across the threshold voltage. We use an operator-splitting method to reduce computation time. We report on speed and accuracy of PDF results and compare them to those from direct, Monte-Carlo simulations. We compute temporal frequency response functions for the transduction from the rate of postsynaptic input to population firing rate, and examine its dependence on background synaptic input rate. The behaviors in the 1-D and 2-D cases-corresponding to instantaneous and non-instantaneous synaptic kinetics, respectively-differ markedly from those for a somewhat different transduction: from injected current input to population firing rate output (Brunel et al. 200 1; Fourcaud & Brunel 2002). We extend our method by adding inhibitory input, consider a 3-D to 2-D dimension reduction method, demonstrate its limitations, and suggest directions for future study.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据