Tao Louis, Praissman Jeremy, Sornborger Andrew T
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetics Engineering, College of Life Sciences, Peking University, Number 5 Summer Palace Road, Beijing 100871, People's Republic of China.
J Comput Neurosci. 2012 Apr;32(2):367-76. doi: 10.1007/s10827-011-0359-3. Epub 2011 Aug 27.
In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance of firing rates in the original large-scale simulations while still accurately predicting the orientation preference distribution.
在本文中，我们扩展了用于构建哺乳动物初级视觉皮层大规模神经元网络低维动力学系统模型的框架。我们的降维过程包括进行适当的变量线性变换，然后系统地截断新的方程组。扩展框架包括将被忽略模式的影响建模为一个随机过程。通过以两种方式之一进行参数化并纳入随机性，我们表明可以改进降维神经元网络模型的系统级特征。我们检查了从大规模模拟和随机降维模型的放电率分布计算得到的方向选择性图，发现通过使用随机过程对被忽略模式进行建模，我们能够更好地重现原始大规模模拟中放电率的均值和方差，同时仍能准确预测方向偏好分布。
