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非线性神经场模型中时空感受野的动态逼近

Dynamic approximation of spatiotemporal receptive fields in nonlinear neural field models.

作者信息

Wennekers Thomas

机构信息

Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany.

出版信息

Neural Comput. 2002 Aug;14(8):1801-25. doi: 10.1162/089976602760128027.

DOI:10.1162/089976602760128027
PMID:12180403
Abstract

This article presents an approximation method to reduce the spatiotemporal behavior of localized activation peaks (also called "bumps") in non-linear neural field equations to a set of coupled ordinary differential equations (ODEs) for only the amplitudes and tuning widths of these peaks. This enables a simplified analysis of steady-state receptive fields and their stability, as well as spatiotemporal point spread functions and dynamic tuning properties. A lowest-order approximation for peak amplitudes alone shows that much of the well-studied behavior of small neural systems (e.g., the Wilson-Cowan oscillator) should carry over to localized solutions in neural fields. Full spatiotemporal response profiles can further be reconstructed from this low-dimensional approximation. The method is applied to two standard neural field models: a one-layer model with difference-of-gaussians connectivity kernel and a two-layer excitatory-inhibitory network. Similar models have been previously employed in numerical studies addressing orientation tuning of cortical simple cells. Explicit formulas for tuning properties, instabilities, and oscillation frequencies are given, and exemplary spatiotemporal response functions, reconstructed from the low-dimensional approximation, are compared with full network simulations.

摘要

本文提出了一种近似方法,可将非线性神经场方程中局部激活峰(也称为“凸起”)的时空行为简化为一组仅关于这些峰的幅度和调谐宽度的耦合常微分方程(ODE)。这使得能够对稳态感受野及其稳定性,以及时空点扩散函数和动态调谐特性进行简化分析。仅对峰值幅度的最低阶近似表明,小型神经系统中许多经过充分研究的行为(例如威尔逊 - 考恩振荡器)应适用于神经场中的局部解。完整的时空响应轮廓可进一步从这种低维近似中重建。该方法应用于两个标准神经场模型:一个具有高斯差分连接核的单层模型和一个两层兴奋性 - 抑制性网络。类似的模型先前已用于解决皮层简单细胞方向调谐的数值研究中。给出了调谐特性、不稳定性和振荡频率的显式公式,并将从低维近似重建的示例性时空响应函数与完整网络模拟进行了比较。

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引用本文的文献

1
Separation of spatio-temporal receptive fields into sums of gaussian components.将时空感受野分离为高斯分量之和。
J Comput Neurosci. 2004 Jan-Feb;16(1):27-38. doi: 10.1023/b:jcns.0000004839.49178.2d.