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神经动力系统理解的可视化。

Visualization for understanding of neurodynamical systems.

出版信息

Cogn Neurodyn. 2011 Jun;5(2):145-60. doi: 10.1007/s11571-011-9153-1. Epub 2011 Mar 26.

Abstract

Complex neurodynamical systems are quite difficult to analyze and understand. New type of plots are introduced to help in visualization of high-dimensional trajectories and show global picture of the phase space, including relations between basins of attractors. Color recurrence plots (RPs) display distances from each point on the trajectory to all other points in a two-dimensional matrix. Fuzzy Symbolic Dynamics (FSD) plots enhance this information mapping the whole trajectory to two or three dimensions. Each coordinate is defined by the value of a fuzzy localized membership function, optimized to visualize interesting features of the dynamics, showing to which degree a point on the trajectory belongs to some neighborhood. The variance of the trajectory within the attraction basin plotted against the variance of the synaptic noise provides information about sizes and shapes of these basins. Plots that use color to show the distance between each trajectory point and a larger number of selected reference points (for example centers of attractor basins) are also introduced. Activity of 140 neurons in the semantic layer of dyslexia model implemented in the Emergent neural simulator is analyzed in details showing different aspects of neurodynamics that may be understood in this way. Influence of connectivity and various neural properties on network dynamics is illustrated using visualization techniques. A number of interesting conclusions about cognitive neurodynamics of lexical concept activations are drawn. Changing neural accommodation parameters has very strong influence on the dwell time of the trajectories. This may be linked to attention deficits disorders observed in autism in case of strong enslavement, and to ADHD-like behavior in case of weak enslavement.

摘要

复杂的神经动力学系统很难进行分析和理解。为了帮助可视化高维轨迹并显示相空间的全貌,包括吸引子盆地之间的关系,引入了新型的图形。颜色递归图(RP)以二维矩阵的形式显示轨迹上每个点与其他所有点之间的距离。模糊符号动力学(FSD)图通过将整个轨迹映射到二维或三维来增强此信息。每个坐标都由模糊局部隶属函数的值定义,该值经过优化以可视化动力学的有趣特征,显示轨迹上的一个点属于某个邻域的程度。在吸引力盆地内的轨迹方差与突触噪声的方差进行比较,可以提供有关这些盆地的大小和形状的信息。还介绍了使用颜色显示每个轨迹点与多个选定参考点(例如吸引子盆地的中心)之间的距离的图形。详细分析了在 Emergent neural simulator 中实现的阅读障碍模型语义层中的 140 个神经元的活动,展示了可以通过这种方式理解的神经动力学的不同方面。使用可视化技术说明了连接性和各种神经特性对网络动力学的影响。得出了关于词汇概念激活的认知神经动力学的一些有趣结论。改变神经适应参数对轨迹的停留时间有非常强的影响。这可能与自闭症中观察到的注意力缺陷障碍有关,在强奴役的情况下,这可能与 ADHD 样行为有关。

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