Causal connectivity of evolved neural networks during behavior
Causal connectivity of evolved neural networks during behavior
复制标题
DOI：
10.1080/09548980500238756
复制
发表时间：
2005-03-01
影响因子：
7.8
通讯作者：
Seth, AK
中科院分区：
文献类型：
--
作者：
Seth, AK
为了说明神经动力学中的因果交互是如何受到行为的调节的，在不干扰或损害神经机制的情况下分析这些交互是有价值的。本文提出了一种基于向量自回归模型和格兰杰因果关系的图论扩展的方法，用于描述在完整的神经机制内产生的因果交互作用。这种方法被称为因果连通性分析，通过模型神经网络来说明，该模型神经网络在模拟的头-眼系统中为控制目标固定而优化，在该系统中，环境的结构可以实验地改变。对这一模型的因果连通性分析对感觉运动协调潜在的神经机制产生了新的见解。与支持相对简单的行为的网络相比，支持丰富适应行为的网络显示出更高密度的因果交互，以及从感觉输入到运动输出的更强的因果流动。它们还显示了“因果源”和“因果汇”的不同排列：不同地影响网络其余部分或受其影响的节点。最后，因果连通性分析可以预测网络损害的功能后果。这些结果表明，因果连通性分析在神经动力学分析中可能有有用的应用。
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.