Modeling and Measuring Flows between Cognitive and Neural Processes

认知神经科学主要关注与感知、注意和记忆等点状过程有关的区域和网络的定位。这种静态的观点忽略了这样一个事实，即这些认知过程随着时间的推移与正常的心理操作密切相关。认知过程的神经基础也是动态互动的。我们提出了一种更深思熟虑的方法，使用经验神经成像数据的多尺度分析和虚拟大脑（TVB: thevirtualbrain.org）的大规模大脑模拟，以更好地表征认知过程和支持这些流动的功能大脑结构之间的流动。我们将把复杂的大脑动力学分解成概率功能模式。这些模式在数学上可操作化为流形，轨迹随着嵌入在低维空间中的动力学展开而演变（流形上的结构化流[SFM]）。神经网络中可用的功能模式集合构成了它的功能库，这些功能库共同实例化了一套完整的潜在认知功能和显性行为。

Cognitive neuroscience has focused mainly on localizing regions and networks that are engaged by punctate processes such as perception, attention and memory. Such a static perspective misses the fact that these cognitive processes are intimately intertwined over time for normal mental operations. So too, the neural bases for the cognitive processes interact dynamically. We propose a more deliberate approach using multiscale analyses of empirical neuroimaging data and large-scale brain simulations with TheVirtualBrain (TVB: thevirtualbrain.org) to better characterize the flow between cognitive processes and the functional brain architectures that support these flows. We will decompose complex brain dynamics into probabilistic functional modes. These modes are mathematically operationalized as manifolds, along which trajectories evolve as the dynamics unfold embedded in a low-dimensional space (structured flows on manifolds [SFM]). The collection of functional modes available in a neural network constitutes its functional repertoire, which together instantiates a complete set of potential cognitive functions and overt behaviors.