Modeling and Measuring Flows between Cognitive and Neural Processes

认知神经科学主要集中在定位区域和网络，参与点状过程，如感知，注意力和记忆。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而紧密交织在一起，以进行正常的心理操作。同样，认知过程的神经基础也是动态地相互作用的。我们提出了一个更深思熟虑的方法，使用多尺度分析的经验神经成像数据和大规模的大脑模拟与TheVirtualBrain（TVB：thevirtualbrain.org），以更好地表征认知过程和功能的大脑架构，支持这些流量之间的流量。我们将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为流形，沿着其轨迹随着嵌入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的集合构成了其功能库，这些功能库一起实例化了一套完整的潜在认知功能和公开行为。

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.