Dynamic task dependent decoding of visual motion information during continuous behavior

为了在日常生活中行动和反应，我们必须不断地整合来自我们感官系统的信息，以解决一系列不断变化的任务。尽管对神经信息整合的途径有了巨大的洞察力，但人们几乎不知道如何在自然场景中实现视觉信息解码所需的灵活性，以解决复杂的任务。在实验室里，这种细心的特性通常被孤立地研究，并做出离散的决定。我的目标是通过一系列创新的实验和分析来克服这些限制。我将在持续的自然行为和神经处理的三个互补尺度上记录数据：猕猴大脑MT和MST区内同时的单细胞反应，大规模皮质电路(人类功能磁共振成像)和由此产生的行为。我的目标是将一个通用的统计模型应用于这些数据，这将为神经过程提供严格的见解。该项目将为未来的研究奠定基础，以便在复杂的自然环境中收集和分析神经科学数据。这将允许理解连续自然行为中的神经计算，并减少对受试者，特别是正在研究的动物的当前限制。

In order to act and react in our daily lives, we have to continuously integrate information from our sensory systems to solve a broad range of constantly changing tasks. Despite tremendous insights into the paths in which neural information is integrated, little is known how the required flexibility in the decoding of the visual information to solve complex tasks in natural scenarios is achieved. In the laboratory, such attentive properties are typically investigated in isolation with discrete decisions being made. I aim to overcome these limitations using a set of innovative experiments and analyses. I will record data during continuous and naturalistic behavior and stimulation at three complementary scales of neural processing: simultaneous single cell responses within areas MT and MST of the macaque brain, large scale cortical circuits (human fMRI) and the resultant behavior. I aim to apply a common statistical model to this data that will provide rigorous insights into the neural processes. The project will lay the groundwork for future research in which neuroscientific data can be collected and analyzed in complex, natural environments. This will allow understanding neural computations in continuous natural behavior and reduce current constraints imposed on the subjects and especially animals under research.