Learning in the Human Brain: Anatomy, Physiology and Computation

该项目将侧重于两个定义良好的系统中的结构和信息流。首先，我们考虑了人类大脑中一个有层次组织的系统，它从代表行动目标的前额叶区域延伸到组织运动执行细节的皮质运动区域。其次，我们考虑小脑回路如何从这个系统中学习，存储表征，使信息处理变得熟练，无意识和自动（例如，机器学习中的摊销）。这两个系统组成了皮质-小脑系统——人类大脑中最大、最突出的网络之一。将有三种紧密结合的方法。首先，这些系统的详细结构将通过分析大型的、已有的静息状态功能mri数据集来研究。其次，使用功能性核磁共振成像的实验工作将测试与额叶动作网络的层次性质有关的假设，并测试与学习相关的可塑性的小脑特征。这些方法将主要由Ramnani监督。第三，理论神经生物学的方法来理解信息流在层次网络和学习相关的可塑性将开发使用计算模型，由弗里斯顿开发的主动推理框架，谁将监督这方面。我们将根据学生的兴趣和技能定制博士学位。实验工作、数据分析和计算建模的灵活平衡将确保博士学位在未来可能无法获取数据的封锁期间保持可行性。这名学生将在皇家霍洛威学院学习，在那里他们将与ramnani研究小组的成员进行互动。将定期访问弗里斯顿和他在伦敦大学学院威康信托神经成像中心的研究小组。研究设施（RoyalHolloway）将包括一个现场，研究专用的西门子Trio MRI扫描仪，一个完全支持的高性能计算集群，以及Ramnani的行为研究实验室设施。

The project will focus on structure and information flow in two well-defined systems.First, we consider a hierarchically organised system in the human brain that extendsfrom prefrontal areas that represent action goals, to the cortical motor areas thatorganise the details of movement execution. Second, we consider how cerebellarcircuitry learns from this system, storing representations that enable this informationprocessing to become skilled, unconscious and automatic (c.f., amortization inmachine learning). These two systems form the cortico-cerebellar system - one of thelargest and most prominent networks in the human brain. There will be three closelyintegrated approaches. First, the detailed structure of these systems will beinvestigated through the analysis of large, pre-existing resting-state functional MRIdatasets. Second, experimental work using functional MRI will test hypotheses relatingto the hierarchical nature of frontal lobe action networks, and test for cerebellarsignatures of learning-related plasticity. These approaches will be primarily supervisedby Ramnani. Third, theoretical neurobiological approaches to understandinginformation flow in hierarchical networks and learning-related plasticity will bedeveloped using computational modelling, informed by the active inference frameworkdeveloped by Friston, who will supervise this aspect. We will tailor the PhD to theinterests and skills of the student. The flexible balance of experimental work, dataanalysis, and computational modelling will ensure that the PhD will remain feasibleduring possible future periods of lockdown when data acquisition may not be possible.The student will be based at Royal Holloway, where they will interact with members ofRamnani's research group. There will be periodic visits to Friston and his researchgroup at Wellcome Trust Centre for Neuroimaging, UCL. Research facilities (RoyalHolloway) will include an on-site, research-dedicated Siemens Trio MRI scanner, a fullysupported,high-performance computing cluster, and Ramnani's lab facilities forbehavioural research.