Learning in the Human Brain: Anatomy, Physiology and Computation

人脑的学习：解剖学、生理学和计算

• 批准号: 2725902
• 金额: --
• 依托单位: Royal Holloway University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2725902
• 关键词: Learning; Human; Brain; Anatomy; Physiology

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.

该项目将集中在两个明确定义的系统中的结构和信息流。首先，我们考虑人类大脑中的一个分层组织系统，该系统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。第二，我们考虑小脑回路如何从这个系统中学习，储存表征，使这种信息处理成为熟练的，无意识的和自动的（参见，机器学习中的摊销）。这两个系统组成了皮质-小脑系统--人类大脑中最大和最突出的网络之一。将有三种密切结合的办法。首先，这些系统的详细结构将通过分析大的，预先存在的静息态功能MRI数据集进行研究。其次，使用功能性MRI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，理论神经生物学方法来理解层次网络中的信息流和学习相关的可塑性将使用计算建模，由弗里斯顿开发的主动推理框架，谁将监督这方面的通知。我们将根据学生的兴趣和技能量身定制博士学位。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的封锁期间仍然可行，届时数据采集可能无法进行。学生将在皇家霍洛威，在那里他们将与Ramnani的研究小组成员进行互动。将定期访问弗里斯顿和他的研究小组在威康信托中心神经成像，伦敦大学学院。研究设施（皇家霍洛威）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。