Unified probabilistic modelling of adaptive spatial-temporal structures in the human brain

人脑自适应时空结构的统一概率建模

基本信息

批准号：
BB/H012508/1
负责人：
Peter Tino
金额：
$ 78.2万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FH012508%2F1
关键词：
Unified probabilistic modelling adaptive spatial

项目摘要

Learning from experience and adapting our behaviour to new situations is a fundamental skill for our everyday interactions. But what are the brain plasticity mechanisms that mediate an individual's ability to make progress during training on complex tasks? What is it that differentiates `good' from `poor' learners in their ability to adapt? Recent advances in functional brain imaging technology provide us with the unique opportunity to study how the human brain changes with learning. However, the existing methods focus predominantly on modelling brain activity data within a single session rather than across training sessions. As such, these methods are not capable of capturing larger scale dependencies emerging in brain activity as training progresses. We will develop a novel methodology that allows holistic unified modelling of a series of brain imaging data measured during the course of learning. Using this methodology we will study brain changes that result from extensive training on complex visual tasks. Our work will offer scientists and practitioners advanced tools for using brain activity measurements to understand the brain learning mechanisms and how they improve our ability to make complex decisions. The proposed methodology may have predictive power for making inferences about 'prototypical' learning patterns that can be used to predict adaptive behaviour in individuals with different learning strategies and design training schemes tailored to the individuals' abilities and needs. Hence our findings have potential implications for the design of dedicated training programmes that take into account an individual's learning capacity. Such programmes may have applications in education or intervention and rehabilitation in normal and pathological development and ageing (e.g. stroke, neurodegenerative disorders).

从经验中学习并使我们的行为适应新情况是我们日常互动的一项基本技能。但是，调节个体在复杂任务训练中取得进步的能力的大脑可塑性机制是什么？ “好”学习者和“差”学习者的适应能力有何不同？功能性脑成像技术的最新进展为我们提供了研究人脑如何随着学习而变化的独特机会。然而，现有的方法主要侧重于对单个会话中的大脑活动数据进行建模，而不是跨训练会话。因此，这些方法无法捕获随着训练的进行而大脑活动中出现的更大规模的依赖性。我们将开发一种新颖的方法，可以对学习过程中测量的一系列大脑成像数据进行整体统一建模。使用这种方法，我们将研究复杂视觉任务的大量训练所导致的大脑变化。我们的工作将为科学家和从业者提供先进的工具，使用大脑活动测量来了解大脑学习机制以及它们如何提高我们做出复杂决策的能力。所提出的方法可能具有对“原型”学习模式进行推断的预测能力，可用于预测具有不同学习策略的个体的适应性行为，并设计适合个体能力和需求的培训方案。因此，我们的研究结果对于考虑个人学习能力的专门培训计划的设计具有潜在的影响。此类计划可应用于正常和病理发育和衰老（例如中风、神经退行性疾病）的教育或干预和康复。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

DOI：
10.3389/fncom.2016.00117
发表时间：
2016
期刊：
Frontiers in computational neuroscience
影响因子：
3.2
作者：
Alahmadi HH;Shen Y;Fouad S;Luft CD;Bentham P;Kourtzi Z;Tino P
通讯作者：
Tino P

Does money matter in inflation forecasting?

DOI：
10.1016/j.physa.2010.06.015
发表时间：
2010-11-01
期刊：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
影响因子：
3.3
作者：
Binner, J. M.;Tino, P.;Kendall, G.
通讯作者：
Kendall, G.

Learning to predict: exposure to temporal sequences facilitates prediction of future events.

DOI：
10.1016/j.visres.2013.10.017
发表时间：
2014-06
期刊：
VISION RESEARCH
影响因子：
1.8
作者：
Baker, Rosalind;Dexter, Matthew;Hardwicke, Tom E.;Goldstone, Aimee;Kourtzi, Zoe
通讯作者：
Kourtzi, Zoe

Training transfers the limits on perception from parietal to ventral cortex.