Cortical retinotopy and specificity of perceptual learning revealed by ERP

在我们的日常生活中，我们的大脑不断适应新的视觉体验，并学习新任务的最佳解决方案。了解神经可塑性的机制对于开发有效的训练范例至关重要。在美国国家科学基金会（National Science Foundation）的支持下，加州大学伯克利分校（University of California, Berkeley）的斯坦利·克莱因（Stanley Klein）教授正在开展一项研究项目，开发揭示支持神经可塑性的大脑动力学和大脑结构的新方法。在过去的20年里，功能性磁共振成像（fMRI）已经确定了许多涉及感知、认知、注意力、决策、行动和其他功能的大脑区域。但功能磁共振成像只能观察到大脑的缓慢变化，大约是几秒钟。大脑中许多重要的神经处理的速度要快一千倍。该项目的一个目标是开发新的方法，使脑电图（EEG）产生的人类脑电波能够用于识别大脑区域并在毫秒时间尺度上跟踪其活动。这项提议的研究利用了最近开发的计算机算法，该算法利用人类大脑的详细折叠模式来分离脑电图活动的产生器。新方法的第一个应用是研究在学习具有感知挑战性的视觉任务时，紧密间隔的大脑区域的皮层活动如何变化。然而，这项研究的重要性远远超出了视觉感知学习的领域：一旦科学家能够可靠地测量紧密相连的大脑区域的皮层活动，他们将能够在认知活动期间跟踪不同大脑区域的活动流动的时间细节。对模式的视觉感知是我们日常活动的基础，当它被破坏时，往往会对生活质量造成特别严重的影响。这项神经可塑性研究的目标是开发方法，不仅可以为这些健康问题提供信息，还可以将视觉学习过程应用于普通教育、职业培训和日常生活的许多其他方面。在项目期间，直接的教育影响是研究生和本科生参与研究。本研究构成了研究生论文研究的核心内容。克莱因教授的实验室是校园本科生研究学徒计划（URAP）的积极参与者，每学期招收几名新的本科生。实验室开会讨论相关的研究问题和发现。学生将学习脑电图记录技术的各个方面，并获得第一手的研究经验。在实验室工作后，许多URAP学生决定并成功地接受高等教育，无论是基础研究还是临床医学。正在开发的技术和软件将在互联网上发布，供其他研究人员使用。这些方法足够通用，对研究感觉或高级认知功能的研究者很有用。

In our daily lives our brains are constantly adapting to new visual experiences and learning optimal solutions to new tasks. Understanding the mechanisms of neural plasticity is crucial for developing effective training paradigms. With support from the National Science Foundation, Professor Stanley Klein of the University of California, Berkeley, is carrying out a research project to develop new methods for revealing brain dynamics and brain structures that support neural plasticity. In the past 20 years, functional magnetic resonance imaging (fMRI) has identified numerous brain regions that are involved in perception, cognition, attention, decision-making, action, and other functions. But fMRI has access only to slow brain changes, on the order of seconds. Much of the important neural processing by the brain takes place a thousand times faster. A goal of this project is to develop new methods that will enable human brain waves from electroencephalography (EEG) to be used for identifying brain areas and tracking their activity on the milli-second time scale. The proposed research makes use of recently developed computer algorithms that use the detailed folding patterns of the human brain to isolate the generators of EEG activity. The first application of the new methods is investigations of how cortical activity in closely spaced brain areas change as a product of learning perceptually challenging visual tasks. However, the importance of this research extends well beyond the domain of visual perceptual learning: Once scientists can reliably measure cortical activity in closely connected brain areas, they will be able to follow in temporal detail the flows of activity across distinct brain areas during cognitive activity.The visual perception of patterns is so fundamental to our daily activities that when it is disrupted, the disruption often leads to an especially devastating impact on quality of life. This research on neural plasticity has the goal to develop methods that could inform not only these health issues but the visual learning process as applied to general education, vocational training, and many other aspects of daily living. During the project, the immediate educational impact is the involvement of graduate and undergraduate students in the research. The research forms the core dissertation research for graduate students. Professor Klein's laboratory is an active participant in the campus undergraduate research apprentice program (URAP) and admits several new undergraduate students each semester. The laboratory meets to discuss relevant research questions and findings. The students learn all aspects of EEG recording technology and get first hand research experience. After working in the laboratory, many URAP students have decided on and succeeded in higher education, be it basic research or clinical medicine. The techniques and software that are being developed will be posted on the internet for other researchers to use. The methods are sufficiently general to be useful to investigators who study sensory or higher cognitive functions.