Cortical Mechanisms of Visual Category Recognition and Learning

描述（由申请人提供）：人类和其他高级动物具有令人印象深刻的能力，能够识别各种感官刺激的行为意义或类别成员。这种能力，这是破坏了一些大脑疾病和条件，如阿尔茨海默氏症，精神分裂症，中风，注意力缺陷障碍，是至关重要的，因为它使我们能够适当地回应连续流的刺激和事件，我们遇到的互动与环境。当然，我们并不是生来就有一个内置的有意义的类别库，比如“桌子”和“椅子”，我们是预先编程识别的。相反，我们学会通过经验来认识这些刺激的意义。这里提出的研究的目标是走向一个更详细的了解大脑机制的学习和识别视觉类别。最近，我们发现的证据表明，后顶叶皮层在编码视觉刺激的类别成员中起着令人惊讶的直接作用。在这些研究中，我们记录了顶叶皮层的神经元在执行分类任务，其中360度的运动方向被分为两个任意类别，分为一个学习的类别边界。这些记录显示，顶叶神经元强大的编码刺激，根据他们的学习类别的成员资格，这表明顶叶视觉表征可以反映抽象信息的学习意义的视觉刺激。本研究的目的是从机制上理解视皮层中的视觉特征表征如何转化为顶叶皮层中的类别编码，并确定类别学习过程中神经元类别信号的真实的发展。虽然我们对大脑如何处理简单的感觉特征（如颜色、方向和运动方向）了解很多，但对大脑如何学习和表示刺激的含义或类别却知之甚少。更好地理解视觉学习和分类对于解决许多大脑疾病和病症（例如中风、阿尔茨海默病、注意力缺陷障碍、精神分裂症和中风）至关重要，这些疾病和病症使患者在需要视觉学习、识别和/或评估并适当地对感官信息做出反应的日常任务中受损。该项目的长期目标是通过帮助开发对学习，记忆和识别基础的大脑机制的详细了解，指导下一代治疗这些基于大脑的疾病和障碍。这些研究也与理解和解决学习障碍有关，如注意力缺陷障碍和阅读障碍，这些障碍影响了学龄儿童和年轻人的很大一部分。因此，更详细地了解学习和注意力的基本大脑机制可能会对涉及这些认知能力的疾病的原因和潜在治疗提供重要的见解。

DESCRIPTION (provided by applicant): Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or category membership, of a wide range of sensory stimuli. This ability, which is disrupted by a number of brain diseases and conditions such as Alzheimer's disease, schizophrenia, stroke, and attention deficit disorder, is critical because it allows us to respond appropriately to the continuous stream of stimuli and events that we encounter in our interactions with the environment. Of course, we are not born with a built in library of meaningful categories, such as "tables" and "chairs", which we are preprogrammed to recognize. Instead, we learn to recognize the meaning of such stimuli through experience. The goal of the studies proposed here is to move towards a more detailed understanding of the brain mechanisms underlying the learning and recognition visual categories. Recently, we found evidence that the posterior parietal cortex plays a surprisingly direct role in encoding the category membership of visual stimuli. In these studies, we recorded from neurons in the parietal cortex during performance of a categorization task in which 360 degrees of motion directions were grouped into two arbitrary categories that were divided by a learned category boundary. These recordings revealed that parietal neurons robustly encoded stimuli according to their learned category membership, suggesting that parietal visual representations can reflect abstract information about the learned significance of visual stimuli. The goals of the proposed studies are to develop a mechanistic understanding of how visual feature representations in visual cortex are transformed into category encoding in parietal cortex, and to determine how neuronal category signals develop in real time during the category learning process. While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meaning, or category, of stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of brain diseases and conditions (e.g. stroke, Alzheimer's disease, attention deficit disorder, schizophrenia, and stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating and responding appropriately to sensory information. The long-term goal of this project is to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction f school age children and young adults. Thus, a more detailed understanding of the basic brain mechanisms underlying learning and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.