CRCNS: Uncovering neurla circuit mechanisms of category computation and learning

CRCNS：揭示类别计算和学习的神经回路机制

• 批准号: 8152255
• 负责人: David J Freedman
• 金额: $ 32.34万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8152255
• 关键词: Academy; African American; Area; Behavior; Behavior assessment; Behavioral; Biology; Brain; Categories; Chicago; Code; Cognition; Communities; Computer Simulation; Data; Data Analyses; Discrimination; Education; Educational process of instructing; Educational workshop; Engineering; Ensure; Exercise; Female; General Population; Goals; Heterogeneity; Individual; International; Knowledge acquisition; Laboratories; Language; Lateral; Learning; Memory; Mentors; Mentorship; Methods; Modeling; Monkeys; Motion; Neural Network Simulation; Neurobiology; Neurons; Neurosciences; Performance; Physics; Physiological; Play; Postdoctoral Fellow; Prefrontal Cortex; Process; Recruitment Activity; Relative (related person); Research; Research Activity; Rewards; Role; Sampling; Schools; Science; Scientist; Sensory; Sensory Process; Signal Transduction; Staging; Stimulus; Students; Synapses; Synaptic plasticity; System; Technology; Testing; Time; Training; Training Programs; Visual; Woman; Work; abstracting; base; computer framework; graduate student; insight; interest; lectures; member; models and simulation; network models; neural circuit; neural model; neuroinformatics; neuromechanism; neurophysiology; outreach; programs; relating to nervous system; research study; success; visual information; visual stimulus

DESCRIPTION (provided by applicant): The proposed research will investigate the cortical circuit mechanisms of visual categorization, the process of learning to classify visual stimuli into groups of objects that are equivalent in terms of their behavioral significance. Previous work revealed that individual neurons in the prefrontal cortex (PFC) and the lateral interparietal (LIP) area encode the category membership of stimuli during visual categorization tasks. Built on these findings, we will combine biophysically-realistic neural modeling and single-unit recording from behaving monkeys, to elucidate the mechanistic questions concerning category learning and category-based behavior. First, we will develop a spiking network model of the reciprocally interacting sensory circuit and parieto-prefrontal circuit, to elucidate the cortical basis of key neural computations underlying a delayed match-to-category (DMC) task (do the attributes of a sample and a test stimulus belong to the same category?) versus delayed match-to-sample (DMS) task (are the attributes of the sample and test identical?). Second, we will examine how categories are learnt through discrete training stages, from identity-based match-to-sample to fine category discrimination with stimuli near an arbitrary category boundary. This will be done using models endowed with reward-dependent synaptic learning, monkey behavioral assessment and single-unit recordings from monkeys at different stages of training. Third, we will examine task switching, on a trial-by-trial basis, between the identity-based DMS versus category-based DMC, to clarify the differential neural coding of stimulus identity and category, as well as task-rule representation in visual categorization, in the LIP and PFC. Together, these studies will shed important insights and yield a computational framework for understanding how the brain encodes the learned significance, or category membership, of visual stimuli. Intellectual Merits: Without the ability to classify or categorize stimuli, it would be difficult to perceive and comprehend the world; concepts and language would seem impossible. Therefore, elucidating the neural mechanisms of categorization is a crucial step in our quest for a neurobiological understanding of higher cognition. While much is known about how the brain processes sensory attributes (such as orientation and direction of motion), much less is known about how the brain achieves more abstract knowledge acquisition such as how attributes are grouped into categories through learning, and what are the computational advantages of category-based behavior. A mechanistic understanding of these issues, at the neural circuit level, necessitates a concerted computational and experimental effort. Thus, the results of our proposed research program are likely to represent a significant advance in this area, with broad implications. Our highly promising preliminary computational, behavioral and neuronal studies have validated our approach, and have ensured that all aspects of this project have a high likelihood of success. Broader Impacts and Integration of Education and Research Activities: Both PIs are actively involved with teaching. Dr. Wang teaches for the Interdepartmental Neuroscience graduate program and for the new Physics/Engineering/Biology (PEB) integrated graduate program at Yale. Dr Freedman is preparing new workshop course called "Methods in neuronal data analysis" to both graduate and undergraduate students. Lessons and exercises will revolve around computational and statistical analysis of real data collected in his laboratory during the experiments proposed here. Dr Wang is a member of the Oversight Committee for Description Standards in Neural Network Modeling, International Neuroinformatics Coordinating Facility (INCF). Models developed in his lab will be made available to the computational community. Broaden Participation of under-represented groups-Both PI have a strong track record of recruiting and mentoring students from under-represented groups. At this time, Dr. Wang has a female graduate student and a female postdoctoral fellow (Dr Tatiana Engel who will spearhead the proposed research in his laboratory). Over the past two years four graduate students in Dr. Freedman's laboratory are from underrepresented groups (one is African American and the others are women). Outreach to general public- Both PIs have been active in outreach. Dr Wang has given lectures on the brain at the Hopkins School in New Haven; Dr Freedman has been involved in the "Science and Technology Outreach and Mentoring Program", "The Young Scientist Training Program", and the student science fair at Kenwood Academy public school, in Chicago. Our work focuses on the brain mechanisms of learning and memory, a topic which is both accessible and of great interest to the general public. For our outreach and mentorship efforts, we will use data generated during the proposed work to produce educational demonstrations of how the brain learns and processes visual information that will be accessible to a lay audience. These demonstrations will be used in K-12 classroom presentations and also available online.

描述（由申请人提供）：拟议的研究将调查视觉分类的皮层电路机制，学习将视觉刺激分类为在行为意义上等同的对象组的过程。先前的工作表明，在视觉分类任务中，前额叶皮层（PFC）和外侧顶间区（LIP）的单个神经元编码刺激的类别成员关系。在这些发现的基础上，我们将结合联合收割机的生物病理学现实的神经建模和单单元记录从行为猴子，阐明有关类别学习和基于类别的行为的机制问题。首先，我们将建立一个神经元相互作用的感觉回路和顶叶-前额叶回路的尖峰网络模型，以阐明延迟类别匹配（DMC）任务（样本和测试刺激的属性属于同一类别吗？）与延迟的样品匹配（DMS）任务（样品和测试的属性是否相同？）。其次，我们将研究如何通过离散的训练阶段，从基于身份的匹配到样本的精细类别歧视与刺激附近的任意类别边界的类别学习。这将使用具有奖励依赖性突触学习的模型、猴子行为评估和来自不同训练阶段的猴子的单个单元记录来完成。第三，我们将在逐个试验的基础上研究基于身份的DMS与基于类别的DMC之间的任务切换，以阐明刺激身份和类别的差异神经编码，以及视觉分类中的任务规则表示，在LIP和PFC中。这些研究将提供重要的见解，并产生一个计算框架，用于理解大脑如何编码学习到的意义，或类别成员资格。智力优势：如果没有对刺激进行分类的能力，就很难感知和理解世界;概念和语言似乎是不可能的。因此，阐明范畴化的神经机制是我们寻求对高级认知的神经生物学理解的关键一步。虽然我们对大脑如何处理感官属性（如运动的方向和方向）了解得很多，但对大脑如何获得更抽象的知识（如属性如何通过学习被分组为类别）以及基于类别的行为的计算优势知之甚少。在神经回路水平上对这些问题的机械理解需要协调一致的计算和实验努力。因此，我们提出的研究计划的结果可能代表了这一领域的重大进展，具有广泛的影响。我们非常有希望的初步计算，行为和神经元研究验证了我们的方法，并确保该项目的各个方面都有很高的成功可能性。更广泛的影响和教育和研究活动的整合：两个PI都积极参与教学。王博士在耶鲁大学教授跨部门神经科学研究生课程和新的物理/工程/生物学（PEB）综合研究生课程。弗里德曼博士正在为研究生和本科生准备一门名为“神经元数据分析方法”的新课程。课程和练习将围绕计算和统计分析的真实的数据收集在他的实验室在这里提出的实验。王博士是国际神经信息学协调机构（INCF）神经网络建模描述标准监督委员会的成员。在他的实验室开发的模型将提供给计算社区。代表性不足的群体的广泛参与-两个PI都有从代表性不足的群体招募和指导学生的良好记录。目前，王博士有一名女研究生和一名女博士后研究员（Tatiana Engel博士将在他的实验室领导拟议的研究）。在过去的两年里，弗里德曼博士实验室的四名研究生来自代表性不足的群体（一名是非洲裔美国人，其他是妇女）。与公众的外联-两个主要新闻机构都积极开展外联活动。王博士在纽黑文的霍普金斯学校做过关于大脑的讲座;弗里德曼博士参与了“科学技术推广和指导计划”、“青年科学家培训计划”和芝加哥肯伍德学院公立学校的学生科学博览会。我们的工作重点是学习和记忆的大脑机制，这是一个公众既容易理解又非常感兴趣的话题。对于我们的推广和指导工作，我们将使用在拟议的工作中产生的数据来制作关于大脑如何学习和处理视觉信息的教育演示，这些演示将对外行观众开放。这些演示将在K-12课堂演示中使用，也可在线使用。