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: 8055676
• 负责人: David J Freedman
• 金额: $ 34.08万
• 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/8055676
• 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; Principal Investigator; 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）：拟议的研究将研究视觉分类的皮质回路机制，学习将视觉刺激分为对象的群体的过程，这些过程就其行为意义而言是等效的。先前的工作表明，前额叶皮层（PFC）中的单个神经元和侧向间（LIP）区域在视觉分类任务中编码刺激的类别成员资格。基于这些发现，我们将结合生物物理现实的神经建模和单单元的记录，从行为猴子，以阐明有关类别学习的机械问题和基于类别的行为。首先，我们将开发一个相互作用的感觉电路和侧面前额叶电路的尖峰网络模型，以阐明延迟匹配类别（DMC）任务（DMC）任务（样品的属性和测试刺激的属性和同一类别的测试范围对deatlib septal and pample（dmsplib）（dmples and pample（DMS）的属性）（是否属于dmc）（dmc）的属性（是否属于dmc）（dmc）的属性？其次，我们将检查如何通过离散训练阶段学习类别，从基于身份的匹配到样本到与刺激的良好类别歧视，在任意类别边界附近。这将使用依赖奖励依赖的突触学习，猴子行为评估和来自培训不同阶段的猴子的单单元记录的模型来完成。第三，我们将在基于身份的DMS与基于类别的DMC之间进行逐审的任务切换，以阐明刺激身份和类别的差异神经编码，以及在LIP和PFC中的视觉分类中的任务规范表示。这些研究将共同​​提供重要的见解，并产生一个计算框架，以了解大脑如何编码视觉刺激的学习意义或类别成员身份。智力优点：如果没有对刺激进行分类或分类的能力，就很难感知和理解世界。概念和语言似乎是不可能的。因此，阐明分类的神经机制是我们寻求对更高认知的神经生物学理解的关键步骤。尽管大脑过程如何处理感官属性（例如方向和运动方向）知之甚少，但对大脑如何实现更多抽象知识获取（例如如何通过学习将属性分为类别，以及基于类别行为的计算优势）的知之甚少。在神经电路层面上，对这些问题的机械理解需要一致的计算和实验性努力。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步，具有广泛的影响。我们高度有希望的初步计算，行为和神经元研究已经验证了我们的方法，并确保该项目的所有方面都具有很高的成功。教育和研究活动的更广泛的影响和整合：两个PI都积极参与教学。 Wang博士为耶鲁大学的新物理/工程/生物学（PEB）综合研究生课程授课。 Freedman博士正在为研究生和本科生准备称为“神经数据分析方法中的方法”的新研讨会课程。课程和练习将围绕在此处提出的实验期间对其实验室收集的实际数据进行计算和统计分析。 Wang博士是神经网络建模，国际神经信息协调设施（INCF）的描述标准的监督委员会成员。在他的实验室中开发的模型将提供给计算社区。 PI的代表性不足的团体的扩大参与具有良好的记录，这些记录是招募和指导人数不足的群体的学生。此时，Wang博士有一位女研究生和一位女性博士后研究员（Tatiana Engel博士，他将在其实验室中率领拟议的研究）。在过去的两年中，弗里德曼博士实验室的四个研究生来自代表性不足的群体（一个是非裔美国人，其他是女性）。向公共公共场所推广 - 两个PI都活跃于外展活动。王博士在纽黑文的霍普金斯学校为大脑讲座。 Freedman博士参与了芝加哥Kenwood Academy Public Sc​​hool的“年轻科学家培训计划”，“年轻科学家培训计划”的“科学和技术外展与指导计划”。我们的工作着重于学习和记忆的大脑机制，这一主题既可以访问又引起公众的关注。对于我们的宣传和指导工作，我们将使用拟议的工作期间生成的数据来提供有关大脑如何学习和过程的视觉信息的教育演示，而这些信息将被外行受众访问。这些演示将用于K-12课堂演示文稿，也可以在线使用。