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

基本信息

  • 批准号:
    8055676
  • 负责人:
  • 金额:
    $ 34.08万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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回路机制,即学习将视觉刺激分类为行为意义相同的对象组的过程。先前的研究表明,前额皮质(PFC)和侧顶叶(LIP)区域的单个神经元在视觉分类任务期间对刺激的类别成员进行编码。基于这些发现,我们将结合生物物理现实的神经模型和行为猴子的单单元记录,以阐明有关类别学习和基于类别的行为的机制问题。首先,我们将开发一个相互交互的感觉回路和顶前额叶回路的尖峰网络模型,以阐明延迟匹配类别(DMC)任务的关键神经计算的皮质基础(做样本的属性和测试刺激属于同一类别?)与延迟样本匹配(DMS)任务(样本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来学习类别,从基于身份的样本匹配到在任意类别边界附近的刺激下进行精细类别区分。这将使用具有奖励依赖性突触学习、猴子行为评估和猴子在不同训练阶段的单单元记录的模型来完成。第三,我们将在逐个试验的基础上检查基于身份的 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分神经编码,以及视觉中的任务规则表示。分类,在 LIP 和 PFC 中。总之,这些研究将揭示重要的见解,并产生一个计算框架,用于理解大脑如何编码视觉刺激的学习意义或类别成员资格。智力优点:如果没有对刺激进行分类或归类的能力,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高级认知的神经生物学理解的关键一步。虽然人们对大脑如何处理感觉属性(例如运动的方位和方向)了解很多,但对大脑如何实现更抽象的知识获取却知之甚少,例如如何通过学习将属性分组为类别,以及什么是计算能力。基于类别的行为的优点。在神经回路层面对这些问题的机械理解需要协调一致的计算和实验工作。因此,我们提出的研究计划的结果可能代表该领域的重大进步,具有广泛的影响。我们极具前景的初步计算、行为和神经元研究验证了我们的方法,并确保了该项目的各个方面都有很高的成功可能性。教育和研究活动的更广泛影响和整合:两位 PI 都积极参与教学。王博士在耶鲁大学任教跨院系神经科学研究生项目和新的物理/工程/生物学 (PEB) 综合研究生项目。弗里德曼博士正在为研究生和本科生准备名为“神经元数据分析方法”的新研讨会课程。课程和练习将围绕在他的实验室在此处提出的实验期间收集的真实数据进行计算和统计分析。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。他的实验室开发的模型将提供给计算界。扩大代表性不足群体的参与——两位 PI 都在招募和指导代表性不足群体的学生方面拥有良好的记录。目前,王博士有一名女研究生和一名女博士后(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(一名是非裔美国人,其他是女性)。向公众宣传——两位 PI 都积极开展宣传活动。王博士曾在纽黑文霍普金斯大学做过有关大脑的讲座;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”以及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,这是一个大众容易理解且感兴趣的话题。对于我们的外展和指导工作,我们将使用拟议工作期间生成的数据来制作教育演示,展示大脑如何学习和处理非专业观众可以访问的视觉信息。这些演示将用于 K-12 课堂演示,也可在线观看。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

David J Freedman其他文献

David J Freedman的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('David J Freedman', 18)}}的其他基金

Cortical-Hippocampal Interactions Underlying Rapid Spatial and Non-Spatial Category Learning
快速空间和非空间类别学习背后的皮质-海马相互作用
  • 批准号:
    10456067
  • 财政年份:
    2018
  • 资助金额:
    $ 34.08万
  • 项目类别:
Cortical-Hippocampal Interactions Underlying Rapid Spatial and Non-Spatial Category Learning
快速空间和非空间类别学习背后的皮质-海马相互作用
  • 批准号:
    9983230
  • 财政年份:
    2018
  • 资助金额:
    $ 34.08万
  • 项目类别:
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
  • 财政年份:
    2010
  • 资助金额:
    $ 34.08万
  • 项目类别:
A Novel Software Tool for Controlling Behavioral and Neurophysiological Studies
用于控制行为和神经生理学研究的新型软件工具
  • 批准号:
    7991020
  • 财政年份:
    2010
  • 资助金额:
    $ 34.08万
  • 项目类别:
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:
    8468747
  • 财政年份:
    2010
  • 资助金额:
    $ 34.08万
  • 项目类别:
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:
    8280430
  • 财政年份:
    2010
  • 资助金额:
    $ 34.08万
  • 项目类别:
A Novel Software Tool for Controlling Behavioral and Neurophysiological Studies
用于控制行为和神经生理学研究的新型软件工具
  • 批准号:
    8064690
  • 财政年份:
    2010
  • 资助金额:
    $ 34.08万
  • 项目类别:
Cortical Mechanisms of Visual Category Recognition and Learning
视觉类别识别和学习的皮质机制
  • 批准号:
    8896797
  • 财政年份:
    2009
  • 资助金额:
    $ 34.08万
  • 项目类别:
Cortical Mechanisms of Visual Category Recognition and learning
视觉类别识别和学习的皮质机制
  • 批准号:
    8324280
  • 财政年份:
    2009
  • 资助金额:
    $ 34.08万
  • 项目类别:
Cortical Mechanisms of Visual Category Recognition and learning
视觉类别识别和学习的皮质机制
  • 批准号:
    7731080
  • 财政年份:
    2009
  • 资助金额:
    $ 34.08万
  • 项目类别:

相似海外基金

Research Project 1
研究项目1
  • 批准号:
    10660383
  • 财政年份:
    2023
  • 资助金额:
    $ 34.08万
  • 项目类别:
Evaluation of Cancer Health Activism Network for Greater Equity (CHANGE)
癌症健康行动网络的评估以实现更大的公平(变更)
  • 批准号:
    10665478
  • 财政年份:
    2023
  • 资助金额:
    $ 34.08万
  • 项目类别:
Frugal Science Academy: Training K-12 innovators and democratizing synthetic biology tools
节俭科学院:培训 K-12 创新者并使合成生物学工具民主化
  • 批准号:
    10705579
  • 财政年份:
    2022
  • 资助金额:
    $ 34.08万
  • 项目类别:
A Mechanistic Trial of Dietary Sodium Reduction on Vascular Structure and Function in African Americans
膳食钠减少对非裔美国人血管结构和功能的机制试验
  • 批准号:
    10365668
  • 财政年份:
    2022
  • 资助金额:
    $ 34.08万
  • 项目类别:
A Mechanistic Trial of Dietary Sodium Reduction on Vascular Structure and Function in African Americans
膳食钠减少对非裔美国人血管结构和功能的机制试验
  • 批准号:
    10550263
  • 财政年份:
    2022
  • 资助金额:
    $ 34.08万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了