课题基金 / 基金详情

A Novel Rodent Model for the Neurophysiology of Visual Object Recognition

A Novel Rodent Model for the Neurophysiology of Visual Object Recognition
用于视觉对象识别神经生理学的新型啮齿动物模型
批准号:
0947777
负责人:
David Cox
金额:
$15.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-09-01 至 2011-08-31

项目摘要

项目成果

David Cox的其他基金

相似基金

相关文献

中文摘要
翻译
人类毫不费力地识别视觉对象,很容易忽视这是多么令人印象深刻的计算壮举。目前,人们对大脑如何实现强大的视觉物体识别知之甚少,而再现这种能力仍然是构建有用的计算机视觉系统的主要障碍。在神经生物学研究中,啮齿动物模型因其优越的可及性和广泛使用的强大实验技术而长期受到重视。虽然啮齿类动物传统上没有被用作物体视觉的模型系统,但最近的行为证据表明,老鼠具有令人惊讶的高级视觉物体识别能力。在这些发现的基础上,本项目试图利用微电极直接记录大鼠视觉系统中的神经元来填补我们对视觉神经元机制的知识空白。这项工作具有很大的潜力,将啮齿动物作为研究物体识别神经生理学的一个新的强大模型。一个更简单、更容易获得的模型的可用性可以极大地加速破译大脑高级视觉的计算基础的进展,这反过来又可以为机器人和机器理解图像的人工视觉系统的构建提供信息。该项目还将为一名博士后和两名本科生提供培训机会,他们将直接参与数据收集和分析。
英文摘要
Humans recognize visual objects so effortlessly that is easy to overlook what an impressive computational feat this represents. At present, little is known about how the brain achieves robust visual object recognition, and reproducing this ability remains a major stumbling block in the construction of useful computer vision systems. In neurobiological research, rodent models have long been valued for their superior accessibility, with a wide range of powerful experimental techniques in widespread use. While rodents have not traditionally been used as a model system for object vision, recent behavioral evidence suggests that rats possess surprisingly advanced visual object recognition abilities. Building on these findings, the present project seeks to fill gaps in our knowledge of the neuronal mechanisms of vision using microelectrodes to record directly from neurons in the rat visual system. This work holds great potential to establish rodents as a new and powerful model for studying the neurophysiology of object recognition. The availability of a simpler, more accessible model can greatly accelerate progress in deciphering the computational underpinnings of high-level vision in the brain, which can, in turn, inform the construction of artificial vision systems for robotics and machine understanding of images. The project will also provide training opportunities for one postdoctoral fellow, and two undergraduate students, who will be directly involved in the data collection and analysis.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
I-Corps: Honest Signals-Machine Learning for Job Candidate Assessment
  • 批准号:
    1511655
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2015
  • 负责人:
    David Cox
  • 依托单位:
RI: Medium: Deep Annotation: Measuring Human Vision to Improve Machine Vision
  • 批准号:
    1409097
  • 项目类别:
    Standard Grant
  • 资助金额:
    $67.41万
  • 财政年份:
    2014
  • 负责人:
    David Cox
  • 依托单位:
RI: Medium: Collaborative Research: Unlocking Biologically-Inspired Computer Vision: A High-Throughput Approach
  • 批准号:
    0963668
  • 项目类别:
    Standard Grant
  • 资助金额:
    $41.0万
  • 财政年份:
    2010
  • 负责人:
    David Cox
  • 依托单位:
Valley Geometry Seminar
  • 批准号:
    9703485
  • 项目类别:
    Standard Grant
  • 资助金额:
    $1.2万
  • 财政年份:
    1997
  • 负责人:
    David Cox
  • 依托单位:
海外基金