Biological Information Technology Systems - BITS: Biological Computation in Olfactory Systems: Experiments, Theory and Analog Circuit Modeling

生物信息技术系统 - BITS：嗅觉系统中的生物计算：实验、理论和模拟电路建模

• 批准号: 0130746
• 负责人: Gilles Laurent
• 金额: --
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0130746&HistoricalAwards=false
• 关键词: Biological; Information; Technology; Systems; BITS

EIA 0130746-Giles J. Laurent-California Institute of Technology - Biological computation in olfactory systems: experiments, theory and analog circuit modelingPattern recognition (e.g., face, voice recognition) remains one of the most difficult problems for computer science. Odors are complex physical objects (blends of molecules) and yet, are perceived as singular objects (e.g., coffee, jasmine); because olfactory circuit designs appear similar across animal species (from insects to mammals), olfaction constitutes a potentially ideal system to identify key solutions to pattern recognition. The investigators are an interdisciplinary team from Biology, Physics, Computer Science and Electrical Engineering from Caltech and from the University of California-San Diego. They are investigating the basic computational principles of olfactory processing and recognition in animals, with the long-term goal to build "intelligent" pattern storage and recognition devices, designed using rules inspired by neurobiology.We plan to study how to build simple and later, scale up, both in number of processors and in frequency, "electronic antennal lobes and mushroom bodies" so that one might, in the future, adapt and exploit the design of biological pattern recognizers and provide new computational paradigms for human uses.

giles J. Laurent-California Institute of Technology -嗅觉系统中的生物计算：实验、理论和模拟电路建模模式识别（例如，人脸、语音识别）仍然是计算机科学中最困难的问题之一。气味是复杂的物理对象（分子的混合物），然而，被认为是单一的对象（例如，咖啡，茉莉花）；因为所有动物（从昆虫到哺乳动物）的嗅觉回路设计似乎都很相似，嗅觉构成了一个潜在的理想系统，可以识别模式识别的关键解决方案。研究人员是一个跨学科的团队，来自加州理工学院和加州大学圣地亚哥分校的生物学、物理学、计算机科学和电子工程。他们正在研究动物嗅觉处理和识别的基本计算原理，其长期目标是建立“智能”模式存储和识别设备，这些设备使用受神经生物学启发的规则设计。我们计划研究如何在处理器数量和频率上建立简单的，后来扩大规模的“电子天线瓣和蘑菇体”，以便将来可以适应和利用生物模式识别器的设计，并为人类使用提供新的计算范式。