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.

EIA 0130746-GILES J. LAURENT-CALIFORNIA技术研究所 - 嗅觉系统中的生物计算：实验，理论和模拟电路建模型识别（例如，面部，语音识别）仍然是计算机科学最困难的问题之一。 气味是复杂的物理物体（分子的混合物），但被视为奇异物体（例如咖啡，茉莉花）。由于嗅觉电路的设计在各种动物物种（从昆虫到哺乳动物）之间的表现相似，因此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。 研究人员是来自加州理工学院和加利福尼亚大学迭戈分校的生物学，物理，计算机科学和电气工程的跨学科团队。 他们正在研究动物中嗅觉处理和认可的基本计算原则，其长期目标是建立“智能”模式存储和识别设备，该目标是使用受神经生物学启发的规则设计的。我们计划研究如何构建简单和以后的缩放，并在频率和频率上进行扩展，并在频率上进行频率，“电子宽阔的身体和蘑菇的范围”和“ bi”的范围，以及一个可能的范围，以及未来的，以及未来的，以及未来的范围，以及未来的范围，以及未来的范围。为人类用途提供新的计算范例。