Computational Investigations of Temporal Odor Processing Mechanisms to Serve Chemotactic Orientation: Olfactory Guided Orientation of the American Lobster HOARUS AMERICANUS

为趋化定向提供时间气味处理机制的计算研究：美国龙虾 HOARUS AMERICANUS 的嗅觉引导定向

• 批准号: 9631665
• 负责人: Jelle Atema
• 金额: $ 24.86万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9631665&HistoricalAwards=false
• 关键词: Computational; Investigations; Temporal; Odor; Processing

9631665 Atema The mechanisms by which animals detect objects by their sense of smell are largely unknown. In natural settings odor plumes are broken up by turbulence into many discontinuous odor patches, hence locating an odor source is a very hard problem. Computational approaches, using artificial neural nets have proven highly effective in recognizing complex patterns. Although most studies of animal pattern recognition are based on visual or acoustic inputs, the research funded this grant is focused on recognition of temporal odor patterns. It is proposed that the rapid concentration fluctuations that animals encounter in turbulent odor plumes contain spatial information and that animals use such dynamic patterns to guide them to the source of the odor. The computational research funded by this grant explores candidate search strategies for such gradients and tests these strategies under various plume conditions. In addition to increasing our understanding of how animals use their olfactory systems, the results obtained by this study are expected to be applicable to robot guidance in underwater search missions, such as locating sources of pollution in the natural environment.

9631665阿特玛动物通过嗅觉探测物体的机制在很大程度上是未知的。在自然环境中，气味羽流被湍流分解成许多不连续的气味斑块，因此确定气味源是一个非常困难的问题。使用人工神经网络的计算方法已被证明在识别复杂模式方面非常有效。虽然大多数动物模式识别的研究都是基于视觉或听觉输入，但这项资助的研究主要集中在识别时间气味模式上。提出动物在湍流气味羽流中遇到的快速浓度波动包含空间信息，动物利用这种动态模式来引导它们找到气味的来源。由该基金资助的计算研究探索了这些梯度的候选搜索策略，并在各种羽流条件下测试了这些策略。除了增加我们对动物如何使用嗅觉系统的理解之外，本研究获得的结果有望应用于水下搜索任务中的机器人指导，例如定位自然环境中的污染源。