Scale-Dependent Processing of Clustered Sensory Signals

聚类感觉信号的尺度相关处理

• 批准号: 0422073
• 负责人: Mark Nelson
• 金额: $ 84.99万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0422073&HistoricalAwards=false
• 关键词: Scale; Dependent; Processing; Clustered; Sensory

In natural environments, sensory signals often arise from clustered sources. For example, during the mating season, the auditory cues that guide a female frog to a particular male are embedded in a dense chorus arising from hundreds of calling males of different species. Clustered signals pose a significant challenge for biological systems as well as for intelligent neural prostheses and machine perception systems. For example, an intelligent hearing aid should exhibit robust performance in cluttered environments with other voices in the background. A multidisciplinary team of investigators will explore the neural mechanisms and computational algorithms that animals use to detect, identify, and localize individual signals embedded in an ensemble of similar signals. Experimental studies will focus on the auditory-mediated approach of female frogs to a mating chorus and the electrosensory-mediated approach of electric fish to a cluster of prey. These studies will include audio recordings of frog choruses at different distances from natural mating ponds and electrical recordings of active electrosensory signals arising from swarms of zooplankton. Approach trajectories will be recorded and analyzed using radio telemetry (frogs) and infrared video recordings (fish, frogs). Theoretical analysis will draw on algorithms from computer vision including multiscale grouping and segmentation, target detection and tracking, active vision, texture analysis, and motion and structure estimation. Neural correlates of clustered signal processing will be assessed through electrophysiological studies on auditory nerve and midbrain neurons in frogs and primary electrosensory afferent and hindbrain neurons in fish. This project will incorporate interdisciplinary training (engineering and neurobiology) for students, a web site for sharing data, software and references in this field and could contribute to the development of improved machine devices for speech recognition and video surveillance.

在自然环境中，感觉信号通常来自集群源。例如，在交配季节，引导雌蛙找到特定雄蛙的听觉线索嵌入了数百只不同物种的雄蛙发出的密集合唱中。混沌信号对生物系统以及智能神经假体和机器感知系统提出了重大挑战。例如，智能助听器应该在背景中有其他声音的杂乱环境中表现出强大的性能。一个多学科的研究团队将探索动物用来检测、识别和定位嵌入在类似信号集合中的单个信号的神经机制和计算算法。实验研究将集中在雌蛙的交配合唱团和电鱼的电介导的方法，一群猎物的嗅觉介导的方法。这些研究将包括在离自然交配池塘不同距离处对青蛙合唱的录音，以及对成群浮游动物发出的活跃电感觉信号的电子记录。将使用无线电遥测（蛙类）和红外视频记录（鱼、蛙类）记录和分析进场轨迹。理论分析将借鉴计算机视觉算法，包括多尺度分组和分割，目标检测和跟踪，主动视觉，纹理分析，运动和结构估计。集群信号处理的神经相关性将通过电生理研究的听觉神经和中脑神经元的青蛙和初级电感觉传入和后脑神经元的鱼。该项目将为学生提供跨学科培训（工程学和神经生物学），建立一个网站，分享这一领域的数据、软件和参考资料，并有助于开发改进的语音识别和视频监控机器设备。