CRCNS: Hierarchical Computations for Vocal Communication.

CRCNS：语音通信的分层计算。

• 批准号: 9471964
• 负责人: Frederic E. THEUNISSEN
• 金额: $ 20.81万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-03 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9471964
• 关键词: Acoustics; Active Listening; Amygdaloid structure; Animal Communication; Animals; Area; Auditory; Auditory area; Auditory system; Autistic Disorder; Behavior; Behavioral; Biological Models; Birds; Brain; Callback; Categories; Chronic; Classification; Classification Scheme; Cochlear Implants; Communication; Comprehension; Data; Databases; Devices; Dimensions; Electrodes; Elements; Emotional; Germany; Goals; Hearing Aids; Heart Rate; Human; Information Theory; Institutes; Laboratories; Learning Disabilities; Measurement; Measures; Mental disorders; Methods; Modeling; Motion; Nature; Neurons; Pair Bond; Pattern; Perception; Physiological; Plant Leaves; Principal Investigator; Production; Research; Response to stimulus physiology; Semantics; Signal Transduction; Songbirds; Speech; Stereotyping; Stream; Structure; System; Technology; Testing; Time; Trees; Variant; Wireless Technology; auditory processing; behavior measurement; behavioral response; cortex mapping; design; digital; information framework; information organization; light weight; neural circuit; neural correlate; neural model; neurophysiology; next generation; non-linear transformation; novel; relating to nervous system; response; sobriety; social; sound; success; vocalization; zebra finch

Vocal communication relies on the production and perception of stereotyped signals that carry particular messages. Bioacousticians studying animal communication and linguists studying human speech have used a unique simple classification scheme for these information-bearing sound elements: phonemes in speech and call categories in animal calls. Although appealing from a computational perspective, classification of communication sounds into a fixed number of phonemes or call types fails to capture all of the complexities of the vocal signaling used by humans or animals. At the same time, this core view has guided past neuroscientific research, including ours: auditory neuroscientists and neurolinguists have searched with mixed success for high-level auditory neurons or regions that are selective for call types or phonemes and invariant to variations of sounds within these groups. One of the sobering lessons of this past research is that we have yet to find a cortical map representing phonemes or call types. In addition, the non-linear transformations yielding invariant and selective responses for certain phonemes/call types remain to be described. For this project, we propose to explore a broader hypothesis of auditory signaling and perception and explore its neural correlate. Our hypothesis is that signaling is organized in an information hierarchy that is best represented as a tree classification scheme. This hierarchical organization of information is found in the acoustics, in the behavioral responses and in the neural representations and computations performed by the auditory system and association areas. We propose to test this hypothesis and reveal the neural representations of communication signals using songbirds as a model system. In this collaborative proposal, we will 1) develop a novel wireless device that will combine array neural recordings, physiological measurements and acoustical recordings, 2) study the hierarchical organization of a complete repertoire as revealed by the acoustical structure of calls, 3) measure the physiological and behavioral responses to these communication sounds, 4) measure the neural representation for these communication sounds and 5) model this communication system using our hierarchical information hypothesis as a guiding principle. This analysis will elucidate not only the nature of the meaning in communication signals but also how the sound to meaning transformations are performed by the brain.

声带交流依赖于刻板信号的生产和感知，这些信号带有特定的信号 消息。研究动物交流和语言学家研究人言语的生物声学家已经使用了 这些信息带有的声音元素的独特简单分类方案：语音中的音素 并在动物呼叫中呼叫类别。尽管从计算角度吸引人，但 固定数量的音素或通话类型的通信声音无法捕获所有复杂性 人类或动物使用的人声信号。同时，这种核心视图引导了过去 神经科学研究，包括我们的神经科学研究：听觉神经科学家和神经语言学家已经搜索了 对于呼叫类型或音素选择性的高级听觉神经元或区域的混合成功， 这些组中声音的变化不变。过去研究的清​​醒课程之一是 我们尚未找到代表音素或呼叫类型的皮质图。另外，非线性 对某些音素/呼叫类型产生不变和选择性响应的转换仍为 描述。对于这个项目，我们建议探讨听觉信号和感知的更广泛的假设 并探索其神经相关性。我们的假设是，信号是在信息层次结构中组织的 最好表示为树格分类方案。在 声学，行为反应以及在神经表示和计算中 听觉系统和协会领域。我们建议检验这一假设并揭示神经 使用鸣禽作为模型系统的通信信号表示。在此协作建议中 我们将1）开发一种新型的无线设备，该设备将结合阵列神经记录，生理 测量和声学记录，2）研究完整曲目的等级组织 通过呼叫的声学结构揭示，3）衡量对生理和行为反应 这些通信声音，4）测量这些通信声音的神经表示和5） 使用我们的分层信息假设作为指导原则进行建模。这 分析不仅会阐明通信信号中含义的性质，还将阐明声音 含义转化是由大脑执行的。