CRCNS: Hierarchical Computations for Vocal Communication.

声音交流依赖于产生和感知带有特定信息的刻板信号， 充分和传播研究动物交流的生物声学家和研究人类语言的语言学家 一个独特的简单分类方案，这些信息承载的声音元素：语音中的音素 和动物叫声的分类。虽然从计算的角度来看很吸引人， 将通信声音转换为固定数量的音素或呼叫类型无法捕捉所有的复杂性 人类或动物使用的声音信号。与此同时，这一核心观点指导了过去 神经科学研究，包括我们的：听觉神经科学家和神经语言学家已经搜索了 对呼叫类型或音素有选择性的高级听觉神经元或区域的混合成功， 不受这些组内声音变化的影响。过去的研究中有一个发人深省的教训， 我们还没有找到代表音素或呼叫类型的皮层地图。此外，非线性 对于某些音素/呼叫类型产生不变和选择性响应的变换仍然有待于 介绍了对于这个项目，我们建议探索一个更广泛的听觉信号和感知的假设 并探索其神经关联。我们的假设是，信号是组织在一个信息层次结构， 最好用树分类法来表示。这种信息的层次结构可以在 声学，在行为反应和神经表征和计算中， 听觉系统和联想区。我们建议测试这一假设，并揭示神经 使用鸣禽作为模型系统的通信信号的表示。在这份合作提案中， 我们将1）开发一种新型的无线设备，将联合收割机阵列神经记录，生理 测量和录音，2）研究一个完整的剧目的层次组织， 通过声音结构的声音揭示，3）测量生理和行为反应， 这些通信声音，4）测量这些通信声音的神经表征，以及5） 使用我们的分层信息假设作为指导原则来建模这个通信系统。这 分析将阐明不仅在通信信号的意义的性质，但也如何声音 意义的转换是由大脑完成的。

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.