Characterizaton of Non-linear Auditory Receptive Fields

描述（由申请人提供）：听觉神经元的特征在于其感受野特性，其描述了神经元响应的刺激的频率和幅度等参数的组织。对于听觉系统较低层次的神经元，感受野的描述相对较好，但高阶神经元表现出复杂的非线性，这阻碍了系统的量化。然而，高阶感受野的表征对于理解正常和异常的听觉感知过程是至关重要的，并且还可以帮助优化假体设备的性能。一个有吸引力的模型系统已被描述，从而高阶听觉神经元在椋鸟成为高度选择性的声学对象（“图案”）嵌入在自然信号（歌曲）。最近的研究结果牵连显着的序列分析能力的椋鸟表现出敏感的原型语法样结构。在拟议的研究中，新的统计技术将结合生理记录的“cmHV”的椋鸟的歌曲识别行为是在操作控制下的神经元。在第一个实验中，将开发的统计方法学学习有效的基组的基序结构和估计基于特征的感受野与分层非线性回归，使用马尔可夫随机场的贝叶斯推理模型，并纳入非线性时间动态。在第二个实验中，操作程序将被用来识别自然特征的基序和参数的基序序列，椋鸟利用歌曲识别行为。在第三个实验中，cmHV响应将进行表征，重点是分析行为测试确定的特征，并使用统计方法表征非线性特性。这种方法的成功开发将为复杂的声音对象和序列识别行为提供定量的神经生理学见解，同时开发出一种对皮质感觉生理学通用的方法。

DESCRIPTION (provided by applicant): Auditory neurons are characterized by their receptive field properties, which describe the organization of parameters such as frequency and amplitude of stimuli that the neurons respond to. Receptive fields are relatively better described for neurons at lower levels of the auditory system, but higher-order neurons exhibit complex non-linearities that have resisted systematic quantification. Characterization of higher-order receptive fields, however, is fundamental to understanding normal and abnormal auditory perceptual processes, and also may help optimize performance of prosthetic devices. An attractive model system has been described whereby high-order auditory neurons in starlings become highly selective for acoustic objects ("motifs") embedded in natural signals (songs). Recent results implicate remarkable sequence parsing abilities of starlings that exhibit sensitivity to prototypic grammar-like structures. In the proposed research, novel statistical techniques will be combined with physiological recordings of "cmHV" neurons of starlings whose song recognition behavior is under operant control. In the first experiment, the statistical methodologies will be developed for learning efficient basis sets for motif structure and for estimating feature-based receptive fields with hierarchical non-linear regression, using Markov random fields of Bayesian inference models and incorporating non-linear temporal dynamics. In the second experiment, operant procedures will be used to identify natural features of motifs and parameters of motif sequences that starlings utilize in song recognition behavior. In the third experiment, cmHV responses will be characterized, with emphasis on analysis of the features identified by behavioral testing, and using the statistical methods for characterization of non-linear properties. Successful development of this approach would give quantitative neurophysiological insight into complex acoustic object and sequence recognition behavior while developing an approach of general utility to cortical sensory physiology.