Characterizaton of Non-linear Auditory Receptive Fields

非线性听觉感受野的表征

• 批准号: 7854079
• 负责人: DANIEL MARGOLIASH
• 金额: $ 18.83万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-17 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7854079
• 关键词: Acoustics; Affect; Alien; Auditory; Auditory area; Auditory system; Behavior; Behavioral; Biological Models; Birds; Cells; Classification; Communication; Complex; Data; Development; Diagnostic; Dimensions; Exhibits; Food; Frequencies; Human; Learning; Length; Lesion; Linear Regressions; Measures; Memory; Methodology; Methods; Modeling; Nervous system structure; Neurons; Noise; Non-linear Models; Performance; Physiological; Plastics; Population; Positioning Attribute; Procedures; Process; Property; Prosthesis; Psychological reinforcement; Psychometrics; Relative (related person); Research; Research Personnel; Rewards; Sensory Physiology; Signal Transduction; Speech; Statistical Methods; Statistical Models; Stimulus; Structure; Sturnus vulgaris; System; Techniques; Testing; Time; Training; base; behavior test; insight; neurophysiology; novel; programs; receptive field; reconstruction; research study; response; sensory system; sound

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.

听觉神经元的特征在于它们的感受野特性，其描述了听觉神经元的组织。 参数，如神经元响应的刺激的频率和幅度。感受野是 相对较好地描述了听觉系统较低水平的神经元，但高阶神经元 表现出复杂的非线性，这阻碍了系统的量化。高阶特征 然而，感受野是理解正常和异常听觉感知的基础。 过程，并且还可以帮助优化假体装置的性能。一个有吸引力的模型系统， 据描述，椋鸟的高阶听觉神经元对声音具有高度选择性， 嵌入自然信号（歌曲）中的对象（“图案”）。最近的结果表明， 对原型语法结构表现出敏感性的椋鸟的分析能力。拟议 研究，新的统计技术将与“cmHV”神经元的生理记录相结合， 其歌曲识别行为受操作性控制的椋鸟。在第一个实验中，统计 将开发用于学习基序结构的有效基组和用于估计基序结构的有效基组的方法。 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 贝叶斯推理模型和结合非线性时间动态。在第二个实验中， 操作性程序将用于识别基序的自然特征和基序序列的参数 椋鸟在歌曲识别行为中所使用的。在第三个实验中，cmHV响应将是 特征，重点是分析行为测试所确定的特征，并使用 非线性特性表征的统计方法。成功开发这一方法 将为复杂的声音对象和序列识别提供定量的神经生理学见解 行为，同时开发一种通用的方法，以皮层感觉生理学。

项目成果

Sleep-dependent consolidation of auditory discrimination learning in adult starlings.

• DOI: 10.1523/jneurosci.4237-09.2010
• 发表时间: 2010-01-13
• 期刊: The Journal of neuroscience : the official journal of the Society for Neuroscience
• 作者: Brawn TP;Nusbaum HC;Margoliash D
• 通讯作者: Margoliash D