Suppr超能文献

听觉神经纤维放电率的时域分析

Time-domain analysis of auditory-nerve-fiber firing rates.

作者信息

Secker-Walker H E, Searle C L

机构信息

Research Laboratory of Electronics, MIT, Cambridge 02139.

出版信息

J Acoust Soc Am. 1990 Sep;88(3):1427-36. doi: 10.1121/1.399719.

Abstract

Time-domain analysis of firing-rate data from over 200 fibers from the auditory nerve of cat has been used to estimate the formants of the synthetic-syllable stimuli. Distinct groups of fibers are identified based on intervals between peaks in the fiber firing rates. The large extent of some of these groups--over an octave in terms of characteristic frequency--and the lack of short intervals in the longer-interval groups suggest that the behavior of the nonlinear cochlear filters for these signals is effectively wideband with steep high-frequency cutoffs. The measured intervals within each group are very similar, and correspond to the period of the formant that dominates the group's response. These intervals are used to estimate the dynamic speech formants. The overall formant estimates are better than those of the previous spectral analyses of the neural data, and the details of lower-formant dynamics are tracked more precisely. The direct temporal representation of the formant in contrasted with the diffuse spectral representation, the dependence of spectral peaks on nonformant parameters, and the distortion of the spectrum by rectification. It is concluded that a time-domain analysis of the responses to complex stimuli can be an important addition to frequency-domain analysis for neural data, cochlear models, and machine processing of speech.

摘要

对猫听觉神经中200多条纤维的放电率数据进行时域分析,已被用于估计合成音节刺激的共振峰。根据纤维放电率峰值之间的间隔来识别不同的纤维组。其中一些组的范围很大——就特征频率而言超过一个倍频程——而且较长间隔组中缺乏短间隔,这表明这些信号的非线性耳蜗滤波器的行为实际上是具有陡峭高频截止的宽带行为。每组内测得的间隔非常相似,并且对应于主导该组响应的共振峰的周期。这些间隔被用于估计动态语音共振峰。总体共振峰估计比之前对神经数据的频谱分析更好,并且较低共振峰动态的细节被更精确地追踪。共振峰的直接时间表示与扩散频谱表示、频谱峰值对非共振峰参数的依赖性以及整流对频谱的失真形成对比。得出的结论是,对复杂刺激响应的时域分析对于神经数据、耳蜗模型和语音机器处理而言,可能是频域分析的重要补充。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验