Erfanian Saeedi Nafise, Blamey Peter J, Burkitt Anthony N, Grayden David B
NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia.
The Bionics Institute, East Melbourne, Victoria, Australia.
PLoS Comput Biol. 2016 Apr 6;12(4):e1004860. doi: 10.1371/journal.pcbi.1004860. eCollection 2016 Apr.
Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.
音高感知对于理解言语韵律、音乐感知、识别声调语言中的声调以及在嘈杂环境中感知言语都很重要。两种主要的音高感知理论将沿听神经最大神经兴奋的位置以及听觉神经元动作电位（尖峰）的时间模式视为音高线索。本文描述了一种生物物理机制，通过该机制可以从听觉外周产生的尖峰中提取精细结构的时间信息。从尖峰时间中提取有意义的音高相关信息需要专门用于从神经事件中捕获同步或相关活动的神经结构。通过听觉处理的计算模型描述了这种音高处理神经机制的出现。模拟结果表明，基于相关性的、无监督的、基于尖峰的赫布学习形式可以解释识别简单和复杂音调音高所需的神经结构的发展，无论有无基频。时间编码对信号频谱形状的变化具有鲁棒性，因此可以解释音高恒定现象。
