Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain.
Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain.
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DOI：
10.3389/fncom.2016.00024
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发表时间：
2016
影响因子：
3.2
通讯作者：
Stringer SM
中科院分区：
文献类型：
--
作者：
Ahmad N;Higgins I;Walker KM;Stringer SM
试图解释音高的感知特性已经被证明是一个困难的问题。诱发音高的声音范围很广，而且神经生理学研究对音高是如何被大脑编码的缺乏一致性，这使得这种尝试变得更加困难。在描述可以处理音高的潜在神经机制时，已经提出并实现了许多神经网络。然而，尚未证明具有生物学上准确的耳蜗输入的无监督神经网络。本文提出了一个简单的系统，其中音高代表神经元产生在一个生物学上合理的设置。神经网络学习的pestrian无监督制度的实施，这些证明是足够的，在识别音调的声音与各种频谱配置文件，包括声音与丢失的基本频率和迭代波纹噪声。
Attempting to explain the perceptual qualities of pitch has proven to be, and remains, a difficult problem. The wide range of sounds which elicit pitch and a lack of agreement across neurophysiological studies on how pitch is encoded by the brain have made this attempt more difficult. In describing the potential neural mechanisms by which pitch may be processed, a number of neural networks have been proposed and implemented. However, no unsupervised neural networks with biologically accurate cochlear inputs have yet been demonstrated. This paper proposes a simple system in which pitch representing neurons are produced in a biologically plausible setting. Purely unsupervised regimes of neural network learning are implemented and these prove to be sufficient in identifying the pitch of sounds with a variety of spectral profiles, including sounds with missing fundamental frequencies and iterated rippled noises.