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Predictive Models of Auditory Perception in Human Electrophysiology

机译:人类电生理学中听觉感知的预测模型

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摘要

It has long been thought that sensory systems operate by representing information in a hierarchy of sensory features, and that these features build upon one another. From low-level information such as spectral content, to high-level information such as word content, the sensory system must rapidly extract all of these features from the world. However, the precise nature of these levels of representation, as well as how they interact with one another, is not well-understood. In audition, intermediate sensory representations are often studied in animals, using techniques that treat neurons as a linear filter for incoming sensory inputs. If those inputs are spectro-temporal features (e.g., a spectrogram), then the result is a Spectro Temporal Receptive Field (STRF). This describes how the neural unit in question (e.g., a neuron) will respond to patterns in spectro-temporal space. It has been a crucial tool in understanding sensory processing in low-level neural activity. Using this approach it is also possible to study how this neural representation changes under different experimental conditions. STRF plasticity has been shown in both reward- and context-modulated experiments in animals.;In recent years, it has been suggested that similar techniques may work in modeling the activity of neural signals recorded from humans. As we cannot generally record from single unit activity in humans, this approach relies on proxies for neural activity -- specifically in the high-frequency activity (HFA) of electrocorticography electrodes. This poses a unique opportunity for two reasons: First, human language is a natural stimulus set for studying hierarchical feature representations in the brain. There are many ways to decompose speech into both auditory and linguistic components, and each of these could serve as inputs to the modeling technique described above. Second, humans are especially skilled at using high-level context such as their experience and assumptions about the world in order to change their behavior. This poses a unique opportunity to study the plasticity of speech representations in the brain.;This thesis reports several new approaches towards studying the sensory representation of speech in the human brain, as well as how these representations may change due to experience. It aims to bridge the literature in rodents and songbirds with ideas in human electrophysiology in order to pursue new approaches to studying perception in humans.
机译:长期以来人们一直认为,感觉系统通过在感觉特征的层次结构中表示信息来进行操作,并且这些特征彼此建立。从低级信息(例如频谱内容)到高级别信息(例如单词内容),感觉系统必须迅速从世界中提取所有这些特征。但是,这些表示层的确切性质以及它们如何相互影响,尚不为人所知。在试听中,经常在动物中研究中间感觉表示,使用将神经元视为传入感觉输入的线性滤波器的技术。如果那些输入是光谱时态特征(例如,光谱图),则结果是光谱时态接收场(STRF)。这描述了所讨论的神经单元(例如神经元)将如何响应光谱时空中的模式。它已成为理解低水平神经活动中的感觉处理的关键工具。使用这种方法,也有可能研究这种神经表示在不同实验条件下如何变化。在动物的奖赏和情境调节实验中均已显示出STRF可塑性。近年来,已提出类似的技术可能可用于模拟人类记录的神经信号的活动。由于我们通常无法从人类的单个单位活动中进行记录,因此这种方法依赖于神经活动的代理-特别是在皮质脑电图电极的高频活动(HFA)中。这提供了一个独特的机会,其原因有两个:首先,人类语言是研究大脑中层次结构特征表示的一种自然刺激。有很多方法可以将语音分解为听觉和语言成分,每种方法都可以用作上述建模技术的输入。其次,人类尤其擅长使用高级上下文,例如他们对世界的经验和假设,以改变其行为。这为研究大脑中语音表达的可塑性提供了独特的机会。本论文报道了几种研究人脑中语音的感觉表达以及这些表达如何由于经验而改变的新方法。它旨在将啮齿动物和鸣鸟中的文学与人类电生理学的思想联系起来,以寻求研究人类感知的新方法。

著录项

  • 作者

    Holdgraf, Christopher R.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Neurosciences.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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