Steadman Mark A, Sumner Christian J
MRC Institute of Hearing Research, School of Medicine, The University of Nottingham, Nottingham, United Kingdom.
Department of Bioengineering, Imperial College London, London, United Kingdom.
Front Neurosci. 2018 Oct 12;12:671. doi: 10.3389/fnins.2018.00671. eCollection 2018.
A fundamental task of the ascending auditory system is to produce representations that facilitate the recognition of complex sounds. This is particularly challenging in the context of acoustic variability, such as that between different talkers producing the same phoneme. These representations are transformed as information is propagated throughout the ascending auditory system from the inner ear to the auditory cortex (AI). Investigating these transformations and their role in speech recognition is key to understanding hearing impairment and the development of future clinical interventions. Here, we obtained neural responses to an extensive set of natural vowel-consonant-vowel phoneme sequences, each produced by multiple talkers, in three stages of the auditory processing pathway. Auditory nerve (AN) representations were simulated using a model of the peripheral auditory system and extracellular neuronal activity was recorded in the inferior colliculus (IC) and primary auditory cortex (AI) of anaesthetized guinea pigs. A classifier was developed to examine the efficacy of these representations for recognizing the speech sounds. Individual neurons convey progressively less information from AN to AI. Nonetheless, at the population level, representations are sufficiently rich to facilitate recognition of consonants with a high degree of accuracy at all stages indicating a progression from a dense, redundant representation to a sparse, distributed one. We examined the timescale of the neural code for consonant recognition and found that optimal timescales increase throughout the ascending auditory system from a few milliseconds in the periphery to several tens of milliseconds in the cortex. Despite these longer timescales, we found little evidence to suggest that representations up to the level of AI become increasingly invariant to across-talker differences. Instead, our results support the idea that the role of the subcortical auditory system is one of dimensionality expansion, which could provide a basis for flexible classification of arbitrary speech sounds.
听觉上行系统的一项基本任务是生成有助于识别复杂声音的表征。在声学变异性的背景下，这一任务极具挑战性，例如不同说话者发出相同音素时的声学差异。随着信息从内耳到听觉皮层（AI）在整个听觉上行系统中传播，这些表征会发生转变。研究这些转变及其在语音识别中的作用是理解听力障碍和未来临床干预发展的关键。在此，我们在听觉处理通路的三个阶段获取了对大量自然元音 - 辅音 - 元音音素序列的神经反应，每个序列由多个说话者发出。使用外周听觉系统模型模拟听觉神经（AN）表征，并在麻醉的豚鼠的下丘（IC）和初级听觉皮层（AI）中记录细胞外神经元活动。开发了一个分类器来检验这些表征识别语音的功效。从AN到AI，单个神经元传递的信息逐渐减少。尽管如此，在群体水平上，表征足够丰富，能够在所有阶段以高度准确性促进辅音识别，这表明从密集、冗余的表征向稀疏、分布式的表征发展。我们研究了辅音识别神经编码的时间尺度，发现最佳时间尺度在整个听觉上行系统中增加，从外周的几毫秒到皮层的几十毫秒。尽管有这些更长的时间尺度，但我们几乎没有发现证据表明直至AI水平的表征对不同说话者之间的差异变得越来越不变。相反，我们的结果支持这样一种观点，即皮层下听觉系统的作用之一是维度扩展，这可以为灵活分类任意语音提供基础。
