Sound encoding by neural populations in auditory cortex during behavior

项目摘要 在整个生命过程中，人类和其他动物调整他们的听力，以感知声音的重要特征 做出成功的行为决定。听力正常的人类能够检测和辨别重要的 在拥挤嘈杂的场景中发出声音，并在个人第一次见面时理解他们的讲话。 然而，患有外周听力损失或中央处理障碍的患者通常在听力方面存在问题 这些具有挑战性的环境。即使当他们能够准确地感知声音时，额外的听力努力 Required会对其他认知功能产生负面影响。更好地理解健康的听觉 在认知挑战的背景下运行的系统将支持针对这些缺陷的新疗法。 这个项目将研究听觉系统如何呈现声音信息，当它在挑战中运行时 声学环境。有三个具体目标。首先，将使用高密度微电极阵列来 记录在需要检测的行为过程中，听觉皮质中神经元群的同时活动 声音被噪音掩盖或学习新的声音奖赏联系。来自多个神经元的录音将 能够表征信息是如何通过神经群体的同时活动来编码的。这些 实验将检验这一假设，即听觉皮质中的群体活动产生的表征 对不相关的分散注意力的声音是不变的。其次，将使用光遗传学工具来识别不同的神经元 皮质中的细胞类型(兴奋性与抑制性)。这项研究将检验这样一种假设，即紧张性激活 抑制性神经元可以解释行为过程中群体活动的变化。第三，机器学习工具将 用于模拟同时记录的神经活动。这些实验将检验这一假设 在听觉皮质的同一局部解剖回路中的神经元编码的信息相对较小 在所有可能的听觉刺激空间中的域。符合实验数据的模型也将描述如何 行为状态的改变改变了神经元编码声音和描述相关信息来源的方式 在行为过程中影响神经辨别能力的群体活动。这些实验将共同确立 声音的神经表征与提取重要信息的认知过程之间的新联系 来自声音的信息有助于成功的行为。

Project Summary Throughout life, humans and other animals adapt their hearing to perceive features of sound that are important for successful behavioral decisions. Normal-hearing humans are able to detect and discriminate important sounds in crowded noisy scenes and to understand the speech of individuals the first time they meet. However, patients with peripheral hearing loss or central processing disorders often have problems hearing in these challenging settings. Even when they can perceive sounds accurately, the additional listening effort required negatively impacts other cognitive functions. A better understanding of how the healthy auditory system operates in cognitively challenging contexts will support new treatments for these deficits. This project will study how the auditory system represents sound information as it operates in challenging acoustic environments. There are three specific aims. First, high-density microelectrode arrays will be used to record the simultaneous activity of neural populations in auditory cortex during behaviors that require detecting sounds masked by noise or learning new sound-reward associations. Recording from multiple neurons will enable characterizing how information is encoded by the simultaneous activity of neural populations. These experiments will test the hypothesis that population activity in auditory cortex generates representations that are invariant to irrelevant distracting sounds. Second, optogenetic tools will be used to identify distinct neuronal cell types (excitatory versus inhibitory) in cortex. This study will test the hypothesis that tonic activation of inhibitory neurons can explain changes in population activity during behavior. Third, machine learning tools will be used to model the simultaneously recorded neural activity. These experiments will test the hypothesis that neurons in the same local anatomical circuit in auditory cortex encode information about a relatively small domain in the space of all possible auditory stimuli. Models fit to experimental data will also describe how changes in behavioral state shift the way neurons encode sounds and describe sources of correlated population activity that impact neural discriminability during behavior. Together these experiments will establish new links between neural representation of sound and the cognitive processes that extract important information from sound for successful behavior.