Circuit Mechanisms of Sound Processing and Detection in the Auditory Pathway

听觉通路中的声音加工和检测机制 听觉感知依赖于输入信号的预测统计，无论是识别一个人的语音， 在拥挤的房间里与人交谈，或者在森林里辨认出潺潺的小溪的声音。人脑 检测统计学的声音作为预测的一个基本方面，证明了减少反应， 重复的声音模式和对意外声音的增强反应。多项研究表明， 通过自适应减少对规则信号的响应，这有助于预测。然而，适应 目前，单靠这一点还不足以解释动物细胞和神经元群体水平的预测和研究 为现有的预测编码理论提供了部分支持。因此，用于所述电路的电路级机制可以是： 对声音中除音调频率之外的统计量的预测，以及它们在行为中的作用，仍然是未知的。我们 我们的目标是缩小这一知识差距，确定预测信号和检测统计规律的电路， 听觉行为中的违规行为为了确定统计预测的前馈和反馈分量， 在听觉系统的声音中，我们将联合收割机光遗传学选择性扰动和大规模成像相结合， 电生理学与行为方法在清醒小鼠。首先，我们测试是否以及如何兴奋抑制 听觉皮层（AC）内的相互作用为声音模式建立预测代码，检测统计学上的错误， 有助于增强对意外声音的反应。其次，我们测试是否以及如何检测统计 在神经元水平上的行为学有助于对声音规律性变化的行为检测。第三，我们测试 以及来自高级皮层区域的反馈如何提供关于规律性和违规的信息。我们的结果将确定 编码统计规律性及其在声音中的破坏的神经回路，并建立它们在听觉中的作用。 行为

CIRCUIT MECHANISMS OF SOUND PROCESSING AND DETECTION IN THE AUDITORY PATHWAY Auditory perception relies on predicting statistics of incoming signals, be it identifying the speech of a conversation partner in a crowded room or recognizing the sound of a bubbling brook in a forest. The human brain detects statistical regularities in sounds as a fundamental aspect of prediction, evidenced by reduced responses to repeated sound patterns and enhanced responses to unexpected sounds. Multiple studies demonstrate that the neuronal responses to regular signals are reduced through adaptation, which can contribute to prediction. However, adaptation alone is not sufficient to account for prediction and studies at cellular and neuronal population level in animals thus far lend onto partial support to existing theories of predictive coding. As such, the circuit level mechanisms for the prediction of statistical regularities beyond tone frequency in sounds, and their role in behavior, remain unknown. Our goal is to close this gap in knowledge and to determine the circuits that predict signals and detect statistical regularity and its violation in auditory behavior. To identify feedforward and feedback components of prediction of statistical regularities in sounds in the auditory system, we combine optogenetic selective perturbation and large-scale imaging and electrophysiology with behavioral methods in awake mice. First, we test whether and how excitatory-inhibitory interactions within the auditory cortex (AC) establish predictive code for sound patterns, detect statistical regularities, and contribute to enhanced responses for unexpected sounds. Second, we test whether and how detection of statistical regularities at the neuronal level contributes to behavioral detection of change in sound regularity. Third, we test whether and how feedback from higher cortical areas provides information about regularity and violation. Our results will identify the neuronal circuits for encoding statistical regularity and its violation in sound and establish their role in auditory behavior.