Neuronal circuits supporting learning-driven changes in auditory perception

听觉知觉中学习驱动变化的神经元机制 学习辨别噪音中的声音是听觉处理的基础，对于 日常通信和导航。听觉丘脑和听觉皮层都被显示为 参与检测噪音中的声音，但皮质内、皮质-丘脑和皮质内的微电路如何 丘脑相互作用促进噪声中信号检测仍然未知。我们的目标是缩小这一知识差距 并确定是否以及如何多个微电路内的皮质丘脑回路，包括兴奋抑制 初级听觉皮层内的回路;听觉皮层与丘系和非丘系之间的反馈回路。 丘系听觉丘脑;和皮层反馈通过抑制丘脑网状，有助于学习- 在噪音中的听觉感知的改善。我们将训练老鼠探测或区分听觉目标 在噪音中的作用。在噪声中检测和辨别声音会激活多个过程，包括 对背景噪声的选择性适应和目标表示的学习放大。我们将改变噪音 在目标呈现之前进行对比，以测试对比度自适应如何与目标检测相互作用。我们将记录 在行为过程中皮层和皮层下区域的神经元反应，并使用时间精确，细胞特异性 操作来建立允许鼠标学习和执行这个复杂任务的电路。我们将合理安排 一种新的广义线性-非线性模型，用于连续估计神经元响应中的增益适应，以测试是否 以及不同的微电路如何适应对比度，检测目标和控制神经元增益。综合起来，我们的结果 将揭示新的电路水平的机制，在噪声中的听觉在皮质丘脑环路内的微电路。

NEURONAL CIRCUITS SUPPORTING LEARNING-DRIVEN CHANGES IN AUDITORY PERCEPTION. Learning to discriminate sounds in noise is fundamental to auditory processing and is critically important for everyday communication and navigation. Both the auditory thalamus and the auditory cortex have been shown as involved in detection of sounds in noise, yet how the microcircuits within intra-cortical, cortico-thalamic and intra- thalamic interactions facilitate detection of signal in noise remains unknown. Our goal is to close this gap in knowledge and determine whether and how multiple microcircuits within the cortico-thalamic loop, including excitatory-inhibitory circuits within the primary auditory cortex; feedback loop between the auditory cortex and the lemniscal and non- lemniscal auditory thalamus; and the cortical feedback via the inhibitory thalamic reticular, contribute to the learning- driven improvement in auditory perception in noise. We will train mice to detect or discriminate between auditory targets in noise using operant conditioning. Detecting and discriminating sounds in noise activates multiple processes, including selective adaptation to background noise and learned amplification of the target representation. We will change noise contrast prior to target presentation to test how contrast adaptation interacts with target detection. We will record the neuronal responses in the cortical and sub-cortical regions during behavior, and use temporally precise, cell specific manipulations to establish the circuit that allows the mouse to learn and carry out this complex task. We will implement a novel generalized linear-non-linear model to continuously estimate gain adaptation in neuronal responses to test whether and how the distinct microcircuits adapt to contrast, detect the target and control neuronal gain. Combined, our results will reveal novel circuit-level mechanisms for hearing in noise across micro-circuits within the cortico-thalamic loop.