Evaluating how inhibition shapes granule cell population codes

摘要 将经验和运动联系起来几乎是所有生活的基础，根据过去的经验进行预测的能力也是如此 经验是，一种情况需要运动反应。这种联系通常依赖于小脑，小脑 利用由密集的颗粒层编码的各种感觉、运动和认知信息 细胞(GC)作为学习的底物。小脑学习的经典模型假设，突触抑制 来自称为高尔基细胞的局部中间神经元的GC对于建立一个不重叠的稀疏种群是必不可少的 密码。然而，最近的研究通过揭示单峰感官对这种模型提出了质疑。 刺激可以激活广泛的GC活性，这表明反应甚至可能是密集和多余的 在某些条件下用于简单的感官表征。这些观察结果令人质疑是否和 GC如何在种群水平编码离散的刺激特征以及突触抑制的程度 生成稀疏性和模式分离。测试GC如何在总体水平上编码刺激功能 以及这些表征是如何受到局部抑制的调节的，我建议测量刺激诱发的GC 在清醒小鼠体内使用视频速率钙成像结合GC特异性操作的活动 突触抑制。在目标1中，我将测试GC是否以及如何编码特定于要素的表示 通过测量单个和群体GC钙瞬变如何改变刺激强度和认同感 在许多随机交叉试验中，对不同刺激特征的反应，同时控制运动- 使用高速视频和振动传感器进行相关活动。确定何时以及如何使用刺激功能 由唯一或重叠的GC种群表示，将允许我们测试这些活动模式是如何 由局部抑制形成的。在目标2中，我将测试GC如何整合多种感觉输入。我会想象GC 对不同模式的组合感觉刺激的反应(即听觉和躯体感觉)或 相同的模式(即两个不同频率的音调)，并测量单个和群体GC钙 瞬变会随着额外的同时刺激而改变。根据初步数据，我预计， 虽然融合输入可以相加来推动GC超过峰值阈值，但GC也将表现出减法 作为加法，信号整合由于刺激特异性的募集而抑制。最后，在目标3中，我将测试如何 高尔基体细胞的抑制塑造了目标1中确定的群体反应，并使多感官 使用一种新的酶通过急性阻断GCs的GABA能输入来评估AIM 2中GCs的整合 捕获系统。这些实验将为GC如何在 个体和群体水平，并揭示这种编码是如何由局部突触抑制调节的。因此， 这项研究将通过建立GC层如何代表小脑功能来测试长期存在的模型 以一种能够进行联想学习的方式处理感觉信息的模式。

ABSTRACT Linking experience and movement is fundamental to nearly all life, as is the ability to predict, based on previous experience, that a condition warrants a motor response. Such associations often rely on the cerebellum, which harnesses diverse sensory, motor, and cognitive information encoded by a densely-packed layer of granule cells (GC) as a substrate for learning. Classical models of cerebellar learning posit that synaptic inhibition of GCs from local interneurons called Golgi cells is essential for establishing a non-overlapping, sparse population code. However, recent studies have called such models into question by revealing that unimodal sensory stimulation can activate widespread GC activity, suggesting that responses can be dense and redundant even for simple sensory representations under some conditions. These observations call into question whether and how GCs encode discrete stimulus features at the population level and the degree to which synaptic inhibition generates sparsity and pattern separation. To test how GCs encode stimulus features at the population level and how these representations are regulated by local inhibition, I propose to measure stimulus evoked GC activity using video-rate in vivo calcium imaging in awake mice in conjunction with a GC-specific manipulation of synaptic inhibition. In Aim 1, I will test whether and how GCs encode feature-specific representations of stimulus intensity and identity by measuring how single and population GC calcium transients change in response to varied stimulus features over many randomly interleaved trials, while controlling for movement- related activity with high-speed video and vibration sensors. Establishing when and how stimulus features are represented by unique or overlapping GC populations will allow us to test how these activity patterns are shaped by local inhibition. In Aim 2, I will test how GCs integrate multiple sensory inputs. I will image GC responses to combined sensory stimuli of either different modalities (i.e. auditory and somatosensory) or the same modality (i.e. two tones with differing frequencies) and measure how single and population GC calcium transients change in response to additional, simultaneous stimuli. Based on preliminary data, I anticipate that, while converging inputs can be summed to drive GCs past spike threshold, GCs will exhibit subtractive, as well as additive, signal integration due to stimulus-specific recruitment of inhibition. Finally in Aim 3, I will test how inhibition from Golgi cells shapes the population responses identified in Aim 1 and enables the multisensory integration by GCs assessed in Aim 2 by acutely blocking GABAergic inputs to GCs using a novel enzymatic capture system. These experiments will provide novel insight into how GCs encode information at the individual and population level and reveal how this encoding is regulated by local synaptic inhibition. Hence, this study will test longstanding models of cerebellar function by establishing how the GC layer represents patterns of sensory information in a manner that enables associative learning.