Measuring input-output operations of cortical neurons with large-scale neurotransmitter imaging

项目摘要/摘要 对组织生理功能的令人满意的解释，有助于指导医疗干预，框架 它的功能取决于组成细胞的输入，以及这些细胞如何转换其 投入转化为产出。到目前为止，大脑功能一直未能得到这样的机械解释，部分原因是1) 组成细胞-神经元-每个结合多达数千个突触输入来产生它们的输出，并且 因为2)很难确定任何给定的神经元如何对整个大脑的功能做出贡献。 因此，我们无法用上述术语来解释哺乳动物的大脑回路，我们也不能 测量哺乳动物大脑中哪怕一个神经元的输入输出操作。解决上述问题 挑战将有助于设计大脑中的医疗干预措施，特别是必须 直接与神经元连接--即所谓的脑机接口(BMI)。 我将通过使用敏感的新的遗传编码的神经递质指示剂来解决第一个挑战 (GETIS)和一种新型的高带宽活体显微镜，可以同时记录数千个 行为小鼠大脑皮层单个神经元内的突触输入和输出。我将在我最近的基础上 通过开发一对光谱相容的GETI来开发谷氨酸的高灵敏度GETI 谷氨酸和GABA。我将完成第二代扫描线投影的开发 显微镜(SLAP2)，一种活体显微镜，将准确和高效地记录来自数千个 突触在100赫兹的3D图像中。这些工具结合在一起，将使人们能够高速地直接看到 无数神经递质输入到神经元的精确时间和位置，观察这些输入是如何排列的 推动激发，并实时观察输入随着学习而变化。克服第二个挑战， 能够可靠地接触到具有已知行为贡献的神经元，我将采用快速训练的BMI- 基于学习的任务，在该任务中，小鼠学习在特定环境中激活单个目标皮质神经元。我 将使用高带宽Geti成像来研究目标神经元的突触输入和输入输出 行动会随着学习而改变。此外，我将调整BMI任务，以取代训练神经元执行 实验者选择的投入产出操作，从而考察哪些类型的投入产出操作 单个神经元可以学习。 这些技术结合在一起将建立一个几乎无限的新的实验范式 研究神经计算和学习的可能性。我将使用这些工具来问：1)行为是如何- 相关的输入-输出操作--神经算法的各个步骤--在大脑皮层内实现？2) 大脑皮层神经元如何学习执行特定的输入输出操作？3)个人可以进行哪些操作 大脑皮层神经元是如何学习的？以及4)我们能否利用由此获得的知识来开发更有效的BMI？

Project Summary/Abstract Satisfying explanations of the physiological function of a tissue, which help guide medical interventions, frame that function in terms of the inputs of component cells and an algorithm for how those cells transform their inputs into outputs. Brain functions have so far eluded such mechanistic explanation, in part because 1) the component cells – neurons – each combine up to thousands of synaptic inputs to generate their output, and because 2) it is difficult to determine how any given neuron contributes to the function of the brain as a whole. As a result, we do not have explanations in the above terms for mammalian brain circuits, nor are we able to measure the input-output operations of even a single neuron in the mammalian brain. Addressing the above challenges will aid design of medical interventions in the brain, especially of therapeutic devices that must directly interface with neurons – so-called brain-machine interfaces (BMIs). I will address the first challenge by using sensitive new genetically encoded neurotransmitter indicators (GETIs) and a novel high-bandwidth in vivo microscope to simultaneously record the activity of thousands of synaptic inputs and outputs within individual neurons in the cortex of behaving mice. I will build on my recent work developing a high-sensitivity GETI for glutamate by developing a spectrally-compatible pair of GETIs for glutamate and GABA. I will complete the development of the 2nd generation Scanned Line Projection Microscope (SLAP2), an in vivo microscope that will accurately and efficiently record from thousands of synapses in 3D at >100 Hz. Together these tools will make it possible to directly see, at high speed, the precise timing and location of myriad neurotransmitter inputs to a neuron, observe how those inputs line up to drive firing, and watch in real-time as inputs change with learning. To overcome the second challenge and enable reliable access to neurons with a known contribution to a behavior, I will adopt a rapidly-trained BMI- based learning task in which a mouse learns to activate a single target cortical neuron in a specific context. I will use high-bandwidth GETI imaging to study how the target neuron’s synaptic inputs and input-output operations change with learning. Moreover, I will adapt the BMI task to instead train neurons to perform an experimenter-selected input-output operation, to thereby investigate what types of input-output operations individual neurons can learn. These technologies combined will establish a new experimental paradigm with nearly limitless possibilities for studying neural computation and learning. I will use these tools to ask: 1) How are behaviorally- relevant input-output operations - the individual steps of neural algorithms - implemented within the cortex? 2) How do cortical neurons learn to perform a specific input-output operation? 3) What operations can individual cortical neurons learn to perform? and 4) Can we use the resulting knowledge to develop more effective BMIs?