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

通过大规模神经递质成像测量皮质神经元的输入输出操作

• 批准号: 10687664
• 负责人: Kaspar Podgorski
• 金额: $ 138.5万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687664
• 关键词: 3-Dimensional; Address; Adopted; Algorithms; Behavior; Behavioral; Brain; Cellular Structures; Development; Devices; Generations; Glutamates; Human; Image; Individual; Intervention; Learning; Location; Measures; Medical; Microscope; Mus; Neurons; Neurotransmitters; Output; Patients; Physiological; Scanning; Sensory; Speed; Synapses; Technology; Therapeutic; Time; Tissues; Training; Work; brain machine interface; cell transformation; design; gamma-Aminobutyric Acid; in vivo; knowledge of results; motor impairment; neural; novel; operation; tool

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?

项目概要/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 该功能根据组件单元的输入以及这些单元如何转换它们的算法来发挥作用 迄今为止，大脑功能还没有得到这样的机械解释，部分原因是：1） 组成细胞——神经元——每个细胞结合多达数千个突触输入来生成输出，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，也无法解释 测量哺乳动物大脑中单个神经元的输入输出操作。 挑战将有助于设计大脑的医疗干预措施，特别是必须设计的治疗设备 直接与神经元连接——所谓的脑机接口（BMI）。 我将通过使用敏感的新基因编码神经递质指标来解决第一个挑战 （GETIs）和新型高带宽体内显微镜可同时记录数千个细胞的活动 我将在我最近的研究基础上研究行为小鼠皮层中单个神经元的突触输入和输出。 通过开发一对光谱兼容的 GETI 来开发谷氨酸的高灵敏度 GETI 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，可准确有效地记录数千个样本 这些工具一起可以以 >100 Hz 的速度直接观察 3D 突触。 无数神经递质输入到神经元的精确时间和位置，观察这些输入如何排列 驱动射击，并实时观察输入随学习的变化，以克服第二个挑战并 为了能够可靠地访问对行为有已知贡献的神经元，我将采用快速训练的 BMI- 基于学习任务，小鼠学习在特定环境中激活单个目标皮层神经元。 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 此外，我将调整 BMI 任务来训练神经元执行任务。 实验者选择的输入输出操作，从而研究什么类型的输入输出操作 单个神经元可以学习。 这些技术结合起来将建立一个新的实验范式，几乎无限 研究神经计算和学习的可能性我将使用这些工具来问：1）行为如何- 相关的输入输出操作 - 神经算法的各个步骤 - 在皮层内实现？ 2) 皮层神经元如何学习执行特定的输入输出操作 3）可以进行哪些操作？ 皮层神经元学习如何执行？4）我们可以利用由此产生的知识来开发更有效的 BMI 吗？