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?

项目摘要/摘要 满足组织的身体功能的解释，这有助于指导医疗干预，框架 该功能是根据组件单元的输入和这些单元如何转化其转换的算法的功能 输入输出。到目前为止，大脑功能已经避开了这种机械解释，部分原因是1） 组件细胞 - 神经元 - 每个组合最多可结合数千个突触输入以产生其输出，并且 因为2）很难确定任何给定神经元如何促进整个大脑的功能。 结果，我们对哺乳动物脑电路没有上述术语的解释，我们也无法 测量哺乳动物大脑中即使是单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的医疗干预措施，特别是必须 直接与神经元 - 所谓的脑机界面（BMI）接口。 我将通过使用敏感的新遗传编码神经递质指标来应对第一个挑战 （getis）和一个新型的高带宽在体内显微镜中，简单地记录了数千个活性 行为小鼠皮质中各个神经元内的突触输入和输出。我将基于我最近的 通过开发一对副兼容的Getis来为谷氨酸开发高敏性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），一种体内显微镜，将准确有效地记录数千个 在3D> 100 Hz中突触。这些工具共同使直接看到 对神经元的无数神经递质输入的精确时机和位置，观察这些输入如何排列 开车射击，并实时观看，随着输入随着学习的变化而变化。克服第二个挑战和 启用对行为有已知贡献的神经元的可靠访问，我将采用快速训练的BMI- 基于学习任务，鼠标学会在特定情况下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如何 操作随着学习而改变。此外，我将调整BMI任务以训练神经元以执行 专家选择的输入输出操作，从而研究哪些类型的输入输出操作 个别神经元可以学习。 这些技术合并将建立一个新的实验范式，几乎有限 研究神经功能和学习的可能性。我将使用这些工具来询问：1）在行为上如何 相关输入输出操作 - 神经算法的各个步骤 - 在皮层内实现？ 2） 皮质神经元如何学习执行特定的输入输出操作？ 3）哪些操作可以个体 皮质神经元学会表现？ 4）我们可以使用所产生的知识来开发更有效的BMI？