CRCNS: Understanding Single-Neuron Computation Using Nonlinear Model Optimization

CRCNS：使用非线性模型优化理解单神经元计算

• 批准号: 10612187
• 负责人: FABRIZIO GABBIANI
• 金额: $ 33.57万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612187
• 关键词: Action Potentials; Address; Agreement; Anatomy; Apical; Axon; Behavior; Behavioral; Biological Models; Biophysical Process; Coupling; Crabs; Data; Dendrites; Detection; Digestion; Drosophila genus; Electrophysiology (science); Environment; Generations; Grasshoppers; Hippocampus (Brain); Hodgkin-Huxley model; Hot Spot; In Vitro; Ion Channel; Language; Membrane Potentials; Methods; Modeling; Motivation; Movement; Neocortex; Neurons; Non-linear Models; Periodicity; Phase; Play; Potassium Channel; Pyramidal Cells; Reporting; Rodent; Role; Spatial Distribution; Specificity; Synapses; Synaptic plasticity; System; Trees; Visualization; base; biophysical analysis; density; detector; information processing; neocortical; neuronal cell body; novel; patch clamp; place fields; simulation

Project Description 1 Motivation and Objectives Why are ion channels localized in subcellular dendritic compartments and is there a tight coupling of the observed localization with neuron function? We argue that this fundamental question [55, 44] can be addressed by studying the biophysical mechanisms of single neuron computation in two model systems where a large amount of electrophysiological and anatomical data is available and has been tied to the functional roles of key neurons. The neurons selected, CA1 hippocampal pyramidal cells and the lobula giant movement detector (LGMD) neuron of grasshoppers, are ideal because we know precisely their role in the emergence of place fields and collision detection, respectively. Furthermore, the involved dendritic ion channels are closely related and can be studied jointly using the common language of compartmental modeling and the Hodgkin-Huxley formalism [97, 56]. Complementarity will allow to draw broader conclusions than by studying either system in isolation. 1.1 Channel Localization and Single Neuron Computation Abundant evidence suggests that ion channels are precisely localized within single neurons. Yet the role of this localization for neuronal information processing remains largely unexplored. The best-known example of zonal channel localization is the axon initial segment, where high densities of Na+ and K+ channels over a short distance play a pivotal role in the generation of action potentials [64]. In dendrites, a variety of conductances are localized in specific dendritic subregions, with densities that often depend on the distance from the spike initiation zone (SIZ) [77, 72]. Channel localization has been studied in specific neuron types such as pyramidal cells of the hip- pocampus and neocortex in rodents through in vitro patch-clamp recordings along the main apical den- drite. These recordings show the presence of Na+ channels that help relay synaptic inputs towards the soma and help action potentials backpropagate in dendrites [99, 66]. Additionally, Ca2+ channel ‘hot spots’ help trigger dendritic spikes favoring non-linear amplification of localized synaptic inputs in layer 5 (L5) neocortical pyramidal cells [63, 73]. In several types of neurons, an increase in HCN channel density away from the SIZ favors consistent synaptic summation across the dendritic tree [71]. Further, a concomitant increase in the density of inactivating K+ channels helps fine tune the role of HCN chan- nels during synaptic integration in hippocampal pyramidal cells [21]. These results have been confirmed through simulations, but their significance for information processing remains elusive. The spatial distribution of channels within dendrites has also been investigated using immunostaining, a method that reveals the localization of ion channels but that is not always in quantitative agreement with electrophysiological methods [67, 70, 40]. In Drosophila, novel methods allows visualization of Na+ channel distributions based on genetically encoded fluorescent markers [90], but the function of ion channels for information processing in single neurons is only beginning to be studied. In the few examples highlighted above, we know little about how constrained ion channel distributions are. This issue has been investigated in the stomatogastric system (STG) of crabs, where a small network of identified neurons generates rhythmic membrane potential oscillations involved in various phases of digestion. In STG neurons, substantial variability in ion channel expression levels has been observed [68, 95, 22]. Simulations confirmed that there exists a large redundancy in neuronal peak conductance levels explaining the STG’s rhythmic behavior [89]. These simulations used point-model neurons lacking dendrites and thus did not address the specificity of dendritic ion channel localization. Thus, little is currently known on the contribution of subcellular ion channel localization to single neuron computation. 1.2 CA1 Pyramidal Cells and Place Fields Behavioral timescale synaptic plasticity (BTSP) was recently reported to underlie the formation of place fields in CA1 pyramidal cells of rodents during spatial exploration of an environment [16]. This plasticity 33

项目描述 1 动机和目标 为什么离子通道位于亚细胞树突区室中，并且是否存在紧密耦合 我们认为这个基本问题 [55, 44] 可以是 通过研究两个模型系统中单神经元计算的生物物理机制来解决 可以获得大量的电生理学和解剖学数据，并且与 所选神经元、CA1 海马锥体细胞和小叶的功能作用。 蚱蜢的巨型运动检测器（LGMD）神经元是理想的，因为我们确切地知道它们的作用 分别出现位置场和碰撞检测此外，还涉及到树突。 离子通道密切相关，可以使用区室的共同语言进行联合研究 建模和 Hodgkin-Huxley 形式主义 [97, 56] 的互补性将允许绘制更广泛的内容。 结论比单独研究任一系统更有效。 1.1 通道定位和单神经元计算 大量证据表明，离子通道精确地定位在单个神经元内，但其作用还存在。 神经信息处理的这种定位在很大程度上仍未被探索。 带状通道定位的例子是轴突初始段，其中高密度的 Na+ 和 K+ 短距离通道在动作电位的产生中发挥着关键作用[64]。 各种电导位于特定的树突子区域，其密度通常取决于 与尖峰起始区 (SIZ) 的距离有关 [77, 72]。 通道定位已在特定神经元类型（例如髋部锥体细胞）中进行了研究。 通过体外膜片钳记录啮齿类动物的马脑和新皮质 这些记录显示 Na+ 通道的存在，有助于将突触输入传递给神经元。 胞体并帮助动作电位在树突中反向传播 [99, 66] 此外，Ca2+ 通道“热”。 点有助于触发树突尖峰，有利于层中局部突触输入的非线性放大 5 (L5) 新皮质锥体细胞 [63, 73] 在几种类型的神经元中，HCN 通道增加。 远离 SIZ 的密度有利于树突树上一致的突触求和 [71]。 失活 K+ 通道密度的随之增加有助于微调 HCN 通道的作用 海马锥体细胞突触整合过程中的神经细胞[21]已得到证实。 通过模拟，但它们对信息处理的意义仍然难以捉摸。 还使用免疫染色研究了树突内通道的空间分布， 一种揭示离子通道定位但并不总是定量一致的方法 使用电生理学方法 [67,70,40] 在果蝇中，新方法可以实现可视化。 基于基因编码荧光标记的Na+通道分布[90]，但离子的功能 单个神经元信息处理通道的研究才刚刚开始。 在上面强调的几个例子中，我们对离子通道分布如何受到限制知之甚少 这个问题已经在螃蟹的口胃系统（STG）中进行了研究，其中有一个小型网络。 已识别的神经元产生节律性膜电位振荡，涉及不同阶段 在 STG 神经元中，观察到离子通道表达水平的显着变化。 [68,95,22] 模拟证实神经峰值电导存在大量冗余。 水平解释了 STG 的节律行为 [89]。 树突，因此没有解决树突离子通道定位的特异性。 目前已知亚细胞离子通道定位对单神经元计算的贡献。 1.2 CA1 锥体细胞和位置场 最近有报道称，行为时间尺度突触可塑性（BTSP）是位置形成的基础 在环境空间探索过程中啮齿类动物 CA1 锥体细胞中的磁场 [16]。 33