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个动机和目标 为什么离子通道位于亚细胞树突室中，并且是否存在紧密的耦合 观察到的神经元功能的定位？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 如果有大量的电生理学和解剖学数据，并且已与 关键神经元的功能作用。选择的神经元，CA1海马锥体细胞和小叶 蚱hoppers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 在位置场和碰撞检测的出现中。此外，涉及的树突状 离子通道密切相关，可以使用隔室的通用语言一起研究 建模和Hodgkin-Huxley格式化[97，56]。互补性将允许更广泛 结论比孤立研究任何一个系统。 1.1通道定位和单个神经元计算 大量证据表明，离子通道精确定位在单个神经元中。但是角色 这种用于神经元信息处理的本地化基本上仍然是出乎意料的。最著名的 区域通道定位的示例是轴突初始段，其中Na+和K+的高密度 短距离上的通道在动作电位的产生中起关键作用[64]。在树突中， 各种电导都位于特殊的树突状子区域，密度通常取决于 在距Spike Initiative区（SIZ）的距离上[77，72]。 通道定位已在特定的神经元类型中研究，例如嘻哈的锥体细胞 啮齿动物中的Pocampus和NeoCortex通过沿主要顶端DEN-的体外贴片钳记录 司机。这些记录显示了有助于中继突触输入的Na+通道的存在 索马并帮助行动潜力在树突中倒流[99，66]。此外，CA2+通道'热 Spots的帮助触发树突状尖峰有利于层中局部突触输入的非线性扩增 5（L5）新皮层锥体细胞[63，73]。在几种类型的神经元中，HCN通道增加 密度远离SIZ的偏爱，整个树突树一致的突触总和[71]。更远， 灭活K+通道密度的同时增加有助于调解HCN chan-的作用 海马锥体细胞突触整合过程中的nels [21]。这些结果已得到确认 通过模拟，但他们的信息处理的重要性仍然难以捉摸。 也已经使用免疫染色的树突中的通道的空间分布， 一种揭示离子渠道本地化的方法，但这并不总是在定量协议中 使用电生理方法[67，70，40]。在果蝇中，新方法可视化 基于一般编码的荧光标记的Na+通道分布[90]，但是离子的功能 单个神经元中信息处理的通道才刚刚开始研究。 在上面突出显示的几个示例中，我们对受约束的离子通道分布如何了解 是。该问题已在Crabs的大口径系统（STG）中进行了研究，其中一个小型网络 识别的神经元产生的节奏膜电位振荡涉及的各个阶段 消化。在STG神经元中，已经观察到离子通道表达水平的实质性变异性 [68，95，22]。模拟证实了神经元电导的大冗余 解释STG节奏行为的水平[89]。这些模拟使用的点模型神经元缺乏 树突不足，因此没有解决树突状离子渠道定位的特定城市。那几乎没有 目前在亚细胞离子通道定位对单个神经元计算的贡献方面已知。 1.2 CA1锥体细胞和放置场 最近据报道，行为时间尺度突触可塑性（BTSP）是基础的。 在环境的空间探索过程中，啮齿动物的Ca1锥体细胞中的领域[16]。这种可塑性 33