ANALYZING CONTRIBUTIONS OF MORPHOLOGIC AND MEMBRANE MODEL PARAMETERS TO NEURAL

分析形态和膜模型参数对神经元的贡献

• 批准号: 7956210
• 负责人: Charles L Weaver
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956210
• 关键词: Accounting; Action Potentials; Adult; Affect; Aging; Algorithms; Animals; Area; Behavior; Biomedical Research; Books; Brain; Brain Stem; Calcium; Caliber; Cell Nucleus; Code; Cognitive; Communication; Complex; Computational Technique; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Computers; Computing Methodologies; Data; Dendrites; Dendritic Spines; Development; Disease; District of Columbia; Ensure; Equilibrium; Event; Exhibits; Eye; Faculty; Flare; Funding; Future; Generations; Goldfish; Grant; Growth; Hand; Head; High Performance Computing; Image; Imaging Techniques; In Vitro; Individual; Institution; Ion Channel; Laboratories; Laboratory Study; Laser Scanning Microscopy; Learning; Link; Lobe; Macaca; Measurement; Measures; Membrane; Methods; Modeling; Monkeys; Morphology; Motor; Nature; Neurons; Neurosciences; Occupations; Output; Pathway interactions; Pattern; Performance; Physiological; Physiology; Positioning Attribute; Potassium; Prefrontal Cortex; Prevalence; Property; Protocols documentation; Pyramidal Cells; Relative (related person); Research; Research Personnel; Resources; Role; Running; Sampling; Shapes; Short-Term Memory; Signal Transduction; Simulate; Slave; Slice; Societies; Sodium; Source; Structure; Supercomputing; System; Task Performances; Techniques; Testing; Three-dimensional analysis; Time; Trauma Research; United States National Institutes of Health; Universities; Work; abstracting; age related; aged; base; computational neuroscience; computing resources; design; eye velocity; fitness; hindbrain; improved; in vivo; interest; medical schools; meetings; member; membrane model; mental representation; morphometry; neocortical; neuronal excitability; neuronal patterning; novel; oculomotor; professor; programs; relating to nervous system; response; shape analysis; shared memory; simulation; supercomputer

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Neurons use electrical signals to communicate with each other. The time-varying pattern of these neuronal signals (firing dynamics) is critically important to information coding. Neuronal firing dynamics are largely determined by the excitability of the neuronal membrane, driven by active ion channels, and the shape (morphology) of its dendrites [1]. The relative importance and interactions between active and morphologic properties remain poorly understood, particularly since these properties, and even the firing dynamics they determine, may vary during development, learning, aging and disease [2-4]. In computational models, active and morphologic features interact nonlinearly, represented by many parameters with different magnitudes and even different units of measurement. I am a member of the research faculty at Mount Sinai School of Medicine, working in the laboratory of Prof Susan Wearne, Associate Professor of Neuroscience. Our research program is twofold: to design computational techniques to investigate scientific questions methodically [5-10], and to use these techniques to study how the organization of intrinsic neuronal properties influences function [10-12]. This particular project is supported by an NIH grant (DC05669) to Prof Wearne. Working memory, which maintains a brief mental representation of a recent event necessary for future task performance, underlies many of the brains complex functions. The Wearne laboratory studies properties of networks and individual neurons that participate in working memory tasks [6,7,10-13]. For example, persistent neural activity, lasting long after an input has ended, is a hallmark of working memory observed throughout the brain [14]. In the vestibular (balance) system, persistent activity is needed to maintain precisely tuned eye velocity in response to a given angular head velocity. In the goldfish, neurons from the hindbrain nucleus Area II exhibit, and likely contribute to, this eye velocity storage [15-17]. Area II neurons display many interesting features in vivo and in vitro [13,17]. Recent models have hypothesized how morphology might contribute to persistent activity [18-20], but none have investigated the contributions of realistic 3D morphology. As morphology varies widely in Area II neurons [16], we have worked to evaluate how morphology influences the precisely tuned firing patterns that underlie persistent activity in Area II. Using the computational methods described below, we have demonstrated that morphology can contribute significantly to neuronal firing properties [10-12,21,22]. In Area II dendrites the tapering, flare and diameter contribute significantly to a range of firing dynamics, as do the persistent sodium, calcium-dependent potassium, and A-type potassium membrane conductances [10,21,22]. We are now extending these results based on simple morphologic models to our realistic morphologic data and physiology collected in vivo and in vitro. The computational resources needed to conduct this research have increased dramatically in our move to more realistic models. To meet this increased computing demand, I would like to obtain a Development Allocation account on one of the supercomputers available through the TeraGrid. Computational Methods Evaluating the contributions of morphology and active ion channels to biologically relevant behaviors requires that all parameters (on the order of 10-100 depending on the system) be assigned values consistent with experimental data. Optimization eases this task by automatically searching a multidimensional parameter space to identify parameter sets that minimize a fitness function representing salient differences between simulated and experimental (target) data. While the results are less subjective and identified more quickly than with hand tuning, automated parameter optimization often requires weeks of computing time to identify suitable parameter sets. My research requires the fitting of model parameters to the complex morphology of three different Area II neurons, as well as several reduced models. To conduct highly efficient searches, I have already designed a fitness function that matches the shape of neuronal firing patterns observed in many neurons [9,10]. At the same time, I have also implemented simulated annealing with recentering, a constrained parameter search algorithm ensuring that identified parameters are biologically plausible [9,23]. After fitting our models to the morphologic and physiologic data of Area II neurons, we will explore how the different model parameters affect model output. To do this, I will use a novel application of sensitivity analysis that Prof Wearne and I have developed [10]. Importantly, our method predicts which parameters to change, and by how much, to compensate for changes in another parameter to restore normal function. These predictions go beyond our Area II models, and may prove important to neuronal aging, disease and trauma research. Exploring these complex models requires significant computation time. To evaluate the sensitivity of model output to each of its many parameters, each parameter must be perturbed independently while others are held constant and a new simulation performed. Needing to explore the sensitivity of several model outputs to 10-20 model parameters, at thousands of local points throughout the parameter space, this study requires considerable amounts of computation time. Computing Requirements This research is already underway on a local resource, a 160-processor G5 Xserve cluster running Mac OS X. For timely completion of this study, I need access to multiple processors for extended periods of time, but will not require access to shared memory machines. So far 500 GB RAM has been sufficient for these simulations; this is not expected to change significantly. My disk space requirement should not exceed 1/3 TB. Access to tape storage is not necessary. I ask for a total of 30,000 SUs, to conduct parameter optimizations and sensitivity analyses of models for each of the three Area II morphology obtained experimentally and for analysis of related reduced models. I will need no more than 120 processors at any one time, and will often use less. No communication is needed between the different processors. The fitness function and simulated annealing algorithm have both been implemented in NEURON [24,25], one of the standard modeling packages within Computational Neuroscience. At present, the simulated annealing code runs several serial jobs simultaneously. In the future, we also look to take advantage of Parallel NEURONs Bulletin Board capacity of running multiple jobs on slave processors, controlled by a single master node, for improved searches of the parameter space. An account on nearly any of the TeraGrid resources would fill these needs. Any of the supercomputing clusters should work well for my needs. NEURON is already running on resources at the San Diego and Pittsburgh Computing Centers (DataStar at SDSC, and BigBen at PSC). If sufficient computing time were available on one of those machines, that might be simplest. Otherwise Michael Hines, one of NEURONs creators, has already offered his assistance in compiling NEURON on any machine to which I am granted access. References 1. Mainen ZF, Sejnowski TJ (1996) Influence of dendritic structure on firing patterns in model neocortical neurons. Nature 382: 363-366. 2. Duan H, Wearne SL, Rocher AB, Macedo A, Morrison JH, et al. (2003) Age-related dendritic and spine changes in corticocortically projecting neurons in macaque monkeys. Cereb Cortex 13: 950-961. 3. Bucher D, Prinz AA, Marder E (2005) Animal-to-animal variability in motor pattern prediction in adults and during growth. J Neurosci 25: 1611-1619. 4. Chang YM, Rosene DL, Killiany RJ, Mangiamele LA, Luebke JI (2005) Increased action potential firing rates of layer 2/3 pyramidal cells in the prefrontal cortex are significantly related to cognitive performance in aged monkeys. Cereb Cortex 15: 409-418. 5. Weaver CM, Hof PR, Wearne SL, Lindquist WB (2004) Automated algorithms for multiscale morphometry of neuronal dendrites. Neural Comput 16: 1353-1383. 6. Wearne SL, Rodriguez A, Ehlenberger DB, Rocher AB, Henderson SC, et al. (2005) New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience 136: 661-680. 7. Rodriguez A, Ehlenberger DB, Hof PR, Wearne SL (2006) Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images. Nature Protocols 1: 2152-2161. 8. Rothnie P, Kabaso D, Hof PR, Henry BI, Wearne SL (2006) Functionally relevant measures of spatial complexity in neuronal dendritic arbors. J Theor Biol 238: 506-526. 9. Weaver CM, Wearne SL (2006) The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing 69: 1053-1057. 10. Weaver CM, Wearne SL (2007) Neuronal firing sensitivity to morphologic and active membrane parameters. PLoS Comput Biol submitted. 11. Kabaso D, Nilson J, Luebke JI, Hof PR, Wearne SL (2006) Electrotonic analysis of morphologic contributions to increased excitability with aging in neurons of the prefrontal cortex of monkeys. 2006 Neuroscience Meeting Planner. Atlanta, GA: Society for Neuroscience. 12. Coskren PJ, Hof PR, Wearne SL (2007) Age-related neuromorphological distortion affects stability and robustness in a simulated test of spatial working memory. BMC Neuroscience 8: P169. 13. Wearne SL, Gamkrelidze G, Weaver CM, Baker R (2006) Distinct modes of spike generation recorded from Area II neurons in goldfish hindbrain slices. 2006 Neuroscience Meeting Planner. Atlanta, GA: Society for Neuroscience. 14. Major G, Tank D (2004) Persistent neural activity: prevalence and mechanisms. Curr Opin Neurobiol 14: 675-684. 15. Pastor AM, de la Cruz RR, Baker R (1994) Eye position and eye velocity integrators reside in separate brainstem nuclei. Proc Natl Acad Sci 91: 807-811. 16. Straka H, Beck JC, Pastor AM, Baker R (2006) Morphology and physiology of the cerebellar vestibulolateral lobe pathways linked to oculomotor function in the goldfish. J Neurophysiol 96: 1963-1980. 17. Beck JC, Rothnie P, Straka H, Wearne SL, Baker R (2006) Precerebellar hindbrain neurons encoding eye velocity during vestibular and optokinetic behavior in the goldfish. J Neurophysiol 96: 1370-1382. 18. Koulakov AA, Raghavachari S, Kepecs A, Lisman JE (2002) Model for a robust neural integrator. Nat Neurosci 5: 77-82. 19. Goldman MS, Levine JH, Major G, Tank DW, Seung HS (2003) Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. Cereb Cortex 13: 1185-1195. 20. Lowenstein Y, Sompolinsky H (2003) Temporal integration by calcium dynamics in a model neuron. Nat Neurosci 6: 961-967. 21. Weaver CM, Gamkrelidze G, Baker R, Wearne SL (2005) Intrinsic dendritic properties contribute to firing regularity in model neurons of the velocity storage integrator. 2005 Abstract Viewer/Itinerary Planner. Washington, D.C.: Society for Neuroscience. 22. Weaver CM, Gamkrelidze G, Baker R, Wearne SL (2006) Sensitivity of firing dynamics to intrinsic dendritic properties in a model of neurons necessary for eye velocity neural integration. 2006 Neuroscience Meeting Planner. Atlanta, GA: Society for Neuroscience. 23. Cardoso MF, Salcedo RL, de Azevedo SF (1996) The simplex-simulated annealing approach to continuous non-linear optimization. Computers Chem Engng 20: 1065-1080. 24. Carnevale NT, Hines ML (2006) The NEURON Book. Cambridge, UK: Cambridge University Press. 25. Weaver CM (2007) AP shape and parameter constraints in optimization of compartment models. ModelDB. http://senselab.med.yale.edu/senselab/modeldb/ShowModel.asp?model=87473

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 神经元使用电信号相互通信。这些神经元信号（射击动力学）的时变模式对于信息编码至关重要。神经元发射动力学在很大程度上取决于由活性离子通道驱动的神经元膜的兴奋性，以及其树突的形状（形态）[1]。主动和形态学特性之间的相对重要性和相互作用仍然很众所周知，特别是因为这些特性，甚至它们确定的发射动力，在发育，学习，衰老和疾病期间可能会有所不同[2-4]。在计算模型中，主动和形态特征与非线性相互作用，由许多参数表示不同，幅度不同，甚至不同单位的测量单位。我是西奈山医学院研究学院的成员，在神经科学副教授苏珊·沃恩（Susan Wearne）的实验室工作。我们的研究计划是双重的：设计计算技术以有条不紊地研究科学问题[5-10]，并使用这些技术来研究内在神经元特性的组织如何影响功能[10-12]。该特定项目得到了NIH赠款（DC05669）的支持。工作记忆保持了对未来任务绩效所必需的最新事件的简短心理表示，这是许多大脑复杂功能的基础。参与工作记忆任务的网络和单个神经元的Wearne实验室研究属性[6,7,10-13]。例如，在输入结束后持续很长时间，持续的神经活动是整个大脑中观察到的工作记忆的标志[14]。在前庭（平衡）系统中，需要持久活动以响应给定的角度速度，以保持精确调节眼速度。在金鱼中，来自后脑核II区域II的神经元展示了这种眼速储存[15-17]。 II区域神经元在体内和体外显示许多有趣的特征[13,17]。最近的模型假设形态可能如何促进持续活动[18-20]，但没有人研究了现实的3D形态的贡献。随着II区域神经元的形态变化很大[16]，我们一直在评估形态如何影响II区域持续活动的精确调整的点火模式。使用下面描述的计算方法，我们已经证明形态可以显着促进神经元发射特性[10-12,21,22]。在II区域中，逐渐变细，直径和直径对一系列发射动力学有显着贡献，持续的钠，依赖钙依赖性钾和A型钾膜电导也是如此[10,21,22]。现在，我们将这些结果基于简单的形态模型扩展到我们在体内和体外收集的现实形态数据和生理学。进行这项研究所需的计算资源在我们转向更现实的模型中急剧增加。为了满足这一增加的计算需求，我想在通过Teragrid可用的超级计算机上获得一个开发分配帐户。评估形态和主动离子通道对生物学相关行为的贡献的计算方法要求分配所有参数（根据系统的不同，取决于系统的阶）与实验数据一致。优化通过自动搜索多维参数空间来识别参数集，以最大程度地减少代表模拟和实验（目标）数据之间的显着差异的参数集来简化此任务。虽然结果比手工调整较少主观，并且更快地识别出来，但自动参数优化通常需要数周的计算时间来识别合适的参数集。我的研究要求将模型参数拟合到三个不同区域II神经元的复杂形态以及几种减少模型的复杂形态。为了进行高效的搜索，我已经设计了一种适应性功能，该功能与许多神经元中观察到的神经元放电模式的形状相匹配[9,10]。同时，我还通过近期实施了模拟退火，这是一种约束参数搜索算法，以确保确定的参数在生物学上是合理的[9,23]。将模型拟合到区域II神经元的形态和生理数据之后，我们将探讨不同模型参数如何影响模型输出。为此，我将使用Wearne教授和我开发的新型敏感性分析应用[10]。重要的是，我们的方法可以预测要更改哪些参数，并通过多少参数来补偿其他参数的变化以恢复正常函数。这些预测超出了我们II区模型，可能对神经元衰老，疾病和创伤研究很重要。探索这些复杂模型需要大量的计算时间。为了评估模型输出对其许多参数的敏感性，每个参数必须独立扰动，而其他参数保持恒定并进行新的模拟。需要探索几个模型输出对10-20个模型参数的敏感性，在整个参数空间中成千上万的本地点，本研究需要相当多的计算时间。计算要求这项研究已经在本地资源上，160个处理器G5 Xserve群集运行Mac OSX。要及时完成本研究，我需要长时间访问多个处理器，但不需要访问共享存储器机器。到目前为止，有500 GB RAM足以完成这些模拟。预计这不会发生重大变化。我的磁盘空间需求不应超过1/3 TB。不需要访问磁带存储。我要求总共30,000个SUS，以对实验获得的三个区域形态中的每一个中的每一个进行参数优化和灵敏度分析，并分析相关的还原模型。我一次不需要超过120个处理器，而且通常会使用少。不同处理器之间不需要通信。健身函数和模拟退火算法均已在神经元中实现[24,25]，这是计算神经科学中的标准建模包之一。目前，模拟退火代码同时运行多个连续作业。将来，我们还希望利用平行神经元公告板的容量，可在由单个主节点控制的从属处理器上运行多个作业，以改善对参数空间的搜索。几乎所有Teragrid资源的帐户都可以满足这些需求。任何超级计算簇都应该很好地满足我的需求。 Neuron已经在圣地亚哥和匹兹堡计算中心（SDSC的数据标准，PSC的Bigben）上运行。如果其中一台机器上有足够的计算时间，那可能是最简单的。否则，神经元创作者之一迈克尔·海因斯（Michael Hines）已经在我被授予访问的任何机器上编译神经元方面提供了帮助。参考文献1。MainenZF，Sejnowski TJ（1996）树突结构对模型新皮质神经元的发射模式的影响。自然382：363-366。 2。DuanH，Wearne SL，Rocher AB，Macedo A，Morrison JH等。 （2003）与年龄相关的树突状和脊柱变化在猕猴中皮层凸出神经元的变化。 Cereb Cortex 13：950-961。 3。BucherD，Prinz AA，Marder E（2005）成人运动和生长期间运动模式预测的动物对动物变化。 J Neurosci 25：1611-1619。 4。ChangYM，Rosene DL，Killiany RJ，Mangiamele LA，Luebke JI（2005），前额叶皮层中2/3层锥体细胞的动作潜在触发速率与老年猴子的认知性能显着相关。 Cereb Cortex 15：409-418。 5。WeaverCM，Hof PR，Wearne SL，Lindquist WB（2004）神经元树突的多尺度形态计量学的自动算法。神经计算16：1353-1383。 6. Wearne SL，Rodriguez A，Ehlenberger DB，Rocher AB，Henderson SC等。 （2005）多个尺度上三维神经形态的成像，数字化和分析的新技术。神经科学136：661-680。 7。RodriguezA，Ehlenberger DB，Hof PR，Wearne SL（2006）Rayburst采样，一种用于从激光扫描显微镜图像的自动化三维形状分析的算法。自然协议1：2152-2161。 8。RothnieP，Kabaso D，Hof PR，Henry BI，Weawne SL（2006）神经元树突状乔木中空间复杂性的功能相关。 J理论生物学238：506-526。 9。WeaverCM，Wearne SL（2006）动作电位形状和参数约束在优化隔室模型中的作用。神经计算69：1053-1057。 10。WeaverCM，Wearne SL（2007）神经元发射对形态和活性膜参数的敏感性。 PLOS Comput Biol提交。 11。KabasoD，Nilson J，Luebke JI，Hof PR，Wearne SL（2006）对猴子前额叶皮层神经元衰老的兴奋性增加的形态学贡献。 2006年神经科学会议计划者。佐治亚州亚特兰大：神经科学学会。 12。CoskrenPJ，Hof PR，Wearne SL（2007）与年龄相关的神经形态失真会在模拟的空间工作记忆测试中影响稳定性和鲁棒性。 BMC神经科学8：P169。 13。WerneSL，Gamkrelidze G，Weaver CM，Baker R（2006）从金鱼后脑切片中的II区域神经元记录的尖峰产生的不同模式。 2006年神经科学会议计划者。佐治亚州亚特兰大：神经科学学会。 14. Major G，Tank D（2004）持续的神经活动：患病率和机制。 Curr Opin Neurobiol 14：675-684。 15。PastorAM，De La Cruz RR，Baker R（1994）眼睛位置和眼速积分器位于单独的脑干核中。 Proc Natl Acad Sci 91：807-811。 16。StrakaH，Beck JC，Pastor AM，Baker R（2006）小脑前庭叶的形态和生理学，与金鱼中的眼球动物功能有关。 J Neurophysiol 96：1963-1980。 17。BeckJC，Rothnie P，Straka H，Wearne SL，Baker R（2006）在金鱼中的前庭和光（Optookinetic行为）中编码眼部速度的前脑后部后脑神经元。 J Neurophysiol 96：1370-1382。 18。KoulakovAA，Raghavachari S，Kepecs A，Lisman JE（2002）强大的神经整合器的模型。 Nat Neurosci 5：77-82。 19。高盛MS，Levine JH，Major G，Tank DW，Seung HS（2003）在具有多个神经元的多个滞后树突的模型积分器中，稳健的持续神经活动。 Cereb Cortex 13：1185-1195。 20。LowensteinY，Sompolinsky H（2003）模型神经元中钙动力学的时间整合。 Nat Neurosci 6：961-967。 21。WeaverCM，Gamkrelidze G，Baker R，Wearne SL（2005）内在的树突特性有助于发射速度存储积分器的模型神经元的规律性。 2005摘要查看器/行程规划师。华盛顿特区：神经科学学会。 22. Weaver CM，Gamkrelidze G，Baker R，Wearne SL（2006）在眼速度神经整合所必需的神经元模型中，动力学对动力学对固有树突的敏感性。 2006年神经科学会议计划者。佐治亚州亚特兰大：神经科学学会。 23。CardosoMF，Salcedo RL，De Azevedo SF（1996）简单模拟的退火方法连续非线性优化。计算机化学工程20：1065-1080。 24。CarnevaleNT，Hines ML（2006）神经元书。英国剑桥：剑桥大学出版社。 25。WeaverCM（2007）在隔室模型优化中的AP形状和参数约束。模型。 http://senselab.med.yale.edu/senselab/modeldb/showmodel.asp?model=87473