Computational mechanisms in the olfactory bulb

描述(申请人提供)：在感觉系统中，提取有关感兴趣刺激的有用信息的大部分任务落在初级感受器补体下游的神经回路上。为了正确解释次级神经信号，如嗅球内二尖瓣细胞对气味刺激的反应，了解这些后感觉神经回路接收和处理传入感觉信息的生理机制是至关重要的。嗅球是一种接近感觉外周的神经网络，描述良好且有界，是一种清晰的候选结构，可以在其中发展对神经编码和信息处理的跨分析水平的集成理解，从细胞内级联和膜特性到它们的行为后果。这种对神经加工机制的垂直整合的理解对于临床精神病学应用的发展将是非常有价值的，在临床应用中，必须将基因治疗或药物特异性地输送到适当的效应部位，以实现预期的、系统的或行为的变化。 本应用的主题是嗅球神经回路的生理约束计算模型，它是实现对全身功能的综合理解的重要工具。简而言之，设计良好和受约束的模型能够探索过于复杂而不能立即直观的多变量系统的能力。本应用的具体目标涉及一套基于非地形性对比度增强原理的嗅球肾小球层模型。第一个目标是在一个完整的网络模拟中实施这个模型机制(包括多达2000个肾小球)，并用肾小球成像数据挑战它的预测；第二个目标是建立一系列能够准确反映其内在动力学和药理学特性的肾小球层嗅球神经元(二尖瓣细胞、外部簇状细胞和肾小球周围细胞)的细胞分区模型。该项目的长期目标是将这两个线索合并到一个包含足够细胞细节的嗅球统一网络模型中，以实现来自不同分析水平的现有数据的定量集成：例如，解释球神经调节剂对膜特性、场现象和行为的影响。如果不使用计算模型，就不太可能实现这样的定量、综合结果。

DESCRIPTION (provided by applicant): In sensory systems, much of the task of extracting useful information about stimuli of interest falls to the neural circuitry downstream from the primary receptor complement. It is critical to understand the physiological mechanisms by which these postsensory neural circuits receive and process incoming sensory information in order to properly interpret secondary neural signals, such as the responses of mitral cells within the olfactory bulb to odor stimuli. As a well-described and delimited neural network close to the sensory periphery, the olfactory bulb is a clear candidate structure within which to develop an integrated understanding of neural coding and information processing across levels of analysis, from intracellular cascades and membrane properties through their behavioral consequences. Such a vertically integrated understanding of neural processing mechanics will be invaluable for the development of clinical psychiatric applications in which gene therapeutic or pharmacological agents must be specifically delivered to appropriate effector sites in order to effect the intended, systemic or behavioral changes. Physiologically constrained computational modeling of olfactory bulb neural circuitry, the subject of this application, is an important tool for achieving an integrated understanding of systemic function. Briefly, well-designed and constrained models enable exploration of the capabilities of multivariate systems that are too complex to be immediately intuitive. The specific aims of this application concern a set of models of the olfactory bulb glomerular layer based on the "non-topographical contrast enhancement" principle. The first aim is to implement this model mechanism in a full-scale network simulation (incorporating up to 2000 glomeruli) and challenge its predictions with glomerular imaging data, while the second is to develop a series of cellular compartmental models of glomerular layer olfactory bulb neurons (mitral, external tufted, and periglomerular cells) that can accurately reflect their intrinsic dynamical and pharmacological properties. The long-term goal of this project is to merge these two threads into a unified network model of the olfactory bulb containing sufficient cellular detail to enable the quantitative integration of existing data from different levels of analysis: for example, to interpret the effects of bulbar neuromodulators on membrane properties, field phenomena, and behavior. Quantitative, integrative results such as these are unlikely to be achieved without the use of computational modeling.