Computational mechanisms in the olfactory bulb

描述（由申请人提供）：在感觉系统中，提取关于感兴趣的刺激的有用信息的大部分任务福尔斯在初级受体补体下游的神经回路上。理解这些感觉后神经回路接收和处理传入的感觉信息的生理机制是至关重要的，以便正确地解释次级神经信号，例如嗅球内的僧帽细胞对气味刺激的反应。作为一个良好的描述和界定的神经网络接近的感觉外围，嗅球是一个明确的候选结构，在其中发展的神经编码和信息处理的分析水平，从细胞内级联和膜特性通过其行为后果的综合理解。这种垂直整合的神经加工机制的理解将是非常宝贵的临床精神病学的应用程序中，基因治疗或药理学药物必须专门提供给适当的效应位点，以影响预期的，系统的或行为的变化的发展。 生理限制的嗅球神经回路的计算建模，本申请的主题，是一个重要的工具，实现系统功能的综合理解。简而言之，精心设计和约束的模型可以探索过于复杂而无法立即直观的多变量系统的能力。本申请的具体目的涉及基于“非地形对比度增强”原理的嗅球肾小球层的一组模型。第一个目标是在一个全面的网络模拟（包括多达2000肾小球），并挑战其预测与肾小球成像数据，而第二个是开发一系列的细胞房室模型的肾小球层嗅球神经元（二尖瓣，外部簇状细胞和肾小球周围细胞），可以准确地反映其内在的动力学和药理学特性。该项目的长期目标是将这两个线程合并到一个统一的嗅球网络模型中，该模型包含足够的细胞细节，以便能够定量整合来自不同分析水平的现有数据：例如，解释延髓神经调质对膜特性，场现象和行为的影响。如果不使用计算建模，这些定量的综合结果是不可能实现的。

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.