Computational mechanisms in the olfactory bulb

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都福尔斯在神经元上。 初级受体补体下游的神经回路。关键是要了解 这些后感觉神经回路接收和处理传入信息的生理机制 感觉信息，以便正确地解释次级神经信号，例如二尖瓣的反应。 嗅球细胞对气味刺激的反应。作为一个良好描述和定界的神经网络， 感觉外围，嗅球是一个明确的候选结构，在其中发展一个综合的 理解神经编码和信息处理的分析水平，从细胞内 级联和膜性能通过其行为后果。这样一个垂直整合的 了解神经处理机制将是非常宝贵的临床精神病学的发展， 其中基因治疗剂或药理学试剂必须特异性地递送至 适当的效应位点，以实现预期的、系统的或行为的变化。 生理限制的嗅球神经回路的计算建模，这一主题 建议，是一个重要的工具，实现系统功能的综合理解。简言之， 良好设计和约束模型使探索多变量系统的能力， 太复杂而无法立即直观。本提案的具体目标涉及一套 嗅球小球层的“非地形对比增强”的原则。第一 目标是在全尺寸网络仿真（包含2000个 肾小球），并挑战其预测与肾小球成像数据，而第二个是开发一个 肾小球层嗅球神经元的一系列细胞隔室模型（二尖瓣，外部簇状， 和肾小球周围细胞），能够准确反映其内在动力学和药理学 特性.该项目的长期目标是将这两个线程合并为一个统一的网络模型， 嗅球包含足够的细胞细节，使现有数据的定量整合成为可能 从不同层次的分析：例如，解释延髓神经调质对 膜特性、场现象和行为。诸如此类的定量、综合结果是 如果不使用计算机建模，就不可能实现。

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 proposal, 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 proposal 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.