Mathematical Sciences: Statistical Inference on Synaptic Release Mechanisms

本计画将发展混合分布之贝氏统计分析方法，以研究神经元突触活动之机制。 突触机制的生理学分析（如短时程或长时程增强）是基于对单个突触位点活性的假设，但很少能直接分析这些单个位点。 因此，为了从生理记录推断各个部位的基线活动和动态变化，已经开发了统计/神经生理模型，其包括来自多个部位的输出并迎合背景噪声。这些模型假设记录的细胞输出表示由突触信号“分量”组成的随机混合分布，其中每个“分量”表示一个或多个突触位点。在这方面，将探讨使用（不确定数量）成分的（不确定）混合物的贝叶斯混合模型和分析方法。这个试点项目的目的是基准和验证新的贝叶斯方法的实验数据的背景下，从突触与少量的神经递质网站。 从技术上讲，研究将根据现有的突触噪声数据，生理证据和实验条件制定模型参数的先验分布;研究模拟以检查操作特性并探索对先验和模型规格的敏感性和鲁棒性;推导有关生理参数的推论;确定评估拟合优度的方法;与现有和传统的方法进行比较;开发计算机软件，以实施建模技术。 该项目代表了一个合作的，跨学科的倡议，开发新的方法进行分析和统计推断有关的机制，在动物神经系统的神经接头的电化学信号传输。理论发展和生理数据之间的相互作用将在识别和解决神经系统中生理过程和机制的不确定性，评估神经信号，突触输出的站点到站点的变化，识别关键突触特征以及评估不同实验和生理条件下的变化方面发挥关键作用。 该项目将涉及对现有和即将推出的生理数据集进行大量分析，以评估统计模型的有效性和对潜在生理学的推断。 当前的目标是验证新的统计方法，通过展示其应用和改进现有的统计分析在突触反应分析。最终，这些方法有望有助于对人类和其他神经系统中信号机制的基本理解。

This project will develop methods of Bayesian statistical analysis for mixture distributions in studies of mechanisms of neuronal synaptic activity. Physiological analysis of synaptic mechanisms (such as short- or long-term potentiation) is based on presumptions regarding activity at individual synaptic sites, yet such individual sites can rarely be analyzed directly. Thus, to infer baseline activity and dynamic changes at individual sites from physiological recordings, statistical/neurophysiological models have been developed which include the output from multiple sites and cater for background noise. These models assume that the recorded cell output represents a random mixture distribution composed of synaptic signal 'components,' where each 'component' represents one or more synaptic sites. Bayesian mixture models and methods of analysis using (uncertain) mixtures of (uncertain numbers of) components will be explored in this context. This pilot project will aim to benchmark and validate the novel Bayesian approaches in the context of experimental data from synapses with small numbers of neural transmitter sites. Technically, research will formulate prior distributions for model parameters based on available synaptic noise data, physiological evidence, and experimental conditions; study simulations to examine operating characteristics and explore the sensitivity and robustness to prior and model specifications; derive inferences about physiological parameters; determine methods of assessment of goodness of fit; make comparisons with existing and traditional approaches; and develop computer software development for implementation of the modelling techniques. The project represents a collaborative, cross-disciplinary initiative to develop novel methods for analysis and statistical inference concerning the mechanisms governing electrochemical signal transmission at neural junctions in animal nervous systems. Interaction between the theoretical development and physiological data will play a critical role in identifying and resolving uncertainties about physiological processes and mechanisms in nervous systems, assessing site to site variability in neural signals, synaptic output, identifying critical synaptic characteristics, and assessing changes under differing experimental and physiological conditions. The project will involve substantial analysis of existing and forthcoming physiological data sets in order to provide assessments of the validity of the statistical models and the derived inferences with respect to underlying physiology. The immediate objective is to validate the new statistical approaches proposed by demonstrating their application and improvements over existing statistical analyses in synaptic response analysis. Ultimately these methods are expected to contribute to fundamental understanding of signalling mechanisms in human and other nervous systems.