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Novel techniques for stochastic modelling of time-dependent multivariate relationships with application to primary visual cortex

时间依赖性多元关系随机建模的新技术及其应用于初级视觉皮层

基本信息

批准号：
EP/S005692/1
负责人：
Arno Onken
金额：
$ 37.52万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS005692%2F1
关键词：
Novel techniques stochastic modelling time

项目摘要

Advances in data acquisition technologies lead to the availability of ever more complex datasets. Often, the gathered variables have fundamentally different statistics, some being continuous while others are discrete. In many domains, the relationships between the recorded variables are of particular importance and also changing in time. One such domain is computational neuroscience where it was recently shown that even in early sensory brain areas, neural responses to stimuli are modulated by behavioural context. The precise functional interactions underlying this modulation are currently unknown but nonetheless important for understanding how the amazing versatility of sensory processing comes about. From an analytical point of view, understanding the complex interactions between neural activity, behaviour and task variables, all being subject to different statistics and timescales, is a major challenge.In this project, we will address the general problem of assessing probabilistic descriptions of time-dependent relationships between elements with mixed statistics as motivated by the context-dependent sensory processing problem encountered in neuroscience. To join mixed elements, we will use parametric copula models embedded in a Bayesian framework for time-varying parameters. For model fitting, we will use an inference scheme based on Expectation Propagation in conjunction with Gaussian Process priors, the latter being naturally suited to take into account different timescales. Contrary to other commonly applied methods, this approach will make stochastic relationships explicit and generate interpretable joint models of elements with strikingly different statistics.In order to investigate neural response modulation and in particular context-dependent visual processing, we will apply our analysis framework to data already recorded by project partner Dr Nathalie Rochefort. The data consist of fluorescence changes in large populations of neurons as recorded from primary visual cortex of awake behaving mice using two-photon calcium imaging. The data also include concurrently recorded behavioural and task variables gathered from a virtual reality environment. Our analysis will deepen our understanding of functional relationships in primary visual cortex that make the visual system so versatile, thereby providing new system state characterizations as well as improved sensory decoders.The development of versatile time-dependent relationship models will be driven by the particular neuroscience application to understand context-dependent relationships in primary visual cortex, but will be more broadly applicable to many other domains where stochastic relationship analysis is of importance.

数据采集技术的进步导致越来越复杂的数据集的可用性。通常，收集到的变量具有根本不同的统计数据，有些是连续的，而另一些是离散的。在许多领域中，记录变量之间的关系是特别重要的，并且随着时间的变化而变化。其中一个领域是计算神经科学，最近的研究表明，即使在早期的感觉大脑区域，对刺激的神经反应也受到行为环境的调节。这种调节背后的精确功能相互作用目前尚不清楚，但对于理解感觉处理的惊人多样性是如何产生的仍然很重要。从分析的角度来看，理解神经活动，行为和任务变量之间复杂的相互作用，所有这些都受到不同的统计和时间尺度的影响，是一个主要的挑战。在这个项目中，我们将解决评估具有混合统计的元素之间时间依赖关系的概率描述的一般问题，这是由神经科学中遇到的上下文依赖的感觉处理问题引起的。为了连接混合元素，我们将使用嵌入时变参数贝叶斯框架中的参数耦合模型。对于模型拟合，我们将使用基于期望传播与高斯过程先验的推理方案，后者自然适合考虑不同的时间尺度。与其他常用的方法相反，这种方法将使随机关系明确，并产生具有显著不同统计量的元素的可解释联合模型。为了研究神经反应调制，特别是与上下文相关的视觉处理，我们将把我们的分析框架应用于项目合作伙伴Nathalie Rochefort博士已经记录的数据。这些数据包括用双光子钙成像从清醒行为小鼠的初级视觉皮层记录的大量神经元的荧光变化。数据还包括从虚拟现实环境中收集的同时记录的行为和任务变量。我们的分析将加深我们对初级视觉皮层功能关系的理解，这些功能关系使视觉系统如此多样化，从而提供新的系统状态表征以及改进的感觉解码器。多用途的时间依赖关系模型的发展将由特定的神经科学应用来驱动，以理解初级视觉皮层的上下文依赖关系，但将更广泛地适用于许多其他领域，其中随机关系分析是重要的。