Multi-sensory integration across reference frames

跨参考系的多感官整合

• 批准号: 356275-2013
• 负责人: Blohm, Gunnar
• 金额: $ 2.11万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=631079
• 关键词: Multi; sensory; integration; across; reference

Multi-sensory integration is the process by which multiple redundant or partially redundant pieces of sensory information are combined in order to provide a better estimate of a measured state. This process can be carried out in a statistically optimal fashion by combining each individual signal based on its reliability using a Bayesian framework. This is of importance not only for appropriate sensory-motor function in the brain, but is also crucial for many real-world industrial applications such as pose estimation for clinical movement analysis devices. One largely underexplored problem in multi-sensory integration is the role of reference frame transformations (RFTs). Different sensory signals are represented in different reference frames, e.g. sound relative to our head but vision relative to our eyes, and in order to combine this information, these signals must undergo a RFT so that they are represented in the same coordinates. This RFT requires that the brain estimates the rotation angles and axes. And since these are essentially sensory or internal signal estimates, they are noisy and have trial-to-trial variability (known as stochastic variability). Thus, a number of questions arise that we will attempt to answer: First and most important, how do noisy (stochastic) RFTs work and how do they affect multi-sensory integration in 3D? How can they be described mathematically in an analytical fashion suitable for quantitative data analyses? What brain areas carry out multi-sensory integration across reference frames? And: how can the new mathematical framework be applied to solving real-world problems for commercial movement analysis devices?We propose to answer these questions using a multi-disciplinary approach (mathematical modeling, behavioural experiments and brain imaging) that is deeply rooted in our previous NSERC-funded research on noise-less 3D RFTs. We will collaborate with C-Motion, Inc. to solve closely related multi-sensory integration problems in the pose estimation industry.

多感觉整合是将多个冗余或部分冗余的感觉信息片段组合以提供对测量状态的更好估计的过程。该过程可以通过使用贝叶斯框架基于其可靠性组合每个单独的信号以统计上最优的方式进行。这不仅对于大脑中适当的感觉运动功能非常重要，而且对于许多现实世界的工业应用也至关重要，例如临床运动分析设备的姿势估计。在多感觉整合中，一个很大程度上未被探索的问题是参考框架转换（RFTs）的作用。不同的感觉信号在不同的参考系中表示，例如，声音相对于我们的头部，而视觉相对于我们的眼睛，并且为了联合收割机组合这些信息，这些信号必须经历RFT，使得它们在相同的坐标中表示。这种RFT要求大脑估计旋转角度和轴。由于这些基本上是感官或内部信号估计，它们是嘈杂的，并具有试验间的变异性（称为随机变异性）。因此，出现了一些问题，我们将试图回答：首先，最重要的是，噪声（随机）RFTs如何工作，它们如何影响3D中的多感觉整合？如何以适合于定量数据分析的分析方式对它们进行数学描述？哪些大脑区域负责跨参考框架的多感觉整合？以及：新的数学框架如何应用于解决商业运动分析设备的现实问题？我们建议使用多学科方法（数学建模，行为实验和大脑成像）来回答这些问题，该方法深深植根于我们以前的NSERC资助的无噪声3D RFTs研究。我们将与C-Motion，Inc.合作。以解决姿态估计行业中密切相关的多传感器集成问题。