Neural basis of optimal cue combination: theory and experiments

最佳线索组合的神经基础：理论与实验

• 批准号: 0346785
• 负责人: Alexandre Pouget
• 金额: $ 10万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0346785&HistoricalAwards=false
• 关键词: Neural; basis; optimal; cue; combination

The nervous system can sense the world through a variety of sensory modalities providing complementary as well as redundant information about various aspects of the real world. Through a process known as multisensory integration, these sensory signals are merged and used to perform a variety of tasks, such as locating objects in space or identifying the words uttered by a speaker. With NSF funding, Dr. Alexandre Pouget is conducting a strongly theory-driven research program, combining modeling and human studies to investigate the neural basis of this process. The experiments involve asking human subjects to locate their arm in space using multisensory cues. The researchers seek to learn whether subjects can integrate over time three types of cues: visual, auditory and proprioceptive. The optimal strategy in this situation is to compute a weighted sum of those cues, with weights inversely proportional to the reliability of the cues. Intuitively, this makes sense: The system should put greater weights on cues that are more reliable. The funded experiments will determine whether subjects perform this weighted average, and whether those weights adapt when the reliability of the cues is changed from trial to trial. The models will be used to predict human performance and to develop a theory of optimal multisensory integration in neural circuits. The approach relies on a general theory of computation known as Bayesian inferences. This theory has been applied to a variety of problems ranging from sensory processing, high level reasoning (like medical diagnosis), and motor control. Therefore, understanding the neural basis of Bayesian inferences in the context of multisensory integration will have wide implications for general theories of how the brain works, with applications to computer vision and robotics. The Broader Impacts of this project include the development of a course in computational neuroscience at the University of Rochester and opportunities for undergraduate and graduate students to participate in research. This project will also foster collaboration with a laboratory in France directed by Dr. Jean-Rene Duhamel.

神经系统可以通过各种感觉模态来感知世界，这些感觉模态提供关于真实的世界的各个方面的补充信息以及冗余信息。通过一个被称为多感觉整合的过程，这些感觉信号被合并并用于执行各种任务，例如定位空间中的物体或识别说话者发出的单词。在NSF的资助下，Alexandre Pouget博士正在进行一项强有力的理论驱动的研究计划，结合建模和人体研究来研究这一过程的神经基础。这些实验包括要求人类受试者使用多感官线索定位他们的手臂。研究人员试图了解受试者是否可以随着时间的推移整合三种类型的线索：视觉，听觉和本体感受。在这种情况下，最佳策略是计算这些线索的加权和，权重与线索的可靠性成反比。直觉上，这是有道理的：系统应该对更可靠的线索赋予更大的权重。受资助的实验将确定受试者是否执行这种加权平均，以及当线索的可靠性从一次试验到另一次试验发生变化时，这些权重是否适应。这些模型将被用来预测人类的表现，并开发一个最佳的神经回路多感官整合的理论。这种方法依赖于一种被称为贝叶斯推理的通用计算理论。这一理论已被应用于各种各样的问题，从感觉处理，高层次的推理（如医疗诊断），和运动控制。因此，在多感觉整合的背景下理解贝叶斯推理的神经基础将对大脑如何工作的一般理论产生广泛的影响，并应用于计算机视觉和机器人技术。该项目的更广泛影响包括在罗切斯特大学开设计算神经科学课程，并为本科生和研究生提供参与研究的机会。该项目还将促进与Jean-Rene Duhamel博士领导的法国实验室的合作。