BIC: Probabilistic Neural Computation: Models and Applications in Robotics and Brain-Machine Interfaces

BIC：概率神经计算：机器人和脑机接口中的模型和应用

• 批准号: 0622252
• 负责人: Rajesh Rao
• 金额: --
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0622252&HistoricalAwards=false
• 关键词: BIC; Probabilistic; Neural; Computation; Models

One of the most outstanding problems in science today is how the activities of the ten billion or so neurons in the human brain allow a person to perceive, think, and act in an intelligent and adaptive manner. Knowing the answer to this question would allow the design of radically new technologies with adaptive capabilities that would far outstrip the capabilities of technologies existing today. Recent behavioral and neurobiological experiments have suggested that the brain may rely on probabilistic principles for perception, action, and learning. The goal of the proposed research project is to develop a rigorous probabilistic framework for neural computation and to test the resulting models in two ways: (1) in collaborative biological experiments, and (2) in applications involving robotics and brain-machine interfaces. Our specific research goals include:1. Probabilistic Models of Neural Computation: We will develop new models of neural computation based on treating the problems of sensory information processing and action selection as probabilistic inference problems. We will investigate how biological models such as networks of integrate-and-fire neurons can represent probability distributions and how the propagation of neural activities in such networks can implement algorithms for probabilistic (Bayesian) inference of unknown quantities. We will also explore the connections between well-known neurobiological rules governing synaptic plasticity and statistically-derived learning rules.2. Experimental Validation using Electrocorticographic Studies: Our models of Bayesian inference will be tested by co-PI Ojemann's group in experiments involving electrocorticographic (ECoG) signals recorded from the human brain in consenting patients being monitored in the days prior to brain surgery. Experiments will focus on testing the predictions of our models in tasks involving visual discrimination, recognition, and sensorimotor integration. Results from the experiments will be used to refine existing models and develop new probabilistic models inspired by neurobiological data.3. Applications in Probabilistic Robotics and Brain-Machine Interfaces: We will test the robustness of our probabilistic models by implementing the corresponding algorithms on an existing humanoid robot in PI Rao's laboratory. We will be focusing primarily on sensorimotor integration and inference of actions for stable control of movements. Simultaneously, we will explore the applicability of our probabilistic models to brain-machine interfaces. The specific goals are to control a cursor on a computer screen and control a 4-degrees-of-freedom robotic arm by probabilistically inferring real and imagined movements from ECoG signals in real time.The educational component of the project involves interdisciplinary training for one graduate student, research experiences for undergraduates, and curriculum development in the form of a new graduate level course on brain-machine interfaces.Intellectual Merit: The proposed research represents one of the first interdisciplinary efforts to develop and test a rigorous probabilistic framework for understanding neuronal computation in the brain. Also novel is the application of neurally-inspired probabilistic models to robotics and brain-machine interfaces, two areas that could benefit tremendously from the robustness and adaptability afforded by such models.Broader Impact: If successful, this research will lead to a new understanding of computation in the brain, offering unique insights into the mechanisms underlying human behavior and cognition. The application to brain-machine interfaces could dramatically improve the quality of life of paralyzed and disabled patients. The grant will enable the training of a graduate student in a multidisciplinary environment. Promising undergraduates, including students from underrepresented groups, will be paired with graduate students, providing valuable research experience for the undergraduates and mentoring experience for graduate students preparing for industrial and academic careers.

当今科学中最杰出的问题之一是，人脑中一亿左右神经元的活动如何使人以聪明和适应性的方式感知，思考和行动。知道这个问题的答案将允许设计具有自适应能力的根本新技术，这将远远超过当今现有的技术的能力。最近的行为和神经生物学实验表明，大脑可能依靠概率原则来感知，行动和学习。拟议的研究项目的目的是为神经计算开发严格的概率框架，并以两种方式测试所得模型：（1）在协作生物学实验中，以及（2）在涉及机器人和脑部接口的应用中。我们的具体研究目标包括：1。神经计算的概率模型：我们将基于将感觉信息处理和动作选择的问题作为概率推理问题开发新的神经计算模型。我们将研究生物学模型，例如集成和开火神经元网络如何代表概率分布，以及此类网络中神经活动的传播如何实现概率（贝叶斯）算法的算法。我们还将探讨有关突触可塑性的著名神经生物学规则与统计衍生的学习规则之间的联系。2。使用皮质学研究的实验验证：我们的贝叶斯推理模型将由Co-Pi Ojemann的组在涉及人脑中记录的电视学（ECOG）信号的实验中测试，以在脑部手术前几天监测同意患者。实验将集中于在涉及视觉歧视，识别和感觉运动集成的任务中测试我们模型的预测。实验的结果将用于完善现有模型，并开发出受神经生物学数据启发的新概率模型。3。概率机器人技术和脑机界面中的应用：我们将通过在Pi Rao实验室中实现现有的人形机器人机器人的相应算法来测试概率模型的鲁棒性。我们将主要关注感官的整合和对运动稳定控制的动作的推断。同时，我们将探索概率模型对脑机界面的适用性。具体目标是通过概率地实时推断出来自ECOG信号的真实和想象的动作来控制计算机屏幕上的光标，并控制一个4度机器人的机器人。拟议的研究是开发和测试严格概率框架以了解大脑中神经元计算的严格概率框架的第一个跨学科努力之一。同样新颖的是神经启发的概率模型在机器人技术和脑机界面上的应用，这两个领域可能会从这种模型带来的鲁棒性和适应性中受益匪浅。BROADER的影响：如果成功，这项研究将导致对大脑的计算的新理解，从而为人类行为和认知的机制提供独特的见解。在脑机界面上的应用可能会大大改善瘫痪和残疾患者的生活质量。该赠款将使在多学科环境中对研究生进行培训。有前途的本科生，包括来自代表性不足的小组的学生，将与研究生配对，为本科生提供宝贵的研究经验，并为准备工业和学术职业的研究生提供指导经验。