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.

当今科学中最突出的问题之一是人类大脑中大约100亿个神经元的活动如何使人以智能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适应能力的全新技术，这些技术将远远超过当今现有技术的能力。最近的行为和神经生物学实验表明，大脑可能依赖于感知，行动和学习的概率原则。拟议研究项目的目标是为神经计算开发一个严格的概率框架，并以两种方式测试所产生的模型：（1）在协作生物实验中，以及（2）在涉及机器人和脑机接口的应用中。我们的具体研究目标包括：1.神经计算的概率模型：我们将开发新的神经计算模型，将感觉信息处理和动作选择问题视为概率推理问题。我们将研究生物模型（如整合和激发神经元网络）如何表示概率分布，以及神经活动在此类网络中的传播如何实现未知量的概率（贝叶斯）推断算法。我们还将探讨著名的神经生物学规则之间的联系，支配突触可塑性和神经衍生的学习规则。使用皮层电图研究的实验验证：我们的贝叶斯推断模型将由共同PI Ojemann小组在涉及脑手术前几天在同意的患者中记录的人脑皮层电图（ECoG）信号的实验中进行测试。实验将侧重于测试我们的模型在涉及视觉辨别，识别和感觉运动整合的任务中的预测。实验结果将用于改进现有模型，并开发受神经生物学数据启发的新概率模型。概率机器人和脑机接口的应用：我们将通过在PI Rao实验室现有的人形机器人上实现相应的算法来测试我们的概率模型的鲁棒性。我们将主要关注感觉运动整合和动作推理，以实现对运动的稳定控制。同时，我们将探索我们的概率模型对脑机接口的适用性。具体目标是控制计算机屏幕上的光标，并通过真实的概率推断ECoG信号的真实的和想象运动来控制4自由度机器人手臂。该项目的教育部分包括一名研究生的跨学科培训，本科生的研究经验，以及以新的研究生水平课程的形式开发脑机接口课程。智力优势：这项研究代表了第一个跨学科的努力，以开发和测试一个严格的概率框架，以了解大脑中的神经元计算。同样新颖的是将神经启发的概率模型应用于机器人和脑机接口，这两个领域可以从这种模型提供的鲁棒性和适应性中受益匪浅。更广泛的影响：如果成功，这项研究将导致对大脑中计算的新理解，为人类行为和认知的潜在机制提供独特的见解。脑机接口的应用可以极大地改善瘫痪和残疾患者的生活质量。这笔赠款将使一名研究生能够在多学科环境中接受培训。有前途的本科生，包括来自代表性不足群体的学生，将与研究生配对，为本科生提供宝贵的研究经验，并为准备从事工业和学术职业的研究生提供指导经验。