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

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能够以智能和适应性的方式感知、思考和行动。知道了这个问题的答案，我们就可以设计出具有自适应能力的全新技术，这种能力将远远超过现有技术的能力。最近的行为和神经生物学实验表明，大脑可能依赖概率原理来感知、行动和学习。拟议的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得模型：(1)在协作生物实验中，(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：我们将在把感觉信息处理和动作选择问题作为概率推理问题的基础上发展新的神经计算模型。我们将研究生物模型（如整合-激活神经元网络）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数量的概率（贝叶斯）推理算法。我们还将探讨控制突触可塑性的众所周知的神经生物学规则与统计推导的学习规则之间的联系。使用皮质电图研究的实验验证：我们的贝叶斯推理模型将由co-PI Ojemann的小组在实验中进行测试，该实验涉及在脑外科手术前几天监测的同意患者的大脑皮质电图（ECoG）信号。实验将集中在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，并根据神经生物学数据开发新的概率模型。在概率机器人和脑机接口中的应用：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。我们将主要集中在感觉运动的整合和推断的行动稳定控制。同时，我们将探索我们的概率模型在脑机接口中的适用性。具体目标是通过实时从ECoG信号中概率推断真实和想象的运动来控制计算机屏幕上的光标和控制一个4自由度的机械臂。该项目的教育部分包括为一名研究生提供跨学科培训，为本科生提供研究经验，并以新的脑机接口研究生课程的形式开发课程。智力优势：该提议的研究代表了第一个跨学科的努力之一，旨在开发和测试一个严格的概率框架，以理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。将其应用于脑机接口可以极大地提高瘫痪和残疾患者的生活质量。该补助金将使研究生能够在多学科环境中进行培训。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，为研究生准备工业和学术生涯提供指导经验。

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.