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.
