CAREER: Deciphering Brain Function Through Dynamic Sparse Signal Processing

适应环境变化的能力和对不良刺激的优化表现是大脑功能的标志之一。实时捕捉脑功能的自适应和鲁棒性不仅对于破译其潜在机制至关重要，而且对于设计具有自适应和鲁棒性的神经假体和脑机接口设备也至关重要。由于神经数据采集技术的进步，数据采集过程得到了极大的便利，从动物和人类的神经系统中产生了丰富的高维、动态和复杂的各种模式和条件下的数据池。然而，由于这些数据的维度不断增长，当前的建模范式和估计算法在处理这些数据时面临着挑战。本研究通过提供一个统一的框架来解决这些挑战，以有效地利用丰富的数据池，以便在系统神经科学中提供改变游戏规则的应用。理论和实验神经科学的证据表明，大脑活动是一个分布的高维时空过程，从稀疏的动态结构中出现。从计算的角度来看，稀疏性是拒绝干扰信号和实现大脑神经计算和信息表示鲁棒性的关键因素。因此，主要目标是开发一种数学原理方法，以高精度的可扩展方式捕获神经数据的动态性和稀疏性。通过关注听觉系统作为复杂大脑功能的典型实例，本研究探讨了系统神经科学中的几个基本问题，如可塑性，注意力和刺激解码。该研究与教育和拓展活动相结合，包括高中水平的实践研讨会，本科顶点项目和跨学科课程开发。

The ability to adapt to changes in the environment and to optimize performance against undesirable stimuli is among the hallmarks of the brain function. Capturing the adaptivity and robustness of brain function in real-time is crucial not only for deciphering its underlying mechanisms, but also for designing neural prostheses and brain-computer interface devices with adaptive and robust performance. Thanks to the advances in neural data acquisition technology, the process of data collection has been substantially facilitated, resulting in abundant pools of high-dimensional, dynamic, and complex data under various modalities and conditions from the nervous systems of animals and humans. The current modeling paradigm and estimation algorithms, however, face challenges in processing these data due to their ever-growing dimensions. This research addresses these challenges by providing a unified framework to efficiently utilize the abundant pools of data in order to deliver game-changing applications in systems neuroscience.Converging lines of evidence in theoretical and experimental neuroscience suggest that brain activity is a distributed high-dimensional spatiotemporal process emerging from sparse dynamic structures. From a computational perspective sparsity is a key ingredient in rejecting interfering signals and achieving robustness in neural computation and information representation in the brain. The main objective therefore is to develop a mathematically principled methodology that captures the dynamicity and sparsity of neural data in a scalable fashion with high accuracy. By focusing on the auditory system as a quintessential instance of sophisticated brain function, this research investigates several fundamental questions in systems neuroscience such as plasticity, attention, and stimulus decoding. The research is integrated with education and outreach activities including high school level hands-on workshops, undergraduate capstone projects, and interdisciplinary course development.