Towards Analysis and Control of Dynamic Brain States

神经编码的研究询问大脑如何将原始信号转换为有用的信息，使我们能够看到、听到和思考。研究神经编码的机制是一项持久的科学挑战，对揭示大脑的工作方式具有重要意义。该奖项支持基础研究，这将使从工程理论角度研究神经编码的新方法成为可能。这一观点认识到，大脑中的网络可以根据其物理条件进行建模，因此，可以使用许多用于研究复杂工程系统(如飞机和电网)的相同工具进行研究。然而，大脑的复杂程度远远超过了典型的工程系统，因此，必须对现有的工程方法进行调整和增强，以满足生物现实。通过解决这些差距，这项研究将带来工程上的新方法，神经技术的进步，以及研究人脑功能的新方法。这项研究是高度多学科的，涉及系统工程、数学和神经科学。作为该奖项的一部分，将采取几项新的举措，以促进这些学科之间的对话，并通过为当地高中生设立暑期研究实习机会，促进未被充分代表的群体更多地参与工程和科学领域。该奖项通过动力系统和控制理论的透镜来探讨神经元网络。这种方法的前提是，理解神经网络的输入输出关系，由其动力学调节，将为神经科学中的基本问题提供新的线索，包括神经动力学和信息处理之间的联系。为了追求这一目标，该奖项集中在两个主要目标上：第一，将制定以神经科学为动机的系统理论属性的适应性，如可达性，以了解动力学如何管理神经输入-输出关系。由于大脑网络中的连接不断适应，因此重点将放在大脑状态的概念上，这一概念同时表征了给定时间的活动和网络结构。其次，将开发对这些状态进行调制的控制方法。为此，将根据网络所赋予的系统理论属性来定义一类新的目标函数。例如，这样的目标将涉及使用控制来扩大网络的可达空间，而不仅仅是控制其活动。在这种情况下，控制输入可能是非常一般的，将研究包括神经刺激在内的几个特定情景。

The study of neural coding asks how the brain converts raw signals into usable information that allows us to see, hear and think. Studying the mechanisms of neural coding is a persistent scientific challenge that has important implications for uncovering the workings of the brain. This award supports fundamental research that will enable a new approach to studying neural coding from the perspective of engineering theory. This perspective recognizes that networks in the brain can be modeled in terms of their physics and, thus, studied using many of the same tools that are used to study complex engineered systems such as aircraft and power grids. However, the brain possesses a level of complexity that far exceeds those of typical engineered systems and, consequently, existing engineering approaches must be adapted and augmented to meet biological realities. By addressing these gaps, this research will lead to new methods in engineering, advances in neural technology, and new ways of studying human brain function. This research is highly multi-disciplinary, involving systems engineering, mathematics and neuroscience. As part of the award, several new initiatives will be pursued to facilitate dialogue across these disciplines and to foster increased participation of underrepresented groups in engineering and science through the establishment of summer research internships for local high school students.This award approaches neuronal networks through the lens of dynamical systems and control theory. This approach is based on the premise that understanding the input-output relationships of neuronal networks, mediated by their dynamics, will shed new light on fundamental questions in neuroscience, including the link between neural dynamics and information processing. In pursuit of this goal, the award focusses on two main objectives: First, neuroscientifically-motivated adaptions of systems theoretic properties, such as reachability, will be formulated so as to understand how dynamics govern neural input-output relationships. Since the connections in brain networks constantly adapt, emphasis will be placed on the notion of a brain state, which characterizes both the activity and the network structure at a given time. Second, control methods will be developed for the modulation of such states. To do so, a new class of objective functions will be defined in terms of the systems-theoretic properties conferred by the network. For example, such objectives will involve using controls to expand a network's reachable space, rather than just controlling its activity. The control input in this context may be quite general, and several specific scenarios, including neurostimulation, will be studied.