Towards Analysis and Control of Dynamic Brain States

神经编码研究的问题是，大脑如何将原始信号转化为可用的信息，使我们能够看、听和思考。研究神经编码的机制是一项持久的科学挑战，对揭示大脑的运作具有重要意义。该奖项支持从工程理论角度研究神经编码的新方法的基础研究。这种观点认为，大脑中的网络可以根据其物理原理进行建模，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，大脑的复杂性远远超过了那些典型的工程系统，因此，现有的工程方法必须适应和增强，以满足生物学的现实。通过解决这些差距，这项研究将导致工程上的新方法，神经技术的进步，以及研究人类大脑功能的新方法。这项研究是高度跨学科的，涉及系统工程、数学和神经科学。作为该奖项的一部分，将推行几项新举措，以促进这些学科之间的对话，并通过为当地高中生建立暑期研究实习机会，促进代表性不足的群体更多地参与工程和科学。该奖项通过动态系统和控制理论的视角来研究神经网络。这种方法是基于这样一个前提，即理解神经网络的输入输出关系，由其动态调节，将为神经科学的基本问题提供新的思路，包括神经动力学和信息处理之间的联系。为了实现这一目标，该奖项集中在两个主要目标上：首先，将制定神经科学驱动的系统理论性质（如可达性）的适应，以便了解动态如何控制神经输入-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，它表征了给定时间内的活动和网络结构。其次，将开发用于调制这些状态的控制方法。为此，将根据网络赋予的系统理论性质定义一类新的目标函数。例如，这样的目标将涉及使用控件来扩展网络的可达空间，而不仅仅是控制其活动。在这种情况下，控制输入可能是相当普遍的，并且将研究几个特定的场景，包括神经刺激。

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.