CAREER: Neural mechanisms underlying optimal performance

在认知要求较高的任务中，比如写一篇文章或解决一个谜题，任务表现会随着一个人的压力或兴奋程度而波动。在低唤醒（疲劳）或高唤醒（激动）状态下，表现很差，在中等唤醒水平时达到最佳状态，通俗地称为“处于状态”。尽管这一现象已经在人类和其他物种中得到了广泛的研究，但大脑是如何达到其最佳表现的仍不得而知。该项目的目标是确定大脑如何实现和维持最佳性能状态的计算原理。结合行为模型和人工神经网络动物神经数据的见解，该项目试图解释皮质回路如何调节其自身的动态特性以优化信息处理。心理物理学的耶克斯-多德森倒u定律描述了认知任务的表现与动物的觉醒状态之间的关系，最佳表现出现在中级唤醒水平。该项目旨在了解在认知表现中实现灵活性和最优性的神经机制，以及这些机制是否可以被人工智能系统利用，从神经回路产生的内在可变性被利用和调节以灵活地适应它们处理信息和产生行为的方式的假设出发。该项目将沿着三个主要方向进行。首先，它将研究在感官辨别和自然觅食过程中最优和次优表现状态的行为特征，以及它们与动物的唤醒水平和运动的关系。其次，它将阐明最佳性能状态是如何从皮质神经元群体的集体活动中产生的。第三，从生物电路中获得的见解将为大脑启发的人工神经网络的设计提供信息，这些神经网络能够以鲁棒、快速和有效的方式学习实现多任务。研究、教育和外展目标将通过一个新颖的科学交流计划整合，通过网络漫画传达神经科学和人工智能的概念。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In cognitively demanding tasks, such as writing an essay or solving a puzzle, task performance fluctuates depending on one's level of stress or arousal. Performance is poor in low arousal (tired) or high arousal (agitated) states, and reaches an optimum at an intermediate arousal level, colloquially described as being “in the zone”. Although this phenomenon has been extensively investigated in both humans and other species, it is still unknown how the brain achieves its peak performance. The goal of this project is to identify the computational principles underlying how optimal performance states are achieved and maintained by the brain. Combining insights from models of behavior and neural data in animals with artificial neural networks, this project seeks to explain how cortical circuits can regulate their own dynamical properties to optimize information processing.The Yerkes-Dodson inverted-U law of psychophysics describes the relationship between cognitive task performance and an animal's state of arousal, with best performance occurring at intermediate arousal levels. This project seeks to understand the neural mechanisms that enable flexibility and optimality in cognitive performance and whether these mechanisms can be harnessed by AI systems, working from the hypothesis that the intrinsic variability produced by neural circuits is harnessed and modulated to flexibly adapt the way they process information and generate behavior. The project will proceed along three main directions. First, it will examine the behavioral signatures of optimal and suboptimal performance states during sensory discrimination as well as naturalistic foraging, and their relationship to an animal’s arousal level and movements. Second, it will elucidate how optimal performance states arise from the collective activity of populations of cortical neurons. Third, the insights obtained from biological circuits will inform the design of brain-inspired artificial neural networks capable of learning to achieve multi-tasking in a robust, fast, and efficient way. Research, education, and outreach goals will be integrated through a novel scientific communication program conveying concepts from neuroscience and artificial intelligence through web-based comics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.