CAREER: Neural mechanisms underlying optimal performance

职业：最佳表现背后的神经机制

• 批准号: 2238247
• 负责人: Luca Mazzucato
• 金额: $ 60万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238247&HistoricalAwards=false
• 关键词: CAREER; Neural; mechanisms; underlying; optimal

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.

在认知要求较高的任务中，例如写论文或解决难题，任务表现会根据一个人的压力或唤醒水平而波动。在低唤醒（疲倦）或高唤醒（焦躁）状态下，表现较差，并在较高的唤醒状态下达到最佳状态。中等唤醒水平，通俗地描述为“处于区域中”，尽管这种现象已经在人类和其他物种中进行了研究，但仍不清楚大脑如何达到其最佳性能。为了确定大脑如何实现和维持最佳性能状态的计算原理，该项目将动物行为模型和神经数据的见解与人工神经网络相结合，旨在解释皮层回路如何调节其自身的动态特性以优化。心理物理学的耶基斯-多德森倒 U 定律描述了认知任务表现与动物的唤醒状态之间的关系，最佳表现发生在中等唤醒水平。认知表现的最优性以及人工智能系统是否可以利用这些机制，假设神经回路产生的内在变异性被利用和调节，以灵活地适应它们处理信息和生成行为的方式。该项目将沿着三个主要方向进行。首先，它将检查感官辨别和自然觅食期间最佳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，它将阐明最佳表现状态是如何从集体中产生的。第三，从生物电路中获得的见解将为受大脑启发的人工神经网络的设计提供信息，该网络能够以稳健、快速和有效的方式学习实现多任务研究、教育和推广目标。将通过一个新颖的科学传播计划进行整合，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 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。