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.

在认知要求高的任务中，比如写一篇文章或解决一个难题，任务表现会根据一个人的压力或兴奋程度而波动。表现在低唤醒（疲劳）或高唤醒（激动）状态下很差，并在中间唤醒水平达到最佳状态，通俗地描述为“在该区域”。尽管这种现象在人类和其他物种中都得到了广泛的研究，但仍然不知道大脑是如何达到其峰值性能的。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。该项目将动物的行为模型和神经数据与人工神经网络相结合，试图解释皮层回路如何调节自身的动力学特性以优化信息处理。心理物理学的Yerkes-Dodson倒U定律描述了认知任务表现与动物唤醒状态之间的关系，最佳表现出现在中等唤醒水平。该项目旨在了解能够实现认知性能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制的假设出发，灵活地适应它们处理信息和产生行为的方式。该项目将沿着沿着三个主要方向进行。首先，它将研究在感官辨别以及自然觅食过程中最佳和次佳性能状态的行为特征，以及它们与动物的唤醒水平和运动的关系。其次，它将阐明最佳的性能状态是如何产生的集体活动的群体皮层神经元。第三，从生物电路中获得的见解将为设计大脑启发的人工神经网络提供信息，这些神经网络能够学习以稳健，快速和有效的方式实现多任务。研究、教育和外展目标将通过一个新颖的科学传播计划整合，该计划通过基于网络的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。