CAREER: Neural mechanisms underlying optimal performance
CAREER: Neural mechanisms underlying optimal performance
批准号:
2238247
负责人:
Luca Mazzucato
金额:
$60.0万
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
项目状态:
未结题
起止时间:
2023-10-01 至 2028-09-30
中文摘要
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英文摘要
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.
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国内基金
海外基金
Neural Process模型的多样化高保真技术研究
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批准号:62306326
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项目类别:青年科学基金项目
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资助金额:30万元
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批准年份:2023
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负责人:王琦
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依托单位: