解码大脑:从神经表征到机理模型

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-10-17 DOI:10.1016/j.cell.2024.08.051
Mackenzie Weygandt Mathis, Adriana Perez Rotondo, Edward F. Chang, Andreas S. Tolias, Alexander Mathis
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引用次数: 0

摘要

神经科学的一个核心原理是,大脑内的神经元协同作用,产生感知、认知和适应行为。神经元被组织成专门的脑区,在不同程度上致力于不同的功能,它们的功能依赖于分布式电路,以持续编码相关的环境和身体状态特征,使其他区域能够解码(解释)这些表征,以计算有意义的决策和执行精确的动作。因此,分布式大脑可被视为一系列对信息进行编码和解码的计算。在这一视角中,我们详细介绍了神经编码和解码的重要概念,并强调了用于测量这些概念的数学工具,包括深度学习方法。我们提供了一些案例研究,其中解码概念促成了运动、视觉和语言处理方面的基础科学和转化科学。
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Decoding the brain: From neural representations to mechanistic models
A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing.
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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