Understanding the neural basis of natural intelligence

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.2024.07.049

Angelo Forli, Michael M. Yartsev

引用次数: 0

Abstract

Understanding the neural basis of natural intelligence necessitates a paradigm shift: from strict reductionism toward embracing complexity and diversity. New tools and theories enable us to tackle this challenge, providing unprecedented access to neural dynamics and behavior across time, contexts, and species. Principles for intelligent behavior and learning in the natural world are now, more than ever, within reach.

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了解自然智能的神经基础

要了解自然智能的神经基础，就必须进行范式转变：从严格的还原论转向接受复杂性和多样性。新的工具和理论使我们能够应对这一挑战，为我们提供了前所未有的跨时间、跨环境和跨物种的神经动态和行为。自然界中的智能行为和学习原理现在比以往任何时候都更加触手可及。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere.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.