Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.

中文翻译：

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用计算模型照亮树突功能。

树枝状晶体一直吸引着研究人员：从拉蒙·卡哈尔（Ramon y Cajal）的艺术作品到当今精美的唱片，神经科学家一直在努力揭示这些结构的奥秘。1960年代的理论工作预言了树突对神经元加工的重要影响，建立了计算模型，将其作为研究的有力技术。从那时起，树突的建模一直以有针对性的方式帮助推动神经科学研究，提供了从亚细胞水平到系统水平的可实验测试的预测，并且它们的相关性扩展到了神经科学以外的领域，例如机器学习和人工智能。验证模型预测通常需要并推动新技术的进步，因此，通过理论驱动的实验使该领域向前发展，从而封闭了这一循环。对于我们的理解，本综述的重点是树突计算建模（包括那些尚待进行的实验验证）的最重要贡献，并重点介绍了建模与实验神经科学界之间成功相互作用的研究。