Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
Nat Rev Neurosci. 2020 Jun;21(6):303-321. doi: 10.1038/s41583-020-0301-7. Epub 2020 May 11.
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.
从拉蒙·卡哈尔（Ramon y Cajal）的艺术绘图，到当今的精美记录，神经科学家一直在努力揭开这些结构的奥秘。20 世纪 60 年代的理论工作预测了树突对神经元处理的重要影响，确立了计算建模作为研究这些影响的强大技术。从那时起，树突建模对于以有针对性的方式推动神经科学研究起到了重要作用，提供了从亚细胞水平到系统水平的可在实验中验证的预测，其应用范围超出了神经科学领域，如机器学习和人工智能。建模预测的验证通常需要（并推动）新的技术进步，从而与理论驱动的实验形成闭环，推动该领域的发展。本篇综述重点介绍了对树突计算建模最重要的贡献（包括那些有待实验验证的贡献），并强调了建模和实验神经科学社区之间成功互动的研究。
