Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA.
Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
Neuron. 2021 May 5;109(9):1433-1448. doi: 10.1016/j.neuron.2021.02.019. Epub 2021 Mar 8.
Over the past few decades, neuroscience experiments have become increasingly complex and naturalistic. Experimental design has in turn become more challenging, as experiments must conform to an ever-increasing diversity of design constraints. In this article, we demonstrate how this design process can be greatly assisted using an optimization tool known as mixed-integer linear programming (MILP). MILP provides a rich framework for incorporating many types of real-world design constraints into a neuroscience experiment. We introduce the mathematical foundations of MILP, compare MILP to other experimental design techniques, and provide four case studies of how MILP can be used to solve complex experimental design challenges.
在过去的几十年中，神经科学实验变得越来越复杂和自然化。实验设计也变得更加具有挑战性，因为实验必须符合越来越多的不同设计约束。在本文中，我们展示了如何使用一种称为混合整数线性规划（MILP）的优化工具来大大辅助这个设计过程。MILP 为将许多类型的现实世界设计约束纳入神经科学实验提供了一个丰富的框架。我们介绍了 MILP 的数学基础，将 MILP 与其他实验设计技术进行了比较，并提供了 MILP 如何用于解决复杂实验设计挑战的四个案例研究。
