Kriegeskorte Nikolaus, Douglas Pamela K
Department of Psychology, Department of Neuroscience, Department of Electrical Engineering, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA.
Nat Neurosci. 2018 Sep;21(9):1148-1160. doi: 10.1038/s41593-018-0210-5. Epub 2018 Aug 20.
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. Here we review recent work in the intersection of cognitive science, computational neuroscience and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive and control tasks are beginning to be developed and tested with brain and behavioral data.
为了了解认知在大脑中是如何实现的，我们必须构建能够执行认知任务的计算模型，并用大脑和行为实验来测试这些模型。认知科学已经开发出将认知分解为功能组件的计算模型。计算神经科学已经对相互作用的神经元如何实现认知的基本组件进行了建模。现在是时候把大脑计算难题的各个部分拼凑起来，并更好地整合这些不同的学科了。现代技术使我们能够以前所未有的丰富方式在动物和人类身上测量和操纵大脑活动。然而，只有当实验被用于测试大脑计算模型时，才能产生理论洞察力。在这里，我们回顾了认知科学、计算神经科学和人工智能交叉领域的最新研究。模拟感知、认知和控制任务期间大脑信息处理的计算模型开始被开发出来，并用大脑和行为数据进行测试。
