Kanwisher Nancy, Khosla Meenakshi, Dobs Katharina
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Psychology, Justus Liebig University Giessen, Giessen, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University, Giessen, Germany.
Trends Neurosci. 2023 Mar;46(3):240-254. doi: 10.1016/j.tins.2022.12.008. Epub 2023 Jan 17.
Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.
长期以来,神经科学家一直在描述神经系统的特性和功能,并且越来越成功地回答大脑是如何执行其任务的。但是,大脑为何以其现有的方式运作这个问题却较少被提及。如今,通过优化人工神经网络(ANN)以使其在类人任务中表现出色的新能力,我们能够通过询问为特定任务优化的网络属性何时反映执行相同任务的人类的行为和神经特征,来探讨这些“为何”的问题。在此,我们着重介绍了该策略最近在解释视觉和听觉系统在行为和神经层面为何以其现有的方式运作方面所取得的成功。