论文标题
在人工神经网络上测试系统神经科学的工具
Testing the Tools of Systems Neuroscience on Artificial Neural Networks
论文作者
论文摘要
神经科学家将一系列常见分析工具应用于记录的神经活动,以了解神经回路如何实施计算。尽管这些工具塑造了整个领域的进步,但我们几乎没有经验证据表明它们有效地快速识别了感兴趣的现象。在这里,我认为应该明确测试这些工具,并且人工神经网络(ANN)是它们的适当测试场所。最近将ANN作为从感知到记忆到运动控制的所有事物的模型的近期复兴源于人工和生物神经网络之间的粗略相似性以及训练这些网络以执行复杂的高维任务的能力。这些特性,结合了完美观察和操纵这些系统的能力,非常适合审查系统和认知神经科学的工具。我在这里提供了用于执行此测试的路线图和适合在ANN上进行测试的工具列表。利用ANN来反思这些工具在多大程度上提供对神经系统的有效理解,以及在这里的理解应确切理解 - 有可能加快大脑研究的进展。
Neuroscientists apply a range of common analysis tools to recorded neural activity in order to glean insights into how neural circuits implement computations. Despite the fact that these tools shape the progress of the field as a whole, we have little empirical evidence that they are effective at quickly identifying the phenomena of interest. Here I argue that these tools should be explicitly tested and that artificial neural networks (ANNs) are an appropriate testing grounds for them. The recent resurgence of the use of ANNs as models of everything from perception to memory to motor control stems from a rough similarity between artificial and biological neural networks and the ability to train these networks to perform complex high-dimensional tasks. These properties, combined with the ability to perfectly observe and manipulate these systems, makes them well-suited for vetting the tools of systems and cognitive neuroscience. I provide here both a roadmap for performing this testing and a list of tools that are suitable to be tested on ANNs. Using ANNs to reflect on the extent to which these tools provide a productive understanding of neural systems -- and on exactly what understanding should mean here -- has the potential to expedite progress in the study of the brain.