NIPS 2016人工和生物神经网络表示学习研讨会（MLINI 2016）
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
摘要：
本次研讨会探讨了认知神经科学与人工智能领域最新进展之间的接口，这些领域旨在再现人类的表现，如自然语言处理和计算机视觉，特别是解决此类问题的深度学习方法。在研究大脑的认知能力时，科学家们遵循一种系统识别方法，在这种方法中，他们向受试者提供不同的刺激，并试图模拟不同大脑区域对该刺激的反应。我们的目标是通过试图找到表达大脑区域活动的功能来理解大脑，这些功能是根据刺激的不同属性来表达的。随着越来越多的人对研究现实生活中的现象，如对自然图像或自然句子的感知感兴趣，实验刺激变得越来越复杂。因此，我们需要一个丰富和充分的向量表示的刺激的属性，我们可以获得使用机器学习的进步。与此同时，新的机器学习方法，其中许多在深度学习中，在一定程度上受到人类行为或生物学原理的启发。例如，神经网络最初受到生物神经元的启发。最近，注意力等过程受到人类行为的启发而被使用。然而，这些方法中的大部分都与大脑功能的发现无关，目前还不清楚机器学习试图模仿大脑功能以实现与大脑相同的任务是否有益。
英文摘要：
This workshop explores the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance such as natural language processing and computer vision, and specifically deep learning approaches to such problems. When studying the cognitive capabilities of the brain, scientists follow a system identification approach in which they present different stimuli to the subjects and try to model the response that different brain areas have of that stimulus. The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus. Experimental stimuli are becoming increasingly complex with more and more people being interested in studying real life phenomena such as the perception of natural images or natural sentences. There is therefore a need for a rich and adequate vector representation of the properties of the stimulus, that we can obtain using advances in machine learning. In parallel, new ML approaches, many of which in deep learning, are inspired to a certain extent by human behavior or biological principles. Neural networks for example were originally inspired by biological neurons. More recently, processes such as attention are being used which have are inspired by human behavior. However, the large bulk of these methods are independent of findings about brain function, and it is unclear whether it is at all beneficial for machine learning to try to emulate brain function in order to achieve the same tasks that the brain achieves.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
