标题： NIPS 2016 人工与生物神经网络表征学习研讨会 (MLINI 2016) 显示英文标题

标题： NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

摘要： 本研讨会探讨了认知神经科学与旨在再现人类表现的最新人工智能领域（如自然语言处理和计算机视觉）之间的接口，并特别关注解决这些问题的深度学习方法。 当研究大脑的认知能力时，科学家采用系统识别的方法，即向受试者呈现不同的刺激，并尝试建立不同脑区对该刺激的响应模型。 目标是通过寻找能够用刺激的不同属性表达脑区活动的功能来理解大脑。 实验刺激正变得越来越复杂，越来越多的研究人员对研究现实生活现象（如自然图像感知或自然句子理解）感兴趣。 因此，我们需要一种丰富且适当的向量表示方法来描述刺激的特性，这可以通过机器学习的进步获得。 同时，新的机器学习方法（许多属于深度学习）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，一些过程（如注意力机制）也受到人类行为的启发。 然而，这些方法中的大多数与关于脑功能的研究发现无关，尚不清楚机器学习是否有必要尝试模拟脑功能以完成大脑实现的任务。 显示英文摘要

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.