Deep neural networks (DNNs) transform stimuli across multiple processing
stages to produce representations that can be used to solve complex tasks, such
as object recognition in images. However, a full understanding of how they
achieve this remains elusive. The complexity of biological neural networks
substantially exceeds the complexity of DNNs, making it even more challenging
to understand the representations that they learn. Thus, both machine learning
and computational neuroscience are faced with a shared challenge: how can we
analyze their representations in order to understand how they solve complex
tasks?
We review how data-analysis concepts and techniques developed by
computational neuroscientists can be useful for analyzing representations in
DNNs, and in turn, how recently developed techniques for analysis of DNNs can
be useful for understanding representations in biological neural networks. We
explore opportunities for synergy between the two fields, such as the use of
DNNs as in-silico model systems for neuroscience, and how this synergy can lead
to new hypotheses about the operating principles of biological neural networks.

该研究综述了计算神经科学家开发的数据分析概念和技术如何有助于分析深度神经网络（DNN）中的表示，并探讨了近期发展的 DNN 分析技术如何有助于理解生物神经网络中的表示，为神经科学和机器学习领域提供了协同机会，例如使用 DNN 作为神经科学的模型系统，并探讨了这种协同怎样能够产生有关生物神经网络操作原理的新假设。

分析生物和人工神经网络：挑战与协同机会？

Analyzing biological and artificial neural networks: challenges with  opportunities for synergy?

Non-negative matrix factorization (NMF) is a recently developed technique for
finding parts-based, linear representations of non-negative data. Although it
has successfully been applied in several applications, it does not always
result in parts-based representations. In this paper, we show how explicitly
incorporating the notion of `sparseness' improves the found decompositions.
Additionally, we provide complete MATLAB code both for standard NMF and for our
extension. Our hope is that this will further the application of these methods
to solving novel data-analysis problems.

本文提出了一种基于非负矩阵分解 (NMF) 的线性表示方法，并引入了 “稀疏性” 概念以提高解分解的效果，并提供了用于标准 NMF 和该方法扩展的完整 MATLAB 代码，旨在进一步推动这些方法在解决新型数据分析问题方面的应用。