Deep learning of artificial neural networks (ANNs) is creating highly
functional processes that are, unfortunately, nearly as hard to interpret as
their biological counterparts. Identification of functional modules in natural
brains plays an important role in cognitive and neuroscience alike, and can be
carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or
calcium imaging. However, we do not have such robust methods at our disposal
when it comes to understanding functional modules in artificial neural
networks. Ideally, understanding which parts of an artificial neural network
perform what function might help us to address a number of vexing problems in
ANN research, such as catastrophic forgetting and overfitting. Furthermore,
revealing a network's modularity could improve our trust in them by making
these black boxes more transparent. Here, we introduce a new
information-theoretic concept that proves useful in understanding and analyzing
a network's functional modularity: the relay information $I_R$. The relay
information measures how much information groups of neurons that participate in
a particular function (modules) relay from inputs to outputs. Combined with a
greedy search algorithm, relay information can be used to identify
computational modules in neural networks. We also show that the functionality
of modules correlates with the amount of relay information they carry.

介绍了一种新信息理论概念 - 中继信息，该概念可用于理解和分析网络的功能模块化，并结合贪婪搜索算法用于识别神经网络中的计算模块，功能模块的功用与其所携带的中继信息量相关。

深度神经网络中的信息中继检测

Detecting Information Relays in Deep Neural Networks

Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.

本研究提出使用视觉中腹流的机制，构建计算模块以提取不变和有差别的音频表示，实现音频流派分类的无监督学习