Researchers typically investigate neural network representations by examining
activation outputs for one or more layers of a network. Here, we investigate
the potential for ReLU activation patterns (encoded as bit vectors) to aid in
understanding and interpreting the behavior of neural networks. We utilize
Representational Dissimilarity Matrices (RDMs) to investigate the coherence of
data within the embedding spaces of a deep neural network. From each layer of a
network, we extract and utilize bit vectors to construct similarity scores
between images. From these similarity scores, we build a similarity matrix for
a collection of images drawn from 2 classes. We then apply Fiedler partitioning
to the associated Laplacian matrix to separate the classes. Our results
indicate, through bit vector representations, that the network continues to
refine class detectability with the last ReLU layer achieving better than 95\%
separation accuracy. Additionally, we demonstrate that bit vectors aid in
adversarial image detection, again achieving over 95\% accuracy in separating
adversarial and non-adversarial images using a simple classifier.

本文基于深度神经网络层次输出的矩阵，使用基于比特向量的表示方法，通过计算相似度分数矩阵建立图像集分离分类，并验证该方法对于神经网络研究及灵敏相似度分析的可行性。

基于海明相似度与图拉普拉斯算子进行类别划分及对抗性图像检测

Hamming Similarity and Graph Laplacians for Class Partitioning and  Adversarial Image Detection

Spiking Neural Networks (SNNs) have received considerable attention not only
for their superiority in energy efficient with discrete signal processing, but
also for their natural suitability to integrate multi-scale biological
plasticity. However, most SNNs directly adopt the structure of the
well-established DNN, rarely automatically design Neural Architecture Search
(NAS) for SNNs. The neural motifs topology, modular regional structure and
global cross-brain region connection of the human brain are the product of
natural evolution and can serve as a perfect reference for designing
brain-inspired SNN architecture. In this paper, we propose a Multi-Scale
Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously
considering micro-, meso- and macro-scale brain topologies as the evolutionary
search space. MSE-NAS evolves individual neuron operation, self-organized
integration of multiple circuit motifs, and global connectivity across motifs
through a brain-inspired indirect evaluation function, Representational
Dissimilarity Matrices (RDMs). This training-free fitness function could
greatly reduce computational consumption and NAS's time, and its
task-independent property enables the searched SNNs to exhibit excellent
transferbility and scalability. Extensive experiments demonstrate that the
proposed algorithm achieves state-of-the-art (SOTA) performance with shorter
simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic
datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also
illustrates the significant performance improvement and consistent
bio-interpretability deriving from the topological evolution at different
scales and the RDMs fitness function.

本论文提出了一种基于大脑多尺度拓扑结构的多尺度进化神经架构搜索算法 (MSE-NAS)，通过基于神经激发的间接评估函数 (表示不同矩阵 RDMs) 演化单个神经元操作，从而得到具有卓越可扩展性和迁移性的 SNN 架构。实验结果表明，该算法在静态数据集 (CIFAR10, CIFAR100) 和神经形态数据集 (CIFAR10-DVS 和 DVS128-Gesture) 上均取得了最新技术水平 (SOTA) 的性能表现，而不需要训练，同时不影响在不同尺度上的生物解释性。

深度脉冲神经网络的多尺度进化神经架构搜索

Multi-scale Evolutionary Neural Architecture Search for Deep Spiking  Neural Networks

High-resolution functional imaging is providing increasingly rich
measurements of brain activity in animals and humans. A major challenge is to
leverage such data to gain insight into the brain's computational mechanisms.
The first step is to define candidate brain-computational models (BCMs) that
can perform the behavioural task in question. We would then like to infer,
which of the candidate BCMs best accounts for measured brain-activity data.
Here we describe a method that complements each BCM by a measurement model
(MM), which simulates the way the brain-activity measurements reflect neuronal
activity (e.g. local averaging in fMRI voxels or sparse sampling in array
recordings). The resulting generative model (BCM-MM) produces simulated
measurements. In order to avoid having to fit the MM to predict each individual
measurement channel of the brain-activity data, we compare the measured and
predicted data at the level of summary statistics. We describe a novel
particular implementation of this approach, called probabilistic RSA (pRSA)
with measurement models, which uses representational dissimilarity matrices
(RDMs) as the summary statistics. We validate this method by simulations of
fMRI measurements (locally averaging voxels) based on a deep convolutional
neural network for visual object recognition. Results indicate that the way the
measurements sample the activity patterns strongly affects the apparent
representational dissimilarities. However, modelling of the measurement process
can account for these effects and different BCMs remain distinguishable even
under substantial noise. The pRSA method enables us to perform Bayesian
inference on the set of BCMs and to recognise the data-generating model in each
case.

本研究提出了使用测量模型对候选的大脑计算模型进行补充并建立生成模型。通过使用表示不相似矩阵作为总结统计数据，可在众多的大脑计算模型中执行贝叶斯推断。