Neural networks (NNs), both living and artificial, work due to being complex
systems of neurons, each having its own specialization. Revealing these
specializations is important for understanding NNs inner working mechanisms.
The only way to do this for a living system, the neural response of which to a
stimulus is not a known (let alone differentiable) function is to build a
feedback loop of exposing it to stimuli, the properties of which can be
iteratively varied aiming in the direction of maximal response. To test such a
loop on a living network, one should first learn how to run it quickly and
efficiently, reaching most effective stimuli (ones that maximize certain
neurons activation) in least possible number of iterations. We present a
framework with an effective design of such a loop, successfully testing it on
an artificial spiking neural network (SNN, a model that mimics the behaviour of
NNs in living brains). Our optimization method used for activation maximization
(AM) was based on low-rank tensor decomposition (Tensor Train, TT) of the
activation function's discretization over its domain the latent parameter space
of stimuli (CIFAR10-size color images, generated by either VQ-VAE or SN-GAN
from their latent description vectors, fed to the SNN). To our knowledge, the
present work is the first attempt to perform effective AM for SNNs. The source
code of our framework, MANGO (for Maximization of neural Activation via
Non-Gradient Optimization) is available on GitHub.

通过构建一个反馈循环并利用低秩张量分解技术，我们成功设计了一个快速高效的优化方法，用于激活最大化的神经网络，并在人工脉冲神经网络上成功测试。

脉冲神经网络中神经元的快速无梯度激活最大化

Fast gradient-free activation maximization for neurons in spiking neural  networks

Automated counting of people in crowd images is a challenging task. The major
difficulty stems from the large diversity in the way people appear in crowds.
In fact, features available for crowd discrimination largely depend on the
crowd density to the extent that people are only seen as blobs in a highly
dense scene. We tackle this problem with a growing CNN which can progressively
increase its capacity to account for the wide variability seen in crowd scenes.
Our model starts from a base CNN density regressor, which is trained in
equivalence on all types of crowd images. In order to adapt with the huge
diversity, we create two child regressors which are exact copies of the base
CNN. A differential training procedure divides the dataset into two clusters
and fine-tunes the child networks on their respective specialties.
Consequently, without any hand-crafted criteria for forming specialties, the
child regressors become experts on certain types of crowds. The child networks
are again split recursively, creating two experts at every division. This
hierarchical training leads to a CNN tree, where the child regressors are more
fine experts than any of their parents. The leaf nodes are taken as the final
experts and a classifier network is then trained to predict the correct
specialty for a given test image patch. The proposed model achieves higher
count accuracy on major crowd datasets. Further, we analyse the characteristics
of specialties mined automatically by our method.

通过使用递增的 CNN，根据由于人们出现在拥挤场景中的多样性的程度来自适应地进行人流计数，分类器网络能够准确地预测所述拥挤景象的特异性，并在主要人群数据集上实现更高的计数准确性。