In the present paper, we consider one-hidden layer ANNs with a feedforward
architecture, also referred to as shallow or two-layer networks, so that the
structure is determined by the number and types of neurons. The determination
of the parameters that define the function, called training, is done via the
resolution of the approximation problem, so by imposing the interpolation
through a set of specific nodes. We present the case where the parameters are
trained using a procedure that is referred to as Extreme Learning Machine (ELM)
that leads to a linear interpolation problem. In such hypotheses, the existence
of an ANN interpolating function is guaranteed. The focus is then on the
accuracy of the interpolation outside of the given sampling interpolation nodes
when they are the equispaced, the Chebychev, and the randomly selected ones.
The study is motivated by the well-known bell-shaped Runge example, which makes
it clear that the construction of a global interpolating polynomial is accurate
only if trained on suitably chosen nodes, ad example the Chebychev ones. In
order to evaluate the behavior when growing the number of interpolation nodes,
we raise the number of neurons in our network and compare it with the
interpolating polynomial. We test using Runge's function and other well-known
examples with different regularities. As expected, the accuracy of the
approximation with a global polynomial increases only if the Chebychev nodes
are considered. Instead, the error for the ANN interpolating function always
decays and in most cases we observe that the convergence follows what is
observed in the polynomial case on Chebychev nodes, despite the set of nodes
used for training.

利用极限学习机来训练具有一层隐藏层的前馈神经网络（也称为浅层或两层网络），研究插值问题中的准确性以及使用 Chebychev 节点时的全局多项式逼近与 ANN 插值函数的异常行为。

关于基于单层人工神经网络的插值准确性研究

On the accuracy of interpolation based on single-layer artificial neural  networks

Recurrent networks of spiking neurons (RSNNs) underlie the astounding
computing and learning capabilities of the brain. But computing and learning
capabilities of RSNN models have remained poor, at least in comparison with
artificial neural networks (ANNs). We address two possible reasons for that.
One is that RSNNs in the brain are not randomly connected or designed according
to simple rules, and they do not start learning as a tabula rasa network.
Rather, RSNNs in the brain were optimized for their tasks through evolution,
development, and prior experience. Details of these optimization processes are
largely unknown. But their functional contribution can be approximated through
powerful optimization methods, such as backpropagation through time (BPTT).
A second major mismatch between RSNNs in the brain and models is that the
latter only show a small fraction of the dynamics of neurons and synapses in
the brain. We include neurons in our RSNN model that reproduce one prominent
dynamical process of biological neurons that takes place at the behaviourally
relevant time scale of seconds: neuronal adaptation. We denote these networks
as LSNNs because of their Long short-term memory. The inclusion of adapting
neurons drastically increases the computing and learning capability of RSNNs if
they are trained and configured by deep learning (BPTT combined with a rewiring
algorithm that optimizes the network architecture). In fact, the computational
performance of these RSNNs approaches for the first time that of LSTM networks.
In addition RSNNs with adapting neurons can acquire abstract knowledge from
prior learning in a Learning-to-Learn (L2L) scheme, and transfer that knowledge
in order to learn new but related tasks from very few examples. We demonstrate
this for supervised learning and reinforcement learning.

研究了如何通过深度学习将适应性神经元包含在递归脉冲神经元网络（RSNNs）中，从而优化网络结构，实现知识迁移和在少数示例中进行学习、手段是 BPTT 算法。

脉冲神经元网络中的长短时记忆和学习 - to-learn

Long short-term memory and learning-to-learn in networks of spiking  neurons

We describe an end-to-end trainable model for image compression based on
variational autoencoders. The model incorporates a hyperprior to effectively
capture spatial dependencies in the latent representation. This hyperprior
relates to side information, a concept universal to virtually all modern image
codecs, but largely unexplored in image compression using artificial neural
networks (ANNs). Unlike existing autoencoder compression methods, our model
trains a complex prior jointly with the underlying autoencoder. We demonstrate
that this model leads to state-of-the-art image compression when measuring
visual quality using the popular MS-SSIM index, and yields rate-distortion
performance surpassing published ANN-based methods when evaluated using a more
traditional metric based on squared error (PSNR). Furthermore, we provide a
qualitative comparison of models trained for different distortion metrics.

本研究论文提出并验证了一种使用基于变分自编码器的可端到端训练模型的图像压缩方法，其中使用了超先验来有效地捕获潜在表示中的空间依赖关系，证明该模型在使用 MS-SSIM 指标度量视觉质量时具有最先进的图像压缩性能，并提供了不同失真度指标的不同训练模型之间的定性比较。