Biological neurons communicate with a sparing exchange of pulses - spikes. It
is an open question how real spiking neurons produce the kind of powerful
neural computation that is possible with deep artificial neural networks, using
only so very few spikes to communicate. Building on recent insights in
neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on
adaptive spiking neurons. These spiking neurons efficiently encode information
in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while
homeostatically optimizing their firing rate. In the proposed paradigm of
spiking neuron computation, neural adaptation is tightly coupled to synaptic
plasticity, to ensure that downstream neurons can correctly decode upstream
spiking neurons. We show that this type of network is inherently able to carry
out asynchronous and event-driven neural computation, while performing
identical to corresponding artificial neural networks (ANNs). In particular, we
show that these adaptive spiking neurons can be drop in replacements for ReLU
neurons in standard feedforward ANNs comprised of such units. We demonstrate
that this can also be successfully applied to a ReLU based deep convolutional
neural network for classifying the MNIST dataset. The ASNN thus outperforms
current Spiking Neural Networks (SNNs) implementations, while responding (up
to) an order of magnitude faster and using an order of magnitude fewer spikes.
Additionally, in a streaming setting where frames are continuously classified,
we show that the ASNN requires substantially fewer network updates as compared
to the corresponding ANN.

本文提出了一种基于适应性尖峰神经元的调整尖峰神经网络（ASNN），使用形式化的异步脉冲 Sigma-Delta 编码有效地对脉冲列进行信息编码，同时通过调节发放率进行神经元适应性，紧密耦合了神经适应性与突触可塑性，用尖峰神经元替换标准前馈人工神经网络中的 ReLU 神经元，成功地应用于分类 MNIST 数据集的深度卷积神经网络。