Artificial Spiking Neural Networks (ASNNs) promise greater information
processing efficiency because of discrete event-based (i.e., spike)
computation. Several Machine Learning (ML) applications use biologically
inspired plasticity mechanisms as unsupervised learning techniques to increase
the robustness of ASNNs while preserving efficiency. Spike Time Dependent
Plasticity (STDP) and Intrinsic Plasticity (IP) (i.e., dynamic spiking
threshold adaptation) are two such mechanisms that have been combined to form
an ensemble learning method. However, it is not clear how this ensemble
learning should be regulated based on spiking activity. Moreover, previous
studies have attempted threshold based synaptic pruning following STDP, to
increase inference efficiency at the cost of performance in ASNNs. However,
this type of structural adaptation, that employs individual weight mechanisms,
does not consider spiking activity for pruning which is a better representation
of input stimuli. We envisaged that plasticity-based spike-regulation and
spike-based pruning will result in ASSNs that perform better in low resource
situations. In this paper, a novel ensemble learning method based on entropy
and network activation is introduced, which is amalgamated with a spike-rate
neuron pruning technique, operated exclusively using spiking activity. Two
electroencephalography (EEG) datasets are used as the input for classification
experiments with a three-layer feed forward ASNN trained using one-pass
learning. During the learning process, we observed neurons assembling into a
hierarchy of clusters based on spiking rate. It was discovered that pruning
lower spike-rate neuron clusters resulted in increased generalization or a
predictable decline in performance.

介绍了一种新的基于熵和网络激活的集成学习方法，该方法与仅使用尖峰活动操作的尖峰率神经元修剪技术相结合，用于处理两个脑电图数据集进行分类实验，结果表明这种修剪低尖峰率神经元群集导致更好的泛化能力。