Image and Audio Data Classification Using Bagging Ensembles of Spiking Neural Networks with Memristive Plasticity

Spiking neural networks (SNNs) are potentially capable of greatly reducing the energy requirements of modern intelligent systems when combined with neuromorphic computing devices based on memristors, that facilitate on-chip SNN training. Currently, the existing spiking approaches either rely on weight transfer and/or backpropagation-based training or utilize large fully-connected spiking networks, imposing high hardware requirements. In this paper, we study the application of the bagging ensembling technique coupled with SNN-based models to the audio and image classification problems. In our experiments, we use a three-layer spiking neural network with Logistic Regression decoding and consider three local plasticity rules—spike time-dependent plasticity and its nanocomposite and poly-p-xylylene memristor counterparts. Using the Digits and FSDD datasets for training and evaluation, we show that bagging yields a performance increase of up to 20% in terms of the F1-score metric, while substantially reducing the total number of connections in the network.