Cascade Spiking Neuron Network For Event-based Image Classification In
Noisy Environment
Abstract
Spiking Neuron Network (SNN) has shown advantages in processing
event-based data for image classification. However, the classification
accuracy of SNNs decreases in noisy environment. The cascade spiking
neuron network (cascade-SNN) was proposed to solve this problem in this
letter. We used spiking convolutional spiking neuron network (SCNN) for
features extraction and liquid state machine (LSM) for read out.
Compared with early works on ANNs, this network achieved the
state-of-the-art classification accuracy in DVS-CIFAR10 dataset and
DVS-Gesture dataset, which are both challenging dataset because of noisy
environment. We conducted ablation experiments to verify the proposed
structure is effective and analyzed the influence of different
hyper-parameters.