Digit classification using biologically plausible neuromorphic vision

Despite tremendous advancement in computer vision, especially with deep learning, understanding scenes in the wild remains challenging. Even modern image classification models often misclassify when presented with out-of-distribution inputs despite having been trained on tens of millions of images or more. Moreover, training modern deep-learning classifiers requires a lot of energy due to the need to iterate many times over the training set, constantly updating billions of model parameters. Owing to problems with generalisability and robustness as well as efficiency, there is growing interest in computer vision to mimic biological vision (e.g., human vision) in the hope that doing so will require fewer resources for training both in terms of energy and in terms of data sets while increasing robustness and generalisability. This paper proposes a biologically plausible neuromorphic vision system that is based on a spiking neural network and is evaluated on the classification of hand-written digits from the MNIST dataset. The experimental outcome indicates improved robustness of the proposed approach over state-of-the-art considering non-digit detection.