Patrick Maier, James Rainey, Elena Gheorghiu, Kofi Appiah, Deepayan Bhowmik

Digit Classification using Biologically Plausible Neuromorphic Vision

ABSTRACT:
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.

KEYWORDS:
Neuromorphic Vision;
Digit Classification;
Spiking Neural Network;
Human Vision System