An Object Detecting Architecture using Spiking Neural Networks

Abstract

Spiking Neural Networks have opened new doors in the world of Neural Networks. This study implements and shows a viable architecture to detect and classify blob-like input data. An architecture consisting of three parts a region proposal network, weight calculations, and the classifier is discussed and implemented. The region proposal network is build based on a blob detecting Laplacian of Gaussian function. The architecture is tested and verified on the Multi MNIST dataset that is generated based on the MNIST dataset that consists of handwritten digits. Results show that, on average, the region proposal network can locate the blobs in the input with an accuracy of within a single pixel distance from the ground truth. Two different ways of decoding the rate data coming from the region proposal network where discussed the Peak based decoder could propose regions even if these regions are situated closely together. A Center of Mass decoder is slightly more accurate than the Peak based decoder but at a higher computational cost and performance degradation when the regions are close together. The region proposal network at worst only accounts for 3.19% of inaccuracy. The implementation shows that the architecture is a viable way of detecting and classifying multiple objects within the input. The data shows that the region proposal network itself is a feasible way of detecting blob-like objects within its input.