Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning

Abstract

With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks remains limited, mainly focusing on simple tasks such as digit recognition. It remains hard to deal with more complex tasks (e.g. segmentation, object detection) due to the small number of works on deep spiking neural networks for these tasks. The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. We propose a network based on DECOLLE, a spiking model that enables local surrogate gradient-based learning. The encouraging results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach for more elaborate vision tasks in the future.

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Deep Learning in Spiking Neural Networks
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
• Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)