Evaluating SPAN incremental learning for handwritten digit recognition

Authors:
Ammar Mohemmed;Guoyu Lu;Nikola Kasabov
Affiliations:
Knowledge Engineering Discovery Research Institute, Auckland University of Technology, New Zealand;Department of Information Engineering and Computer Science, University of Trento, Italy;Knowledge Engineering Discovery Research Institute, Auckland University of Technology, New Zealand,Institute for Neuroinformatics, ETH and University of Zurich, Switzerland
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

Citing 6
Cited 0

Networks of spiking neurons: the third generation of neural network models

Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Evolution of spiking neural circuits in autonomous mobile robots: Research Articles

International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

Neural Computation
Towards Spike-Based Speech Processing: A Biologically Plausible Approach to Simple Acoustic Classification

International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
Evolving spiking neural networks for audiovisual information processing

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In a previous work [12, 11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications.