Optimization of output spike train encoding for a spiking neuron based on its spatiotemporal input pattern

Taherkhani, A ORCID logoORCID: https://orcid.org/0000-0002-3627-6362, Cosma, G ORCID logoORCID: https://orcid.org/0000-0002-4663-6907 and McGinnity, TM ORCID logoORCID: https://orcid.org/0000-0002-9897-4748, 2019. Optimization of output spike train encoding for a spiking neuron based on its spatiotemporal input pattern. IEEE Transactions on Cognitive and Developmental Systems. ISSN 2379-8920

[thumbnail of 13723_a1459_Cosma.pdf]
Preview
Text
13723_a1459_Cosma.pdf - Post-print

Download (1MB) | Preview

Abstract

A common learning task for a spiking neuron is to map a spatiotemporal input pattern to a target output spike train. There is no prescribed method for selection of the target output spike train. However, the precise spiking pattern of the target output spike train (output encoding) can affect the learning performance of the spiking neuron. Therefore, systematic methods of finding the optimum spiking pattern for a target output spike train that can be learned by spiking neurons are needed. Here, a method is proposed to adaptively adjust an initial sub-optimal output encoding during different learning epochs to find the optimal output encoding. A time varying value of a local event called a spike trace is used to calculate the amount of a required adjustment. The Remote Supervised Method (ReSuMe) learning algorithm is used to train the weights, and the proposed method is used for finding optimized output encoding (optimized desired spikes). Experimental results show that optimizing the output encoding during the learning phase increases the accuracy. The proposed method was applied to find optimized output encoding in classification tasks and the results revealed improvements up to 16.5% in accuracy compared to when using the non-adapted method. It also increases the accuracy in a classification task from 90% to 100%.

Item Type: Journal article
Publication Title: IEEE Transactions on Cognitive and Developmental Systems
Creators: Taherkhani, A., Cosma, G. and McGinnity, T.M.
Publisher: IEEE
Date: 11 April 2019
ISSN: 2379-8920
Identifiers:
Number
Type
10.1109/TCDS.2019.2909355
DOI
Divisions: Schools > School of Social Sciences
Record created by: Jill Tomkinson
Date Added: 08 Apr 2019 11:13
Last Modified: 09 Sep 2019 10:29
URI: https://irep.ntu.ac.uk/id/eprint/36206

Actions (login required)

Edit View Edit View

Statistics

Views

Views per month over past year

Downloads

Downloads per month over past year