Population Step Forward Encoding Algorithm

Improving the signal encoding accuracy and efficiency of spike encoding algorithms

Master Thesis (2021)
Author(s)

L. de Gelder (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

R. van Leuken – Mentor (TU Delft - Signal Processing Systems)

Amir Zjajo – Graduation committee member

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Luuk de Gelder
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Luuk de Gelder
Graduation Date
08-02-2021
Awarding Institution
Delft University of Technology
Programme
['Electrical Engineering | Circuits and Systems']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major problem that can occur in a specific use-case is the limited bandwidth for the spikes that get generated through the encoding algorithm. In this thesis, we propose population Step Forward Encoding algorithm. This algorithm takes the signal encoding accuracy of Step Forward encoding algorithm and makes it into a population, generating multiple spike trains. This allows a higher threshold to encode a large part of the signal, increasing the efficiency. We show that population Step Forward Encoding algorithm doesn't just work good for the signal encoding accuracy, but also for the classification accuracy. Moreover, population Step Forward Encoding algorithm does not only have a high efficiency with a low spike count, it can also achieve higher efficiency with higher spike count. Thus, population Step Forward can make most use of a limited bandwidth of spikes.

Files

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