Population Step Forward Encoding Algorithm

Population Step Forward Encoding Algorithm: Improving the signal encoding accuracy and efficiency of spike encoding algorithms

de Gelder, Luuk (TU Delft Electrical Engineering, Mathematics and Computer Science)

van Leuken, T.G.R.M. (mentor)
Zjajo, Amir (graduation committee)

Delft University of Technology

Electrical Engineering | Circuits and Systems

2021-02-08

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.

Spiking Neural Networks(SNNs))
Spiking Neural Networks
spiking neural networks
Encoding Algorithms
Temporal Encoding Algorithms
temporal data
Encoding
Algorithm
Algorithm Configuration
Population Step Forward Encoding

http://resolver.tudelft.nl/uuid:0191762e-fed2-467e-9038-858c96dcefc4

2023-02-08

Student theses

master thesis

© 2021 Luuk de Gelder