Abstract
There is much excitement surrounding the idea of using spiking neural networks (SNNs) as the next generation of function-approximating neural networks. However, with the unique mechanism of communication (neural spikes) between neurons comes the challenge of transferring real-world data into the network to process. Many different encoding methods have been developed for SNNs, most temporal and some spatial. This paper analyzes three of them (Poisson rate encoding, Gaussian receptor fields, and a dual-neuron n-bit representation) and tests to see if the information is fully transformed into the spiking patterns. an oft-neglected consideration in encoding for SNNs is whether or not the real-world data is even truly being introduced to the network. by testing the reversibility of the encoding methods in this paper, the completeness of the information's presence in the pattern of spikes to serve as an input to an SNN is determined. © 2011 IEEE.
Recommended Citation
C. Johnson et al., "A Reversibility Analysis of Encoding Methods for Spiking Neural Networks," Proceedings of the International Joint Conference on Neural Networks, pp. 1802 - 1809, article no. 6033443, Institute of Electrical and Electronics Engineers, Oct 2011.
The definitive version is available at https://doi.org/10.1109/IJCNN.2011.6033443
Department(s)
Mining Engineering
Second Department
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-145771086-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
24 Oct 2011
