DocumentCode
252979
Title
Self-synchronizing signal parsing with spiking feature-detection filters
Author
Loeliger, H.-A. ; Neff, S. ; Reller, C.
fYear
2014
fDate
Sept. 30 2014-Oct. 3 2014
Firstpage
123
Lastpage
128
Abstract
Following an earlier suggestion, the concept of a hierarchical network of feature-detection filters is developed. The individual filters are derived from a localized least-squares approach based on non-generative state space models, which results in simple forward-only recursions for the actual computations. It is demonstrated that such filters can naturally cope with spiking signals, and the use of spiking signals in such networks is advocated. The feasibility of the approach is demonstrated with a four-layer network that understands Morse code.
Keywords
filtering theory; least squares approximations; signal processing; localized least-squares approach; self-synchronizing signal parsing; spiking feature detection filters; spiking signals; state space models; Biological neural networks; Computational modeling; Feature extraction; Hilbert space; Proposals; Robustness; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Communication, Control, and Computing (Allerton), 2014 52nd Annual Allerton Conference on
Conference_Location
Monticello, IL
Type
conf
DOI
10.1109/ALLERTON.2014.7028445
Filename
7028445
Link To Document