Title :
Self-synchronizing signal parsing with spiking feature-detection filters
Author :
Loeliger, H.-A. ; Neff, S. ; Reller, C.
fDate :
Sept. 30 2014-Oct. 3 2014
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;
Conference_Titel :
Communication, Control, and Computing (Allerton), 2014 52nd Annual Allerton Conference on
Conference_Location :
Monticello, IL
DOI :
10.1109/ALLERTON.2014.7028445