DocumentCode :
395156
Title :
An competitive learning pulsed neural network for temporal signals
Author :
Kurojanagi, S. ; Iwata, Akira
Author_Institution :
Dept. of Electr. & Comput. Eng., Nagoya Inst. of Technol., Japan
Volume :
1
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
348
Abstract :
In this study, we propose a new competitive learning method for temporal signals using pulsed neuron model. The pulsed neuron models deal with pulse trains as the inputs and outputs, and employ leaky integrators as there internal potentials. Therefore, the models can deal with temporal signals without the windowing process. The proposed method is based on a winner selection method controlling the firing threshold of competitive neurons using a few observer neurons. By employing this method, the winner neuron switches dynamically according to variation of input signals. As a result of the experiment, it become clear that the temporal input signals generated from a real sound could be quantized and the reference vector changes according to variation of input signals.
Keywords :
neural nets; signal processing; unsupervised learning; Kohonen algorithm; competitive learning; competitive neural network; competitive neurons; firing threshold; pulsed neuron model; temporal signals; winner neuron switches; Biomembranes; Image converters; Learning systems; Neural networks; Neurons; Pulse generation; Signal generators; Signal processing; Signal processing algorithms; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1202191
Filename :
1202191
Link To Document :
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