DocumentCode :
1682476
Title :
Learning of sparse auditory receptive fields
Author :
Körding, Konrad P. ; König, Peter ; Klein, David J.
Author_Institution :
Inst. of Neuroinformatics, ETH/UNI Zurich, Switzerland
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1103
Lastpage :
1108
Abstract :
It is largely unknown how the properties of the auditory system relate to the properties of natural sounds. Here, we analyze representations of simulated neurons that have optimally sparse activity in response to spectro-temporal speech data. These representations share important properties with the auditory neurons determined in electrophysiological experiments
Keywords :
hearing; learning (artificial intelligence); neural nets; neurophysiology; auditory neurons; auditory receptive fields; auditory system; learning; simulated neurons; sparse activity; spectral temporal speech data; Analytical models; Auditory system; Frequency; Layout; Mathematical model; Neurons; Principal component analysis; Spectrogram; Speech analysis; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1007648
Filename :
1007648
Link To Document :
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