DocumentCode :
1951133
Title :
Theories of Neural Networks Leading to Unsupervised Learning
Author :
Szu, Harold
Author_Institution :
Fellows of AIMBE, IEEE, OSA, SPIE; ONR & GWU, szuh@onr.navy.mil; szuh@gwu.edu
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
3116
Lastpage :
3123
Abstract :
In this paper, we derive an exact single-pixel BSS solution for two components. Furthermore, we prove the solution for n components to be unique and stable by means of the augmented Lagrange or Karush, Kuhn and Tucker methodology [S 07]. Our constant-temperature free energy can estimate the neuronal population of brain´s grey matter which is responsible for the consciousness activities identified by Crick & Koch as the Claustrum accomplishing binding among firing rates (similar to C-node tuning in the beginning of an orchestra performance). Furthermore, the retinal neuronal response Mexican hat functions could be explained by finite resource sharing for replenishment.
Keywords :
autoregressive processes; blind source separation; independent component analysis; learning (artificial intelligence); neural nets; time series; Lagrange parameter; Wiener auto-regression; artificial neural network; blind sources separation; brain neural net; independent component analysis; unsupervised learning; vector time series; Animals; Artificial neural networks; Biological neural networks; Blood; Eyes; Independent component analysis; Intelligent sensors; Neural networks; Temperature sensors; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371458
Filename :
4371458
Link To Document :
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