DocumentCode :
3069598
Title :
Learning algorithm for pulse coupled neural network
Author :
Calvin, Priscilla ; Yuen, G. ; Boddruzzaman, M. ; Malkani, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tennessee State Univ., Nashville, TN, USA
fYear :
1998
fDate :
8-10 Mar 1998
Firstpage :
407
Lastpage :
411
Abstract :
Living neurons act as leaky integrators in that they store on their surface signals applied to them in previous fractions of a second. For large pyramidal neurons in the cortex the time constant of the cell membrane may be higher than had previously been thought and the temporal properties of the neurons need to be reassessed in this light. In any case, time constants of tens, or even hundreds of milliseconds lead to the storage of ongoing activity in a way that may be crucial in determining the possible modes of information processing. We propose a system in which the sequence is stored directly in a temporal manner. The system also leads to recall in which there is no distortion of the timing of the temporal patterns
Keywords :
pattern classification; self-organising feature maps; time series; unsupervised learning; cell membrane; leaky integrators; learning algorithm; living neurons; pulse coupled neural network; pyramidal neurons; recall; temporal patterns; Biological system modeling; Biomembranes; Equations; Fires; Image recognition; Neural engineering; Neural networks; Neurons; Target recognition; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on
Conference_Location :
Morgantown, WV
ISSN :
0094-2898
Print_ISBN :
0-7803-4547-9
Type :
conf
DOI :
10.1109/SSST.1998.660106
Filename :
660106
Link To Document :
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