DocumentCode :
2008847
Title :
Biological Plausibility in Artificial Neural Networks: An Improvement on Earlier Models
Author :
Silva, Alberione Braz da ; Rosa, João Luís Garcia
Author_Institution :
Dept. of Syst., Intermedica Health Care S. A., Sao Paulo, Brazil
fYear :
2008
fDate :
11-13 Dec. 2008
Firstpage :
829
Lastpage :
834
Abstract :
Biological plausibility is a fact today to artificial neural network (ANN) community. Since researchers have not come to an agreement about this feature yet, they develop their own visions. Two of these are highlighted here: one is related directly to the cerebral cortex biological structure, and the other focuses the neural features and the signaling between neurons. This proposal departs from these approaches, considering that a biologically plausible ANN aims to create a more faithful model concerning the biological structure, properties, and functionalities of the cerebral cortex, not disregarding its computational efficiency. The choice of the models to be considered takes into account two main criteria: the fact they are considered biologically more realistic and the fact they deal with intra and interneuron signaling in electrical and chemical synapses. In addition to the features for encoding information regarding biological plausibility present in current models an alternative one is emphasized here: the timing of action potentials.
Keywords :
brain; neural nets; neurophysiology; artificial neural networks; biological plausibility; cerebral cortex biological structure; chemical synapses; electrical synapses; interneuron signaling; Artificial neural networks; Biological information theory; Biological system modeling; Brain modeling; Cerebral cortex; Chemicals; Computational efficiency; Encoding; Neurons; Proposals; Artificial Neural Networks; Biological Plausibility;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
Type :
conf
DOI :
10.1109/ICMLA.2008.73
Filename :
4725075
Link To Document :
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