NDLI: Simultaneous verses successive learning in neural networks

Content Provider	IEEE Xplore Digital Library
Author	Choi, A. Nareshkumar, N.
Copyright Year	2009
Description	Author affiliation: Electrical and Computer Engineering, Mercer University, Macon, GA, US (Choi, A.) \|\| Computer Science and System Analysis, Miami University, Oxford, OH, US (Nareshkumar, N.)
Abstract	Neural networks were inspired by the human brain, with great hopes that neural networks would capture the vast potential of its biological counterpart. This paper explores the link between neural networks and the human brain in the context of simultaneous vs. successive learning. Learning experiments conducted on human subjects were modeled and repeated using neural networks as test subjects. Neural networks confirmed the conclusion from human subject experiments that simultaneous learning was faster than successive learning. Loess and Duncan [1] further extended their hypothesis without formal experimental evidence that simultaneous would outperform successive as complexity increased beyond the scope of their human experiments. Interestingly, neural networks contradict their hypothesis. The results from neural networks demonstrate an existence of a threshold, after which the effects of simultaneous and successive learning become negligible. Intuitively, when humans are presented with complicated tasks, the type of learning is immaterial, since the complexity of the problem would overwhelm any advantages one method has over the other. Confirmation of this intuition can only be confirmed through future human experiments. Furthermore, this paper demonstrates that neural networks can be used as a rough model and give valuable insight into a problem, before the costly human subject experiments are conducted.
Starting Page	4224
Ending Page	4231
File Size	178978
Page Count	8
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346827
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Biological neural networks Humans Artificial neural networks Psychology Cognition Learning Artificial intelligence Brain modeling Cognitive science neural networks artificial intelligence
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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