Learning and development in neural networks: The importance of starting small

Learning and development in neural networks: The importance of starting small (1993)

by Jeffrey L. Elman

Venue:	Cognition
Citations:	290 - 12 self

Datum	Value	Source
TITLE	Learning and development in neural networks: The importance of starting small	INFERENCE
AUTHOR NAME	Jeffrey L. Elman	SVM HeaderParse 0.2
AUTHOR AFFIL	Departments of Cognitive Science and Linguistics, University of California, San Diego, La Jolla,	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92093-0526, USA	SVM HeaderParse 0.2
ABSTRACT	It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language), as investigated in con-nectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and ‘adultlike ’ in their capacity. Training succeeds only when networks begin with limited working memory and gradually ‘mature ’ to the adult state. This result suggests that rather than being a limitation, developmental restrictions on resources may constitute a necessary prerequisite for mastering certain complex domains. Specifically, successful learning may depend on starting small.	SVM HeaderParse 0.2
YEAR	1993	INFERENCE
VENUE	Cognition	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	71--99	INFERENCE
VOLUME	48	INFERENCE
CITATIONS	31 found	ParsCit 1.0