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Author Patterson, Kate
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract The physiology of how the human brain recalls memories is not well understood. Neural networks have been used in an attempt to model this process. Two types of networks have been used in several models of temporal sequence memory for simple sequences of randomly generated and also of structured patterns: auto- and hetero-associative networks. Previous work has shown that a model with coupled auto- and hetero-associative continuous attractor networks can robustly recall learned simple sequences. In this paper, we compare Hebbian learning and pseudo-inverse learning in a model for recalling temporal sequences in terms of their storage capacities. The pseudo-inverse learning method is shown to have a much higher storage capacity, making the new network model 700% more efficient by reducing calculations.
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2003-03-01
Publisher Place New York
Journal Crossroads (CROS)
Volume Number 14
Issue Number 1
Page Count 12
Starting Page 1
Ending Page 12


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Source: ACM Digital Library