1997
DOI: 10.1590/s0104-65001997000200003
|View full text |Cite
|
Sign up to set email alerts
|

Employing a Multiple Associative Memory Model for Temporal Sequence Reproduction

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2008
2008
2020
2020

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 0 publications
0
1
0
Order By: Relevance
“…These states are often recorded by Hebbian learning rule [12], [19]. When a perfect, partial, or noisy version of a trained piece of information is presented to the neural network, it responds to the stimulus with the closest of the stored memories [18]. In this section a linear auto-associative memory model has been discussed.…”
Section: Auto-associative Memory For Face Recognitionmentioning
confidence: 99%
“…These states are often recorded by Hebbian learning rule [12], [19]. When a perfect, partial, or noisy version of a trained piece of information is presented to the neural network, it responds to the stimulus with the closest of the stored memories [18]. In this section a linear auto-associative memory model has been discussed.…”
Section: Auto-associative Memory For Face Recognitionmentioning
confidence: 99%