---
abstract: |-
The Convergence-Zone model shows how sparse, random memory patterns can lead to one-shot storage and high capacity in the hippocampal component of the episodic memory system. This paper presents a biologically more realistic version of the model, with continuously-weighted connections and storage through Hebbian learning and normalization. In contrast to the gradual weight adaptation in many neural network models, episodic memory turns out to require high learning rates. Normalization allows earlier patterns to be overwritten,
introducing time-dependent forgetting similar to the hippocampus.
altloc:
- http://www.cs.utexas.edu/users/nn/pages/publications/memory.html
- ftp://ftp.cs.utexas.edu/pub/neural-nets/papers/howe.hebbian-convergence-zone.pdf
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creators_id: []
creators_name:
- family: Howe
given: Michael
honourific: ''
lineage: ''
- family: Miikkulainen
given: Risto
honourific: ''
lineage: ''
date: 2000
date_type: published
datestamp: 2001-11-18
department: ~
dir: disk0/00/00/18/92
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eprint_status: archive
eprintid: 1892
fileinfo: /style/images/fileicons/application_pdf.png;/1892/3/howe.hebbian%2Dconvergence%2Dzone.pdf
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keywords: 'Episodic memory, hippocampus, forgetting, convergence-zones'
lastmod: 2011-03-11 08:54:49
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metadata_visibility: show
note: ~
number: ~
pagerange: 817-821
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publication: Neurocomputing
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refereed: TRUE
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reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:41:28
subjects:
- comp-neuro-sci
- comp-sci-neural-nets
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title: |-
Hebbian Learning and
Temporary Storage in the Convergence-Zone Model of Episodic Memory
type: journalp
userid: 438
volume: 32-33