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abstract: Nonlinear dendritic processing appears to be a feature of biological neurons and would also be of use in many applications of artificial neural networks. This paper presents a model of an initially standard linear unit which uses unsupervised learning to find clusters of inputs within which inactivity at one synapse can occlude the activity at the other synapses.
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creators_name:
  - family: Spratling
    given: Michael
    honourific: ''
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  - family: Hayes
    given: Gillian
    honourific: ''
    lineage: ''
date: 2000
date_type: published
datestamp: 2000-11-15
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dir: disk0/00/00/11/09
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eprintid: 1109
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lastmod: 2011-03-11 08:54:27
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number: 1
pagerange: 17-27
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publication: Neural Processing Letters
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rev_number: 10
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status_changed: 2007-09-12 16:36:30
subjects:
  - comp-sci-neural-nets
  - neuro-mod
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title: Learning synaptic clusters for non-linear dendritic processing
type: journalp
userid: 1040
volume: 11
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