Abstract:
:Synaptic runaway denotes the formation of erroneous synapses and premature functional decline accompanying activity-dependent learning in neural networks. This work studies synaptic runaway both analytically and numerically in binary-firing associative memory networks. It turns out that synaptic runaway is of fairly moderate magnitude in these networks under normal, baseline conditions. However, it may become extensive if the threshold for Hebbian learning is reduced. These findings are combined with recent evidence for arrested N-methyl-D-aspartate (NMDA) maturation in schizophrenics, to formulate a new hypothesis concerning the pathogenesis of schizophrenic psychotic symptoms in neural terms.
journal_name
Neural Computjournal_title
Neural computationauthors
Greenstein-Messica A,Ruppin Edoi
10.1162/089976698300017836subject
Has Abstractpub_date
1998-02-15 00:00:00pages
451-65issue
2eissn
0899-7667issn
1530-888Xjournal_volume
10pub_type
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journal_title:Neural computation
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