Synaptic Runaway in Associative Networks and the Pathogenesis of Schizophrenia
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.
Cite
Text
Greenstein-Messica and Ruppin. "Synaptic Runaway in Associative Networks and the Pathogenesis of Schizophrenia." Neural Computation, 1998. doi:10.1162/089976698300017836Markdown
[Greenstein-Messica and Ruppin. "Synaptic Runaway in Associative Networks and the Pathogenesis of Schizophrenia." Neural Computation, 1998.](https://mlanthology.org/neco/1998/greensteinmessica1998neco-synaptic/) doi:10.1162/089976698300017836BibTeX
@article{greensteinmessica1998neco-synaptic,
title = {{Synaptic Runaway in Associative Networks and the Pathogenesis of Schizophrenia}},
author = {Greenstein-Messica, Asnat and Ruppin, Eytan},
journal = {Neural Computation},
year = {1998},
pages = {451-465},
doi = {10.1162/089976698300017836},
volume = {10},
url = {https://mlanthology.org/neco/1998/greensteinmessica1998neco-synaptic/}
}