Compensatory mechanisms in an attractor neural network model of schizophrenia.

D. Horn*, E. Ruppin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We investigate the effect of synaptic compensation on the dynamic behavior of an attractor neural network receiving its input stimuli as external fields projecting on the network. It is shown how, in the face of weakened inputs, memory performance may be preserved by strengthening internal synaptic connections and increasing the noise level. Yet, these compensatory changes necessarily have adverse side effects, leading to spontaneous, stimulus-independent retrieval of stored patterns. These results can support Stevens' recent hypothesis that the onset of schizophrenia is associated with frontal synaptic regeneration, occurring subsequent to the degeneration of temporal neurons projecting on these areas.

Original languageEnglish
Pages (from-to)182-205
Number of pages24
JournalNeural Computation
Issue number1
StatePublished - Jan 1995


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