Abstract

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 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. 1 Introduction A prominent feature of attractor neural networks (ANN) as models for associative memory is their robustness, i.e. their ability to maintain performance in face of damage to their neurons and synapses. Robustness of biological systems is due, however, not just to their dist...

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