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KMID : 0981420000020010099
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Schizophrenia Clinics
2000 Volume.2 No. 1 p.99 ~ p.119
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Artificial Neural Network Theory and Positive / Negative Symptoms
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Kang Ung-Gu
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Abstract
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Much about the pahtophysiology of schizophrenia has been elucidated by the molecular and biochemical studies. However, these studies could not explain how their findings in-duce clinically observable psychopathology. The theory of artificial neural network (ANN) may serve as a bridge crossing this gap. ANNs are the systems composed of many simple calculating units (neurons) extensively connected (synapse) Nvith each other. They work by the parallel distributed processing and have dispersed memory. These characteristics are similar to real brain in anatomical, physiological and molecular as well as phenomenological aspects. There is a multitude of models concerning the ANN. Some of them have been utilized to simulate abnormal brain functioning as well as normal cognitive functions. With regard to schizophrenia, one suggested model was the " synaptic pruning". This model could simulate the loosening of association, delusions, hallucinations, Schneiderian first rank symptoms and negative symptoms. In microscopic aspect, synaptic pruning might be a model related to the neurodevelopmental, glutamatergic and the sensory gating hypotheses of schizophrenia. Another was the "reduced neuronal gain" model. This model could simulate the cognitive impairments found in the schizophrenic patients and could be a model related to the prefi-ontal copaminergic hypoactivity hypothesis. ANN theories provide a ncNv approach to the understanding of psychopathology. However, in order to achieve a more comprehensive understanding of normal and abnormal brain functioning, we may need a new, model composed of the "network of networks".
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KEYWORD
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Artificial neural network, Parallel distributed processing, Psychopathology, Schizophrenia, Simulation, Synaptic pruning
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