SOM Cognitive Modeling of Autistic and Schizophrenic Traits Using an Oscillating Topological Neighborhood Width Function

Abstract

The artificial neural network class of self-organizing maps (SOMs) is a powerful and promising cognitive modeling tool in the study of the brain and its disorders. Under this premise, this paper proposes a novel modification of the standard SOM algorithm in the form of an oscillating Topological Neighborhood (TN) width function. Existing research in neuroscience indicates that SOMs with oscillating TN width could exhibit higher biological plausibility than standard TN width SOMs. In this paper, two neuro-developmental disorders, autism and schizophrenia, are modeled, based on existing neurocomputational theories, using both SOM approaches. The simulation results demonstrate that there is significant equivalence between standard and oscillating TN width SOM modeling in terms of map formation behavior, output and structure. The theoretical and computational arguments presented validate the proposed SOM modification within a cognitive modeling framework.


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