Abstract
The neural network class of self-organizing maps (SOMs) is a promising cognitive modeling tool in the study of the autistic spectrum pervasive developmental disorder. This work offers a novel validation of Gustafsson’s neural circuit theory, according to which autism relates to formation characteristics of cortical brain maps. A previously constructed spatial SOM behavioral model is used here as a cognitive model, and by incorporating formation deficiencies related to the topological neighborhood (TN) function. The resulting cognitive SOM maps, being sensitive to the width of TN during SOM formation, point to a model that exhibits marked behavioral characteristics of autism. The simulation results support the causal hypothesis that associates autistic behavior with certain functional and structural characteristics of the human nervous system and, specifically, Gustafsson’s theoretical proposition of the role of inhibitory lateral feedback synaptic connection strengths in autism.
Keywords
- Neural Networks
- Self-Organizing Maps
- Cognitive Modeling
- Autism
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References
Sun, R., Coward, L.A., Zenzen, M.J.: On Levels of Cognitive Modeling. Philosophical Psychology 18, 613–637 (2005)
Thomas, M.S.C., Karmiloff-Smith, A.: Connectionist Models of Cognitive Development, Atypical Development and Individual Differences. In: Sternberg, R.J., Lautrey, J., Lubart, T. (eds.) Models of Intelligence: International Perspectives, vol. 44, pp. 133–150. American Psychological Association, Washington, DC (2003)
Munakata, Y., McClelland, J.L.: Connectionist Models of Development. Developmental Science 6, 413–429 (2003)
Kanner, L.: Autistic Disturbances of Affective Contact. Nervous Child 2, 217–250 (1943)
Asperger, H.: Die Autistischen Psychopathen im Kindesalter. Archiv für Psychiatrie und Nervenkrankheiten 117, 76–136 (1944)
Gillberg, C., Coleman, M.: The Biology of the Autistic Syndromes. Cambridge University Press, Cambridge (2000)
Hermelin, B.: Images and Language. In: Rutter, M., Schopler, E. (eds.) Autism: A Reappraisal of Concept and Treatment. Plenum, New York (1978)
Happe, F.: The Autobiographical Writings of Three Asperger Syndrome Adults: Problems of Interpretation and Implications for Theory. In: Frith, U. (ed.) Autism and Asperger Syndrome. Cambridge University Press, Cambridge (1991)
Shulman, C., Yirmiya, N., Greenbaum, C.W.: From Categorization to Classification: A Comparison Among Individuals with Autism, Mental Retardation, and Normal Development. Journal of Abnormal Psychology 104, 601–609 (1995)
Gustafsson, L.: Inadequate Cortical Feature Maps: A Neural Circuit Theory of Autism. Biological Psychiatry 42, 1138–1147 (1997)
Frith, U.: Autism: Explaining the Enigma. Blackwell Publishers, Oxford (1989)
Hermelin, B., O’Connor, N.: Psychological Experiments with Autistic Children. Pergammon Press, Oxford (1970)
Willshaw, D.J., vor der Malsburg, C.: How Patterned Neural Connections Can Be Set Up by Self-Organization. In: Proceedings of the Royal Society of London, Series B, vol. 194, pp. 431–445. Royal Society, London (1976)
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)
Revithis, S., Wilson, W.H., Marcus, N.: IPSOM: A Self-organizing Map Spatial Model of How Humans Complete Interlocking Puzzles. In: Sattar, A., Kang, B.H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 285–294. Springer, Heidelberg (2006)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, NJ (1999)
Sun, R., Ling, C.: Computational Cognitive Modeling, the Source of Power and Other Related Issues. AI Magazine 19, 113–120 (1997)
Cottrell, M., Fort, J.C., Pagès, G.: Theoretical Aspects of the SOM Algorithm. Neurocomputing 21, 119–138 (1998)
Revithis, S.: Significance of Topological Neighborhood in SOM Cognitive Modeling of Brain Disorders: Current Neurocomputational Simulations. Book of Abstracts of the 16th International Conference of the Association of Psychology & Psychiatry for Adults & Children - APPAC Journal, vol. 18(2), p. 26. APPAC, Athens (2011)
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Revithis, S., Tagalakis, G. (2012). A SOM-Based Validation Approach to a Neural Circuit Theory of Autism. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_4
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DOI: https://doi.org/10.1007/978-3-642-30448-4_4
Publisher Name: Springer, Berlin, Heidelberg
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