Self-Organization of an Artificial Neural Network Subjected to Attention Shift Impairments and Familiarity Preference, Characteristics Studied in Autism
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Autism is a developmental disorder with possibly multiple pathophysiologies. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations. Cortical feature maps make it possible to classify stimuli, such as phonemes of speech, disregarding incidental detail. Hierarchies of such maps are instrumental in creating abstract codes and representations of objects and events. Self-Organizing Maps (SOMs) are artificial neural networks that offer insights into the development of cortical feature maps.
Attentional impairment is prevalent in autism, but whether it is caused by attention-shift impairment or strong familiarity preference or negative response to novelty is a matter of debate. We model attention shift during self-organization by presenting a SOM with stimuli from two sources in four different modes, namely, novelty seeking (regarded as normal learning), attention-shift impairment (shifts are made with a low probability), familiarity preference (shifts made with a lower probability to the source that is the less familiar to the SOM of the two sources), and familiarity preference in conjunction with attention-shift impairment.
The resulting feature maps from learning with novelty seeking and with attention-shift impairment are much the same except that learning with attention-shift impairment often yields maps with a somewhat better discrimination capacity than learning with novelty seeking. In contrast, the resulting maps from learning with strong familiarity preference are adapted to one of the sources at the expense of the other, and if one of the sources has a set of stimuli with smaller variability, the resulting maps are adapted to stimuli from that source. When familiarity preference is less pronounced, the resulting maps may become normal or fully restricted to one of the sources, and in that case, always the source with smaller variability if such a source is present. Such learning, in a system with many different maps, will result in very uneven capacities.
Learning with familiarity preference in conjunction with attention-shift impairment surprisingly has higher probability for the development of normal maps than learning with familiarity preference alone.
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- American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. (4th ed.). Washington, DC: Author.Google Scholar
- Asperger, H. (1944). Die “autistischen Psychopathen” im Kindesalter. Arch. Psychiatrie Nervenkrankheiten, 117, 76-136. (Translated in Frith U. (1991). Autism and Asperger Syndrome. Cambridge: Cambridge University Press.)Google Scholar
- Cohen, I. (1994). An artificial neural network analogue of learning in autism. Biological Psychiatry, 36, 5-20.Google Scholar
- Cohen, I. (1998). Neural network analysis of learning in autism. In D. Stein & J. Ludick (Eds.), Neural networks and psychpathology (pp. 274-315). Cambridge: Cambridge University Press.Google Scholar
- Courchesne, E. (2002). Deciphering the puzzle: Unusual patterns of brain development in autism. In Inaugural World Autism Congress. Melbourne, Australia.Google Scholar
- Courchesne, E., Akshoomoff, N., Townsend, J., & Saitoh, O. (1995). A model system for the study of attention and the cerebellum: Infantile autism. In G. Karmos, M. Molnár, I. Csépe, & J. Desmedt (Eds.), Perspectives of event-related potentials research (pp. 315-325). Amsterdam: Elsevier Science.Google Scholar
- Courchesne, E., Townsend, J., Akshoomoff, N., Saitoh, O., Yeung-Courchesne, R., Lincoln, A., James, H., Haas, R., Schreibman, L., & Lau, L. (1994a). Impairment in shifting attention in autistic and cerebellar patients. Behavioral Neuroscience, 108, 848-865.Google Scholar
- Courchesne, E., Townsend, J., Akshoomoff, N., Yeung-Courchesne, R., Press, G., Murakami, J., Lincoln, A., James, H., Saitoh, O., Egaas, B., Haas, R., & Schreibman, L. (1994b). A new finding: Impairment in shifting attention in autistic and cerebellar patients. In S. Broman & J. Grafinan (Eds.), Atypical cognitive deficits in developmental disorders: Implications for brain function (pp. 101-137). Hillsdale, NJ: Erlbaum.Google Scholar
- Dawson, G., Meltzoff, A., Osterling, J., Rinaldi, J., & Brown, E. (1998). Children with autism fail to orient to naturally occurring social stimuli. Journal of Autism and Developmental Disorders, 28, 479-485.Google Scholar
- Frith, U. (1989). Autism: Explaining the enigma. Oxford: Basil Blackwell.Google Scholar
- Gillberg, C., & Coleman, M. (2000). The biology of the autistic syndromes. (3rd ed.). Cambridge University Press.Google Scholar
- Gustafsson, L. (1997). Inadequate cortical feature maps: A neural circuit theory of autism. Biol. Psychiatry, 42, 1138-1147.Google Scholar
- Happé, F. (1991). The autobiographical writings of three asperger syndrome adults: Problems of identification and implications for theory. In U. Frith (Ed.), Autism and asperger syndrome (pp. 207-242). Cambridge: Cambridge University Press.Google Scholar
- Haykin, S. (1999). Neural networks-A comprehensive foundation. (2nd ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
- Hermelin, B. (1978). Images and language. In M. Rutter & E. Schoppler (Eds.), Autism: A reappraisal of concept and treatment (pp. 141-154). New York: Plenum.Google Scholar
- Kandel, E., Schwartz, J., & Jessel, T. (Eds.). (2000). Principles of neural science. (4th ed.). New York: McGraw Hill.Google Scholar
- Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child, 2, 217-250.Google Scholar
- Kohonen, T. (2001). Self-organising maps. (3rd ed.). Berlin: Springer.Google Scholar
- Kootz, J., Marinelli, B., & Cohen, D. (1982). Modulation of response to environmental stimulation in autistic children. Journal of Autism and Developmental Disorders, 12, 185-193.Google Scholar
- McClelland, J. L. (2000). The basis of hyperspecificity in autism: A preliminary suggestion based on properties of neural nets. Journal of Autism and Developmental Disorders, 30, 497-502.Google Scholar
- Minshew, N., Luna, B., & Sweeney, J. (1999). Oculomotor evidence for neocortical systems but not cerebellar dysfunction in autism. Neurology, 52, 917-922.Google Scholar
- Oliver, A., Johnson, M. H., & Pennington, B. (2000). Deviation in the emergence of representations: A neuroconstructivist framework for analysing developmental disordere. Developmental Science, 3, 1-23.Google Scholar
- Pascualvaca, D., Fantie, B., Papageorgiou, M., & Mirsky, A. (1998). Attentional capacities in children with autism: Is there a general deficit in shifting focus? Journal of Autism and Developmental Disorders, 28, 467-478.Google Scholar
- Price, D. J., & Willshaw, D. J. (2000). Mechanisms of cortical development. Oxford: Oxford University Press.Google Scholar
- Ritter, H., Martinetz, T., & Schulten, K. (1992). Neural computation and self-organizing maps. Reading, MA: Addison-Wesley.Google Scholar
- Spitzer, M. (1995). A neurocomputational approach to delusions. Compr. Psychiatry, 36, 83-105.Google Scholar
- Townsend, J., Harris, N., & Courchesne, E. (1996). Visual attention abnormalities in autism: Delayed orienting to location. Journal of the International Neuropsychological Society, 2, 541-550.Google Scholar
- Townsend, J. P., Courchesne, E., Covington, J., Westerfield, M., Harris, N. S., Lyden, P., Lowry, P., & Press, G. A. (1999). Spatial attention deficits in patients with acquired or developmental cerebellar abnormality. Journal of Neuroscience, 19, 5632-5643.Google Scholar