Journal of Autism and Developmental Disorders

, Volume 34, Issue 2, pp 189–198

Self-Organization of an Artificial Neural Network Subjected to Attention Shift Impairments and Familiarity Preference, Characteristics Studied in Autism



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.

Autism attention neural networks self-organizing maps 


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Copyright information

© Plenum Publishing Corporation 2004

Authors and Affiliations

  1. 1.Luleå University of TechnologyLuleåSweden
  2. 2.Computer Science and Software EngineeringMonash UniversityVicAustralia

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