Lennart Gustafsson

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The autistic syndromes are caused by neurological dysfunctions. The capacity of autistic individuals to form representations of previous sensory impressions, useful for the processing of present information, is impaired. Self-organizing feature maps are mathematical models of cortical feature maps and may be used to simulate cortical processing.(More)
Narrow neural columns have been suggested to be a neuroanatomical abnormality in autism. A previous hypothetical explanation, an unbalance between excitatory and inhibitory lateral feedback in the neocortex, has been found to be difficult to reconcile with the relatively high comorbidity of autism with epilepsy. Two alternative explanations are discussed,(More)
— We present a model of integration of auditory and visual information as in the human cortex. More specifically, we demonstrate a possible way in which the phonetic symbols and associated Mandarin Chinese phonemes pronounced by different speakers are mapped onto the model of cortical areas. Our model has been strongly influenced by recent fMRI studies on(More)
We present a recurrent multimodal model of binding written words to mental objects and investigate the capability of the network in reading misspelt but categorically related words. Our model consists of three mutually interconnected association modules which store mental objects, represent their written names and bind these together to form mental(More)
The multimodal self-organizing network (MMSON), an artificial neural network architecture carrying out sensory integration, is presented here. The architecture is designed using neurophysiological findings and imaging studies that pertain to sensory integration and consists of interconnected lattices of artificial neurons. In this artificial neural(More)
Autism is a developmental disorder in which attention shifting is known to be restricted. Using an artificial neural network model of learning we show how detailed learning in narrow fields develops when attention shifting between different sources of stimuli is restricted by familiarity preference. Our model is based on modified Self-Organizing Maps (SOM)(More)
It is known from psychology and neuroscience that multi-modal integration of sensory information enhances the perception of stimuli that are corrupted in one or more modalities. A prominent example of this is that auditory perception of speech is enhanced when speech is bimodal, i.e. when it also has a visual modality. The function of the cortical network(More)
— Multimodal integration of sensory information has clear advantages for survival: events that can be sensed in more than one modality are detected more quickly and accurately, and if the sensory information is corrupted by noise the classification of the event is more robust in multimodal percepts than in the unisensory information. It is shown that using(More)
Autism is a developmental disorder in which attention shifting is known to be restricted. Self-organization of neural networks, conditioned by different attention shifting characteristics is investigated for higher-dimensional stimuli presented to the network from different sources. The attention shifting modes are 1) novelty seeking, 2) attention shift(More)