Lennart Gustafsson

Inadequate cortical feature maps: A neural circuit theory of autism

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. Dysfunctional self-organization, resulting in disability to extract features from stimuli, is proposed as a neural circuit theory of autism. The nature and a possible cause of dysfunction self-organization are examined. It is shown that impaired feature detection is valid for explaining the memory function in autism, the lack of drive for central coherence according to Friths theory of autism, and a number of impairments from the diagnostic criteria. Unequal levels of impairment of different cortical feature maps can account for the typically uneven intelligence profile of autistic individuals. Excessive inhibitory lateral feedback synaptic connection strengths are presented as one factor impairing the development of feature maps. Strong or excessive inhibitory lateral feedback synaptic connection strengths also cause high sensory discrimination and abnormal sensory responses, both documented in autism. A neural circuit theory for autism has been presented. For a proof of this neural circuit theory neurological investigations are required.

Comment on “Disruption in the Inhibitory Architecture of the Cell Minicolumn: Implications for Autism”:

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, an early low capacity for producing serotonin, documented in autism, and insufficient production of nitric oxide. An early low level of serotonin has in animal experiments caused narrow neural columns. Insufficient nitric oxide is known from neural network theory to cause narrow neural columns.

Speaker-dependent Bimodal Integration of Chinese Phonemes and Letters Using Multimodal Self-organizing Networks

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 integration of letters and speech sounds in the human brain. The model is based on multimodal self-organizing networks (MuSoNs) which were introduced in our previous works and proved to be a convenient tool to describe and study mapping and integration of sensory information as in the cortex. The model also shows how phonemes pronounced by different speakers are mapped onto overlapping cortical areas.

A self-organized artificial neural network architecture for sensory integration with applications to letter-phoneme integration

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 architecture, the degree of recognition of stimuli, that is, the perceived reliability of stimuli in the various subnetworks, is included in the computation. The MMSONs behavior is compared to aspects of brain function that deal with sensory integration. According to human behavioral studies, integration of signals from sensory receptors of different modalities enhances perception of objects and events and also reduces time to detection. In neocortex, integration takes place in bimodal and multimodal association areas and result, not only in feedback-mediated enhanced unimodal perception and shortened reaction time, but also in robust bimodal or multimodal percepts. Simulation data from the presented artificial neural network architecture show that it replicates these important psychological and neuroscientific characteristics of sensory integration.

Multimodal feedforward self-organizing maps

We introduce a novel system of interconnected Self- Organizing Maps that can be used to build feedforward and recurrent networks of maps. Prime application of interconnected maps is in modelling systems that operate with multimodal data as for example in visual and auditory cortices and multimodal association areas in cortex. A detailed example of animal categorization in which the feedworward network of self-organizing maps is employed is presented. In the example we operate with 18-dimensional data projected up on the 19-dimensional hyper-sphere so that the “dot-product” learning law can be used. One potential benefit of the multimodal map is that it allows a rich structure of parallel unimodal processing with many maps involved, followed by convergence into multimodal maps. More complex stimuli can therefore be processed without a growing map size.

Excessive lateral feedback synaptic inhibition may cause autistic characteristics.

Reviews the existence of abnormalities of perception in autism and argues that cortical maps for feature recognition are impaired in autistic individuals. Suitably chosen artificial neural networks ...

Bimodal Integration of Phonemes and Letters: an Application of Multimodal Self-Organizing Networks

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 a multimodal self-organizing network (MuSON), consisting of several interconnected Kohonen self-organizing maps (SOM), bimodal integration of phonemes, auditory elements of language, and letters, visual elements of language, can be simulated. Robustness of the bimodal percepts against noise in both the auditory and visual modalities is clearly demonstrated.

Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli

It is known from psychology and neuroscience that multimodal 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 processing speech in auditory and visual cortices and in multimodal association areas, is modeled with a Multimodal Self-Organizing Network (MuSON), consisting of several Kohonen Self-Organizing Maps (SOM) with both feedforward and feedback connections. Simulations with heavily corrupted phonemes and uncorrupted letters as inputs to the MuSON demonstrate a strongly enhanced auditory perception. This is explained by feedback from the bimodal area into the auditory stream, as in cortical processing.

A recurrent multimodal network for binding written words and sensory-based semantics into concepts

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 concepts. A feedback gain controlling top-down influence is incorporated into the model architecture and it is shown that correct settings for this during map formation and simulated reading experiments is necessary for correct interpretation and semantic binding of the written words.

An attempt in modelling autism using self-organizing maps

Autism is a developmental disorder in which attention shift impairment and strong familiarity preference are considered to be prime deficiencies. We model these two characteristics of autistic behaviour using Self-Organizing Maps (SOFM).