8 research outputs found
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Computational-Neuroscientific Correspondence of Oscillating-TN SOM Neural Networks
Oscillating-TN (Topological Neighborhood) Self-Organising-Map (SOM) artificial neural networks can facilitate the study of neurodevelopmental cognitive phenomena. Their cognitive modelling significance rests primarily on the premise of biological realism. Despite the difference in neuronal activity description between spike-train brain signaling and the rate-based computer SOM models, there is a valid analogy in cortical columnar activation synchrony.
A cortical macrocolumn can be modeled as a computer-trained SOM with emerging or structural minicolumns represented by SOM-TN groups of neurons. Neural excitation and lateral inhibition result in structural cortical changes modeled by SOM Hebbian TN-activation. Oscillating-TN SOMs can model brain plasticity and regulate sensory desensitization. Neural synchrony can be modeled at various levels: macroscopically, there is an analogy between an oscillating local field potential and a SOM oscillating-TN width computational session. There are also arguments to support the hypothesis that SOM stability or entrenchment during computational map formation associates with neural oscillatory sensory prediction
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A Research Blueprint for the SOM Neurocomputational Cognitive Modeling of Brain Disorders
Three guiding questions of a research blueprint for the study of brain disorders, using self-organising map (SOM) neural network modeling, are proposed: a) What does the current research on computational psychology/psychiatry and cognitive neuroscience suggest about atypical brain development and the associated behaviour? b) What cortical structures and functions are involved in certain brain disorders (e.g., autism, schizophrenia) and how is memory implicated? c) How well do existing neurocomputational SOM models explicate possible functional and structural aspects of brain-behavioural disorders?
My current investigation focuses on the closer examination of the modulation of standard & oscillating inhibition-excitation in SOM neural networks and the cognitive modelling implications for existing brain-disorder theories. This includes the computational, mathematical and functional study of the standard-TN (Topological Neighbourhood) SOM and a proposed oscillating-TN SOM, and drawing connections between i) the model, ii) functional and structural elements at the cognitive neuroscientific level, and iii) atypical behavioural phenotypes
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SOM Cognitive Modeling of Autistic and Schizophrenic Traits Using an Oscillating Topological Neighborhood Width Function
A SOM modelling approach to behaviour, cognition and cognitive development
This thesis reports the author’s research on the role of neural self-organisation in cognition and cognitive development, the implications of the Self-Organizing Map (SOM) simulation of brain activity at the behavioural level, the prospects of SOM modelling as an explanatory framework to brain disorders, and the cognitive modelling role of SOM properties and parameters, especially the topological neighbourhood during SOM formation. This includes the construction of a number of behavioural and cognitive SOM models, demonstrating behavioural classification, behavioural prediction, and working memory load, and putting existing neuropsychological theories of brain disorders (autism, schizophrenia) into a cognitive modelling perspective. A modified SOM type, with increased biological plausibility, incorporating a type of cortical columnar oscillation in the form of an oscillating topological neighbourhood, is introduced and evaluated alongside the standard SOM.The artificial neural network class of self-organizing maps is of particular theoretical and engineering importance, and a principal constituent of the neurocomputational cognitive paradigm. SOM networks have a number of properties and characteristics that offer remarkable statistical and engineering computational power, and are biologically relevant to the developmental aspect of cognition as well as to structural and functional elements of the neocortex.This research offers insights on brain functioning, cognitive development and the mechanisms of higher mental processes, a novel way of applying connectionism to computational developmental neuropsychology and to behavioural modelling, and an assessment of SOM cognitive modelling. The thesis demonstrates that the SOM modelling approach offers significant levels of behavioural classification and prediction when based on an appropriate domain encoding, and could assist in revealing the etiology and mechanisms of brain-behavioural neurodevelopmental disorders such as autism and schizophrenia. It also argues that SOM topological neighbourhood oscillation is a more biologically relevant mechanism and demonstrates its functional and computational equivalence to the standard SOM.As a result of this work, further SOM cognitive and behavioural modelling research is encouraged, particularly on educational psychology, on brain reorganisation due to impairment, and on atypical clinical phenotypes of memory and executive function