Cognitive Modelling Aspects of Neurodevelopmental Disorders Using Standard and Oscillating Neighbourhood SOM Neural Networks

2025-07-16

AI Summary

Standard self-organizing map (SOM) neural networks lack biological plausibility in cognitive modeling—particularly for simulating neurodevelopmental disorders such as autism spectrum disorder and schizophrenia—due to their static, non-oscillatory neighborhood mechanisms. Method: We propose an oscillatory topological neighborhood mechanism that incorporates cortical oscillation properties, extending the inhibitory-pulse SOM (IPSOM) model through systematic modulation of the neighborhood width function to yield a biologically grounded, computationally tractable oscillatory SOM. Contribution/Results: Simulation results demonstrate that the oscillatory SOM matches standard SOMs in mapping stability, topological preservation, and output structure, confirming its validity within cognitive modeling frameworks. Crucially, the oscillatory neighborhood enhances the model’s capacity to emulate endogenous brain rhythms, offering a novel, neurobiologically informed computational paradigm for investigating neurodevelopmental disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Background/Introduction: In this paper, the neural network class of Self-Organising Maps (SOMs) is investigated in terms of its theoretical and applied validity for cognitive modelling, particularly of neurodevelopmental disorders. Methods: A modified SOM network type, with increased biological plausibility, incorporating a type of cortical columnar oscillation in the form of an oscillating Topological Neighbourhood (TN), is introduced and applied alongside the standard SOM. Aspects of two neurodevelopmental disorders, autism and schizophrenia, are modelled using SOM networks, based on existing neurocomputational theories. Both standard and oscillating-TN SOM training is employed with targeted modifications in the TN width function. Computer simulations are conducted using revised versions of a previously introduced model (IPSOM) based on a new modelling hypothesis. Results/Conclusions: The results demonstrate that there is strong similarity between standard and oscillating-TN SOM modelling in terms of map formation behaviour, output and structure, while the oscillating version offers a more realistic computational analogue of brain function. Neuroscientific and computational arguments are presented to validate the proposed SOM modification within a cognitive modelling framework.

Problem

• Modeling neurodevelopmental disorders using SOM neural networks
• Comparing standard and oscillating-TN SOM for cognitive modeling
• Enhancing biological plausibility of SOM for brain function simulation

Innovation

• Modified SOM with oscillating Topological Neighbourhood
• Targeted TN width function modifications
• Revised IPSOM model for brain function analogy