Exploring Self-Organizing Maps for Addressing Semantic Impairments

Jorge Graneri, Sebastian Basterrech, Eduardo Mizraji, Gerardo Rubino

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Since the 1990s, Self-Organizing Maps (SOMs) have been instrumental in reducing dimensionality and visualizing high-dimensional data. This study adapts SOMs to explore the neural representation of human concepts, their neural ‘word net’ mapping, and the deterioration of these mappings in certain neurological disorders. Our model draws inspiration from semantic dementia, a severe condition that degrades semantic knowledge in the brain. Although our exploration utilizes a low-dimensional model — a rough simplification with respect of our brains — it successfully replicates observed clinical patterns. These promising results inspire further research to enhance our understanding of language pathophysiology in neurological disorders.
Original languageEnglish
Title of host publicationProceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN 2024
Publisheri6doc.com
Pages345-350
ISBN (Print)978-2-87587-090-2
Publication statusAccepted/In press - 2025
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2024 - Bruges, Belgium
Duration: 9 Oct 202411 Oct 2024

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2024
Country/TerritoryBelgium
CityBruges
Period09/10/202411/10/2024

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