Computational analysis of learning in young and ageing brains
Learning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline. Although there are numerous studies on computational models and approaches whic...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2025.1565660/full |
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| author | Jayani Hewavitharana Kathleen Steinhofel Karl Peter Giese Carolina Moretti Ierardi Amida Anand |
| author_facet | Jayani Hewavitharana Kathleen Steinhofel Karl Peter Giese Carolina Moretti Ierardi Amida Anand |
| author_sort | Jayani Hewavitharana |
| collection | DOAJ |
| description | Learning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline. Although there are numerous studies on computational models and approaches which aim to mimic the learning process of the brain, they often concentrate on generic neural function exhibiting the potential need for models that address age-related changes in learning. In this paper, we present a computational analysis focusing on the differences in learning between young and old brains. Using a bipartite graph as an artificial neural network to model the synaptic connections, we simulate the learning processes of young and older brains by applying distinct biologically inspired synaptic weight update rules. Our results demonstrate the quicker learning ability of young brains compared to older ones, consistent with biological observations. Our model effectively mimics the fundamental mechanisms of the brain related to the speed of learning and reveals key insights on memory consolidation. |
| format | Article |
| id | doaj-art-8f8fdd3843b44916a61e2edffce008f4 |
| institution | DOAJ |
| issn | 1662-5188 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj-art-8f8fdd3843b44916a61e2edffce008f42025-08-20T03:10:58ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882025-05-011910.3389/fncom.2025.15656601565660Computational analysis of learning in young and ageing brainsJayani Hewavitharana0Kathleen Steinhofel1Karl Peter Giese2Carolina Moretti Ierardi3Amida Anand4Department of Informatics, King's College London, London, United KingdomDepartment of Informatics, King's College London, London, United KingdomDepartment of Basic and Clinical Neuroscience, King's College London, London, United KingdomDepartment of Neuroimaging, King's College London, London, United KingdomDepartment of Physics, King's College London, London, United KingdomLearning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline. Although there are numerous studies on computational models and approaches which aim to mimic the learning process of the brain, they often concentrate on generic neural function exhibiting the potential need for models that address age-related changes in learning. In this paper, we present a computational analysis focusing on the differences in learning between young and old brains. Using a bipartite graph as an artificial neural network to model the synaptic connections, we simulate the learning processes of young and older brains by applying distinct biologically inspired synaptic weight update rules. Our results demonstrate the quicker learning ability of young brains compared to older ones, consistent with biological observations. Our model effectively mimics the fundamental mechanisms of the brain related to the speed of learning and reveals key insights on memory consolidation.https://www.frontiersin.org/articles/10.3389/fncom.2025.1565660/fullageing-brainslearningmemorycomputational-neuroscienceneural networks |
| spellingShingle | Jayani Hewavitharana Kathleen Steinhofel Karl Peter Giese Carolina Moretti Ierardi Amida Anand Computational analysis of learning in young and ageing brains Frontiers in Computational Neuroscience ageing-brains learning memory computational-neuroscience neural networks |
| title | Computational analysis of learning in young and ageing brains |
| title_full | Computational analysis of learning in young and ageing brains |
| title_fullStr | Computational analysis of learning in young and ageing brains |
| title_full_unstemmed | Computational analysis of learning in young and ageing brains |
| title_short | Computational analysis of learning in young and ageing brains |
| title_sort | computational analysis of learning in young and ageing brains |
| topic | ageing-brains learning memory computational-neuroscience neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2025.1565660/full |
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