Computational analysis of learning in young and ageing brains.

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-05-06 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1565660
Jayani Hewavitharana, Kathleen Steinhofel, Karl Peter Giese, Carolina Moretti Ierardi, Amida Anand
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Abstract

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.

年轻和衰老大脑学习的计算分析。
学习和记忆是大脑获取和储存信息的基本过程。然而,随着年龄的增长,大脑会发生重大变化,导致与年龄相关的认知能力下降。尽管有许多关于模拟大脑学习过程的计算模型和方法的研究,但它们往往集中在一般的神经功能上,显示出对解决学习中与年龄相关的变化的模型的潜在需求。在这篇论文中,我们提出了一个计算分析,关注年轻人和老年人大脑在学习方面的差异。使用二部图作为人工神经网络来模拟突触连接,我们通过应用不同的生物学启发的突触权重更新规则来模拟年轻人和老年人大脑的学习过程。我们的研究结果表明,与老年人相比,年轻人的大脑具有更快的学习能力,这与生物学观察结果一致。我们的模型有效地模拟了大脑与学习速度相关的基本机制,并揭示了记忆巩固的关键见解。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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