Computational analysis of learning in young and ageing brains

TL;DR

This paper uses a computational model to show that young brains learn faster than older brains, matching biological observations.

Contribution

A novel computational model using a bipartite graph to simulate and compare learning in young and ageing brains.

Findings

Young brains learn faster than older brains in the model, consistent with biological data.

The model reveals key insights into memory consolidation differences between age groups.

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.

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