Computational analysis of learning in young and ageing brains

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.

Keywords: ageing-brains; computational-neuroscience; learning; memory; neural networks.

Figure 1

The structure of the artificial neural network represented by a bipartite graph with N vertices as input nodes, N vertices as output nodes, and N² edges connecting each pair of input and output nodes.

Figure 2

Young and old learning processes. (Top) One iteration of the young learning process represented using a 6-node network. The chosen output node is O₁ and all its incoming edges are incremented by 0.5 of their initial weights resulting in the signal reaching the selected node being increased by 0.5 of its previous value. (Bottom) One iteration of the old learning process represented using a 6-node network. If the chosen output node is O₁ and w₁₁ is selected as the edge for the weight update, the second edge w₁₂ is selected based on proximity from the same input node and its connection is re-wired from O₂ to O₁ by removing the edge and adding its weight to w₁₁.

Figure 3

The graph represents the comparison of speed of the learning processes in young and old brains. The x-axis shows the number of iterations and the y-axis shows the number of patterns that completed learning at each iteration, with percentages highlighted as data tips.

Figure 4

The graph represents the comparison of speed of the learning in old brains with and without prior learning. The x-axis shows the number of iterations and the y-axis shows the number of patterns that completed learning at each iteration, with percentages highlighted as data tips.