Protective effects of learning on a progressively impaired neural network model of memory

The human brain consists of billions of neurons with many connections. Each neuron has a relatively simple function, however, when combined with other neurons the resultant behavior becomes complex. Neural network modeling offers the possibility of clarifying the biological interactions, but neural networks are yet to be applied to the study of age-related memory loss. A neural network model capable of reproducing some aspects of brain function, namely pattern recognition and classification, is presented. The model is increasingly impaired to replicate aging effects. The performance of various networks, trained to different degrees (representing various levels of education or BRC), are compared.