Using Neural Networks to Simulate the Alzheimer's Disease

Abstract

Making use of biologically plausible artificial neural networks that implement Grossberg’s presynaptic learning rule, we simulate the possible effects of calcium dysregulation in the neuron’s activation function, to represent the most accepted model of Alzheimer's Disease: the calcium dysregulation hypothesis. According to Cudmore and Turrigiano calcium dysregulation alters the shifting dynamic of the neuron’s activation function (intrinsic plasticity). We propose that this alteration might affect the stability of synaptic weights in which memories are stored. The results of the simulation supported the theoretical hypothesis, implying that the emergence of Alzheimer's disease's symptoms such as memory loss and learning problems might be correlated to intrinsic neuronal plasticity impairment due to calcium dysregulation.