Using neural networks to simulate the Alzheimer’s Disease

Making use of biologically plausible artificial neural networks that implement Grossbergpsilas presynaptic learning rule, we simulate the possible effects of calcium dysregulation in the neuronpsilas activation function, to represent the most accepted model of Alzheimerpsilas Disease: the calcium dysregulation hypothesis. According to Cudmore and Turrigiano calcium dysregulation alters the shifting dynamic of the neuronpsilas 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 Alzheimerpsilas diseasepsilas symptoms such as memory loss and learning problems might be correlated to intrinsic neuronal plasticity impairment due to calcium dysregulation.