Effect of Alzheimer's disease on the dynamical and computational characteristics of recurrent neural networks

Abstract

Recurrent circuits of simple model neurons can provide the substrate for cognitive functions such as perception, memory, association, classification or prediction of dynamical systems . In Alzheimer's disease (AD), the impairment of such functions is clearly correlated to synapse loss . So far, the mechanisms underlying this correlation are only poorly understood. Here, we investigate how the loss of excitatory synapses in sparsely connected random networks of spiking excitatory and inhibitory neurons alters their dynamical and computational characteristics. By means of simulations, we study t