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The impairment of cognitive function in Alzheimer's is clearly correlated tosynapse loss. However, the mechanisms underlying this correlation are onlypoorly understood. Here, we investigate how the loss of excitatory synapses insparsely connected random networks of spiking excitatory and inhibitory neuronsalters their dynamical characteristics. Beyond the effects on the network'sactivity statistics, we find that the loss of excitatory synapses on excitatoryneurons shifts the network dynamic towards the stable regime. The decrease insensitivity to small perturbations to time varying input can be considered asan indication of a reduction of computational capacity. A full recovery of thenetwork performance can be achieved by firing rate homeostasis, hereimplemented by an up-scaling of the remaining excitatory-excitatory synapses.By analysing the stability of the linearized network dynamics, we explain howhomeostasis can simultaneously maintain the network's firing rate andsensitivity to small perturbations.

Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's disease