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

TL;DR

This study explores how synapse loss in Alzheimer's affects neural network stability and demonstrates that firing rate homeostasis can restore network function by adjusting remaining synapses.

Contribution

It reveals that firing rate homeostasis counteracts stability changes caused by synapse loss, maintaining network performance in Alzheimer's disease models.

Findings

Synapse loss shifts network dynamics towards stability.

Firing rate homeostasis restores network activity and sensitivity.

Homeostatic up-scaling maintains computational capacity.

Abstract

The impairment of cognitive function in Alzheimer's is clearly correlated to synapse loss. However, 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 characteristics. Beyond the effects on the network's activity statistics, we find that the loss of excitatory synapses on excitatory neurons shifts the network dynamic towards the stable regime. The decrease in sensitivity to small perturbations to time varying input can be considered as an indication of a reduction of computational capacity. A full recovery of the network performance can be achieved by firing rate homeostasis, here implemented by an up-scaling of the remaining excitatory-excitatory synapses. By analysing the stability of the linearized network dynamics, we explain how homeostasis can simultaneously maintain the network's firing rate and sensitivity to small perturbations.