TY  - JOUR
AU  - Bachmann, Claudia
AU  - Tetzlaff, Tom
AU  - Duarte, Renato
AU  - Morrison, Abigail
TI  - Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease
JO  - PLoS Computational Biology
VL  - 16
IS  - 8
SN  - 1553-734X
CY  - San Francisco, Calif.
PB  - Public Library of Science
M1  - FZJ-2020-02860
SP  - e1007790 -
PY  - 2020
N1  - Additional grants: Helmholtz Association Initiative and Networking Fund (project no. SO-092 [Advanced Computing Architectures] and Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain"),
AB  - The impairment of cognitive function in Alzheimer’s disease 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 activity statistics, we find that the loss of excitatory synapses on excitatory neurons reduces the network’s sensitivity to small perturbations. This decrease in sensitivity can be considered as an indication of a reduction of computational capacity. A full recovery of the network’s dynamical characteristics and sensitivity can be achieved by firing rate homeostasis, here implemented by an up-scaling of the remaining excitatory-excitatory synapses. Mean-field analysis reveals that the stability of the linearised network dynamics is, in good approximation, uniquely determined by the firing rate, and thereby explains why firing rate homeostasis preserves not only the firing rate but also the network’s sensitivity to small perturbations.
LB  - PUB:(DE-HGF)16
C6  - pmid:32841234
UR  - <Go to ISI:>//WOS:000565612000002
DO  - DOI:10.1371/journal.pcbi.1007790
UR  - https://juser.fz-juelich.de/record/878454
ER  -