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

This title appears in the Scientific Report : 2020

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

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...

Personal Name(s):	Bachmann, Claudia (Corresponding author)
	Tetzlaff, Tom / Duarte, Renato / Morrison, Abigail
Contributing Institute:	Computational and Systems Neuroscience; INM-6 Jülich Supercomputing Center; JSC JARA - HPC; JARA-HPC Jara-Institut Brain structure-function relationships; INM-10 Theoretical Neuroscience; IAS-6
Published in:	PLoS Computational Biology, 16 (2020) 8, S. e1007790 -
Imprint:	San Francisco, Calif. Public Library of Science 2020
DOI:	10.1371/journal.pcbi.1007790
PubMed ID:	32841234
Document Type:	Journal Article
Research Program:	Human Brain Project Specific Grant Agreement 2 Human Brain Project Specific Grant Agreement 1 Theory, modelling and simulation (Dys-)function and Plasticity Connectivity and Activity Functional Neural Architectures Advanced Computing Architectures KFO 219-TP9 - Basalganglien-Kortex-Schleifen: Mechanismen pathologischer Interaktion und ihrer therapeutischen Modulation, Teilprojekt 9: "Mathematische Modellierung der Entstehung und Suppression pathologischer Aktivitätszustände in den Basalganglien-Kortex-Schleifen" (DI 1721/3-1_18032015)
Link:	OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/25883 in citations.
Please use the identifier: http://dx.doi.org/10.1371/journal.pcbi.1007790 in citations.

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.