This title appears in the Scientific Report :
2020
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.
Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease
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...
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Please use the identifier: http://dx.doi.org/10.1371/journal.pcbi.1007790 in citations.
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245 | |a Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer’s disease | ||
260 | |a San Francisco, Calif. |c 2020 |b Public Library of Science | ||
500 | |a 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"), | ||
520 | |a 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. | ||
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