Synaptic Impairment, Robustness of Excitatory NNs w/ Different Topologies (Mirzakhalili et al 2017)

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Accession:264628
"Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. ..."
Reference:
1 . Mirzakhalili E, Gourgou E, Booth V, Epureanu B (2017) Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Front Neural Circuits 11:38 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Persistent activity;
Implementer(s): Mirzakhalili, Ehsan ;