SenseLab
Computational model
  Data
Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
Yim_et_al_2015 [168071]
The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
  • Yim MY, Hanuschkin A, Wolfart J (2015) Show Other
jakob.wolfart@uni-rostock.de
Kir2 leak
Pattern Separation
Wolfart, Jakob
Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability. Yim MY, Hanuschkin A, Wolfart J. Hippocampus. 2015 Mar;25(3):297-308. doi: 10.1002/hipo.22373. PMID: 25269417
False
True
Other categories referring to Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
Revisions: 22
Last Time: 10/30/2015 5:37:48 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin