A detailed data-driven network model of prefrontal cortex (Hass et al 2016)

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Accession:189160
Data-based PFC-like circuit with layer 2/3 and 5, synaptic clustering, four types of interneurons and cell-type specific short-term synaptic plasticity; neuron parameters fitted to in vitro data, all other parameters constrained by experimental literature. Reproduces key features of in vivo resting state activity without specific tuning.
Reference:
1 . Hass J, Hertag L, Durstewitz D (2016) A detailed data-driven network model of prefrontal cortex reproduces key features of in vivo activity PLoS Comput Biol 12(5):e1004930 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Prefrontal cortex (PFC);
Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB;
Model Concept(s): Activity Patterns; Methods; Laminar Connectivity;
Implementer(s): Hass, Joachim [joachim.hass at zi-mannheim.de]; Hertäg, Loreen [loreen.hertaeg at tu-berlin.de]; Durstewitz, Daniel [daniel.durstewitz at plymouth.ac.uk];
Search NeuronDB for information about:  GabaA; AMPA; NMDA;

Hass J, Hertag L, Durstewitz D (2016) A detailed data-driven network model of prefrontal cortex reproduces key features of in vivo activity PLoS Comput Biol 12(5):e1004930[PubMed]

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