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

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.

Model Type: Realistic Network

Region(s) or Organism(s): Prefrontal cortex (PFC)

Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron

Receptors: GabaA; AMPA; NMDA

Model Concept(s): Activity Patterns; Methods; Laminar Connectivity

Simulation Environment: C or C++ program; MATLAB

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]


Hass J, Hertäg L, Durstewitz D. (2016). A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity. PLoS computational biology. 12 [PubMed]

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