Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)

Accession:144089
"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. ... Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression. "
Reference:
1 . Sidiropoulou K, Poirazi P (2012) Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons Plos Computational Biology 8(4):e1002489 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex layer 5-6 pyramidal cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I A; I K; I K,Ca; I CAN;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA; IP3;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Detailed Neuronal Models;
Implementer(s): Sidiropoulou, Kyriaki [sidirop at imbb.forth.gr];
Search NeuronDB for information about:  Neocortex layer 5-6 pyramidal cell; GabaA; GabaB; AMPA; NMDA; IP3; I Na,p; I Na,t; I L high threshold; I A; I K; I K,Ca; I CAN; Gaba; Glutamate;
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This is the readme for the model associated with the paper:

Sidiropoulou K, Poirazi P (2012) Predictive Features of Persistent
Activity Emergence in Regular Spiking and Intrinsic Bursting Model
Neurons. Plos Computational Biology 8(4):e1002489

These NEURON model files were contributed by the paper authors.
NEURON is freely available from http://www.neuron.yale.edu

To run: either auto-launch from ModelDB or download and expand the
archive, compile the mod files (with mknrndll (mac or mswin) or
nrnivmodl (unix/linux)).  Start by double clicking the mosinit.hoc
(mswin), drag and dropping the mosinit.hoc on the nrngui icon (mac) or
by typing "nrngui mosinit.hoc" in the expanded archive (unix/linux).

Once the simulation has started press the "run simulation" button.
After a short time you should see a graph similar to fig 4B1 from the
paper:

screenshot

20140310 An ar2 argument was added to a rate call in naf.mod so that
the model would work with a new version (1024) of NEURON whose C
compiler checks that the number of arguments in function calls matches
the number in the definition. The results of the simulation was
uneffected.