Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)

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Accession:144579
Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.
Reference:
1 . Chambers JD, Bethwaite B, Diamond NT, Peachey T, Abramson D, Petrou S, Thomas EA (2012) Parametric computation predicts a multiplicative interaction between synaptic strength parameters that control gamma oscillations. 6:53. Epub 2012 Jul 24. Front Comput Neurosci. 6:53:1-12 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon; Synapse; Channel/Receptor; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-6 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I A; I K; I K,leak; I K,Ca; I Calcium; I_K,Na;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Parameter sensitivity;
Implementer(s): Thomas, Evan [evan at evan-thomas.net]; Chambers, Jordan [jordandchambers at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-6 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; I A; I K; I K,leak; I K,Ca; I Calcium; I_K,Na; Gaba; Glutamate;
These are the source files to generate the data in Figs 4-6 of the paper:

"Parametric computation predicts a multiplicative interaction between synaptic
strength parameters that control gamma oscillations" by Jordan D. Chambers,
Blair Bethwaite, Neil T. Diamond, Tom Peachey, David Abramson,
Steve Petrou1 and Evan A. Thomas. 
Frontiers in Computational Neuroscience (2012) 6:1

1) Build the neuron load library from the command line
nrnivmodl mod

2) The python script des2runs.py reads the design file created by Nimrod,
creates a PBS run file which passes the parameters to neuron model and 
submits the run to the PBS queueing system.

3) The matlab script EFPgen.m reads the output files and generates local 
field traces in matlab format (EFP.mat)

4) The matlab script EFP2PSD.m reads the EPF data and creates power spectra
in matlab format (PSD.mat)

5) The matlab script singleoutput.m reads the PSD file and generates the
output used in figs 4, 5 and 6 of the paper.

You will almost certainly need to modify these scripts to work in your
environment.

Any questions? Email me evan@evan-thomas.net