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. Front Comput Neurosci 6:53 [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 L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA 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 L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; I A; I K; I K,leak; I K,Ca; I Calcium; I_K,Na; Gaba; Glutamate;
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FRBGamma
cells
data
hoc
mod
net
README
balanal.hoc *
balcomp.hoc *
cell_templates.hoc *
des2runs.py
EFP2PSD.m
EFPgen.m
evan.hoc
finit.hoc *
fortmap.hoc *
FRBGamm.val
FRBGamma.des
gidcell.hoc *
manage_setup.hoc
singleoutput.m
spkplt.hoc *
synstrengthdef.hoc
                            
function EFPgen


if matlabpool('size')==0
    % 4-5 seems to be optimal for grinding EFP data
    matlabpool 8
end


JORDANROOT = './';
datadir = [JORDANROOT 'data/efs'];


vals = dlmread('FRBGamm.val', ' ', 1, 0);
vals(:,end) = []; % For some reason a column of zeros is added at the end
[N M] = size(vals);
fprintf(1, 'Processing %d datasets\n', N);

% Format string
fstr = char(zeros(1,2*M));
fstr(1:2:end) = '%';
fstr(2:2:end) = 'd';

tmz = 0:0.025:2000;
EFP = zeros(N, length(tmz));

for j=1:N/128
parfor i=((j-1)*128+1):((j)*128)
	idstr = sprintf(fstr, vals(i, :));

	fn = [datadir '/en*_' idstr '.dat.gz'];
    [~, dt, ~, ~] = efs(fn);

    dt = [dt; zeros(length(tmz)-length(dt), 1)];
    EFP(i, :) = dt; %#ok<PFOUS>
end
	save -v7.3
end

tm=tmz;
save -v7.3 EFP tm EFP
		
function [tm dt fail failmsg] = efs(fng)
fail = false;
failmsg = 'well done';
dt = 0; tm = 0;

path = fileparts(fng);
fnl = dir(fng);

if isempty(fnl)
	fail = true;
	failmsg = 'files not found';
	return
end

fn = [path '/' fnl(1).name];
try
	EF = loadgz(fn);
	dt = sum(EF(:,[3 5 7 9 11]),2);
	tm  = EF(:,1);
catch crap
	failmsg = crap.message;
	fail = true;
	return
end

for i=2:length(fnl)
	fn = [path '/' fnl(i).name];
	try
		EF = loadgz(fn);
	catch crap
		failmsg = crap.message;
		fail = true;
		return
	end
	try
		dt = dt + sum(EF(:,[3 5 7 9 11]),2);
	catch crap
		failmsg = crap.message;
		fail = true;
		return
	end
end

function EF = loadgz(fn)
system(['gunzip ' fn]);
EF = load(fn(1:end-3));
system(['gzip ' fn(1:end-3)]);