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]
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 singleoutput


freq = 0:0.1:100;
load PSD1000ms
% load 'D:\Stuff\Dropbox\Papers\Parametric computation and TC oscillations\Fig 5&6 - effects analysis\PSD'

N = length(PSD);
gindx = 30<=freq & freq<=80;
gfreq = freq(gindx);

gStrength    = zeros(N, 1);
gPeakF    = zeros(N, 1);


designfile = 'FRBGamma.des';
for i=1:N
	G = PSD(i, gindx);
    gStrength(i) = sum(G);
	[~, x] = max(G);
	gPeakF(i) = gfreq(x);
end

gStrength(gStrength>1e6) = mean(gStrength);
save gStrength.csv gStrength -ASCII
save gPeakF.csv gPeakF -ASCII

titlestr   = 'Peak Frequency';
figure
hist(gPeakF)
xlabel('Hz');
title('Frequency distribution')

% saveas(gcf, [titlestr ' - qqplot.png']);
ND(designfile, gPeakF, [titlestr ' - effects'])

titlestr   = '\Gamma power';
ND(designfile, gStrength, titlestr)

title(titlestr)
% saveas(gcf, [titlestr ' - qqplot.png']);

set(findall(0, '-property', 'fontweight'), ...
    'fontweight', 'bold', ...
    'fontsize', 14)
set(get(0, 'children'), 'color', 'white')

function cyclecolour(lh, lasth, fh)
% Cycle the colour of a line to the next one, useful for adding lines to
% plots and cycling the color.

if nargin<3, fh = gcf; end

if isempty(lasth), return, end

c = get(lasth, 'markeredgecolor');

cm = get(fh, 'DefaultAxesColorOrder');

indx = find(c(1)==cm(:,1) & c(2)==cm(:,2) & c(3)==cm(:,3));

indx = mod(indx, length(cm)) + 1;

set(lh, 'markeredgecolor', cm(indx,:))
function [Nfact UserNames defconts desarray] = readdesign(dfname)
df = fopen(dfname, 'r');

Nfact = fscanf(df, '%d factors');

while true
	l = fgetl(df);
	if strcmp(l, 'User''s names:')==1
		break
	end
end
UserNames = textscan(df, '%s', Nfact);
UserNames = UserNames{1};


dcstr = 'defining contrasts:';
ndcstr = length(dcstr);
while true
	l = fgetl(df);
	if length(l)<ndcstr, continue, end
	if strcmp(l(end-ndcstr+1:end), dcstr)==1
		Ndef = sscanf(l, '%d');
		break
	end
end
crap = textscan(df, '%s', Ndef);
defconts = cell(Ndef, 1);
for i=1:Ndef
	s = crap{1}{i};
	defconts{i} = s(1:2:end);
end

fgetl(df);
fgetl(df);
desarray = textscan(df, '%d');%, 2^(Nfact-Ndef)*Nfact);
desarray = reshape(desarray{1}, Nfact, 2^(Nfact-Ndef))';

fclose(df);

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