GitHub - ModelDBRepository/144027: Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)

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derivs.c		derivs.c
derivs.h		derivs.h
dpoisson.c		dpoisson.c
float2.c		float2.c
float2.h		float2.h
input		input
lib.c		lib.c
lib.h		lib.h
main.c		main.c
network.c		network.c
network.h		network.h
params.c		params.c
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rgauss.c		rgauss.c
rgauss.h		rgauss.h
runge_kutta.c		runge_kutta.c
runge_kutta.h		runge_kutta.h
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<html><pre>
This is the readme for the code for the paper:
London M, Roth A, Beeren L, Hausser M, Latham PE (2010) Sensitivity to
perturbations in vivo implies high noise and suggests rate coding in
cortex. Nature 466:123-7
The network code was supplied by Peter Latham and updated by Tom Morse
for compiling in a modern linux (tested with g++ 4.4.3) by simply
typing "make". You can follow built-in help by running the executable
./theta1d without any arguments.
To run you can create a sample input file by typing:
./theta1d -s > sample_inputfile.txt
You can then edit sample_inputfile.txt as desired and then type
./theta1d sample_inputfile.txt
to run the model. The provided sample takes 10 seconds to run and
should produce graphs such as under matlab/octave commands:
For population averaged rate for excitatory (blue) and inhibitory
cells (red):
load arate;
figure
plot(arate(:,1),arate(:,2))
hold on
plot(arate(:,1),arate(:,3),'r')
<img src="screenshot.png" alt="screenshot">
For the last second of voltage activity of a selected neuron:
load v;
figure
plot(v(9000:10000,1),v(9000:10000,2))
<img src="screenshot_v.png" alt="screenshot_v">
</pre></html>