Recurrent random network with excitatory and inhibitory Leaky IntegrateandFire neurons with currentbased
synapses from the paper “Comparison of the dynamics of neural interactions between currentbased and
conductancebased integrateandfire recurrent networks” written by S.Cavallari, S.Panzeri and A.Mazzoni and
published in Frontiers in Neural Circuits (2014), 8:12. doi:10.3389/fncir.2014.00012. The paper compares the
activity of this currentbased network (i.e. code_CUBN.c) with the activity of a comparable network of LIF
neurons with conductancebased synapses (whose source code is in the “LIF_COBN” folder).
The function code_CUBN.c is a mex source code. You have to compile this routine in Matlab to generate the
mex file (e.g.: code_CUBN.mexw64). Note that you have to include the functions ran1.c and gasdev.c in the
compiling instruction in the Matlab workspace, in the following way:
mex code_CUBN.c ran1.c gasdev.c
After you compiled the function, you can call it as specified in the help.
For more information use the help of the function (and see the example below):
help code_CUBN
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In the following an easy example for setting the arguments to generate the data used in figures 4I; 7 (i.e. LFP)
and 6A (i.e. average firing rate) once you compiled the mex file.
In the Matlab workspace:
1. parameters_CUBN; % to generate the structure net_CUBN with all the parameters of the network
2. simulation_length = 4500; % units: (ms)
3. M = simulation_length/net_CUBN.Dt; % length of the simulation in time steps
4. external_signal_intensity = 2; % units; (spikes/ms)/cell
5. external_signal = ones(M,1) * external_signal_intensity * net_CUBN.Dt;
6. SEED_OU = 1; % positive integer number
7. external_noise = OU_process(M, net_CUBN.Dt, 16, 0.16*net_CUBN.Dt, SEED_OU);
8. INPUT2E = external_signal + external_noise;
9. INPUT2I = INPUT2E;
10. SEED_connections = 2; % positive integer number
11. SEED_poisson = 3; % positive integer number
12. [E2EI,I2EI,eFR,iFR] = code_CUBN(net_CUBN, INPUT2E, INPUT2I, SEED_connections, SEED_poisson);
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Question on how to use the model should be addressed to:
ste.cavallari@gmail.com
Please cite the paper if you use the code.
