Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)

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Accession:142199
A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme.
Reference:
1 . Hayut I, Fanselow EE, Connors BW, Golomb D (2011) LTS and FS inhibitory interneurons, short-term synaptic plasticity, and cortical circuit dynamics. PLoS Comput Biol 7:e1002248 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program;
Model Concept(s): Short-term Synaptic Plasticity; Rate-coding model neurons;
Implementer(s): Golomb, David [golomb at bgu.ac.il];
#!/bin/sh
# Dynamics of RLF circuit.

fla=d4
flb=d1
flc=d2
fld=d5

./rlf.ex $fla
./rlf.ex $flb
./rlf.ex $flc
./rlf.ex $fld

echo "-1000.0 0.0" > start.xx
echo "-0.0 0.0" >> start.xx

cp start.xx rlf.col.$fla.xx.MR
awk '{print $1 / 1000.0, $2}' rlf.col.$fla >> rlf.col.$fla.xx.MR
cp start.xx rlf.col.$fla.xx.ML
awk '{print $1 / 1000.0, $3}' rlf.col.$fla >> rlf.col.$fla.xx.ML
cp start.xx rlf.col.$fla.xx.MF
awk '{print $1 / 1000.0, $4}' rlf.col.$fla >> rlf.col.$fla.xx.MF

cp start.xx rlf.col.$flb.xx.MR
awk '{print $1 / 1000.0, $2}' rlf.col.$flb >> rlf.col.$flb.xx.MR
cp start.xx rlf.col.$flb.xx.ML
awk '{print $1 / 1000.0, $3}' rlf.col.$flb >> rlf.col.$flb.xx.ML
cp start.xx rlf.col.$flb.xx.MF
awk '{print $1 / 1000.0, $4}' rlf.col.$flb >> rlf.col.$flb.xx.MF

cp start.xx rlf.col.$flc.xx.MR
awk '{print $1 / 1000.0, $2}' rlf.col.$flc >> rlf.col.$flc.xx.MR
cp start.xx rlf.col.$flc.xx.ML
awk '{print $1 / 1000.0, $3}' rlf.col.$flc >> rlf.col.$flc.xx.ML
cp start.xx rlf.col.$flc.xx.MF
awk '{print $1 / 1000.0, $4}' rlf.col.$flc >> rlf.col.$flc.xx.MF

cp start.xx rlf.col.$fld.xx.MR
awk '{print $1 / 1000.0, $2}' rlf.col.$fld >> rlf.col.$fld.xx.MR
cp start.xx rlf.col.$fld.xx.ML
awk '{print $1 / 1000.0, $3}' rlf.col.$fld >> rlf.col.$fld.xx.ML
cp start.xx rlf.col.$fld.xx.MF
awk '{print $1 / 1000.0, $4}' rlf.col.$fld >> rlf.col.$fld.xx.MF

xmgrace -graph  0 rlf.col.$fla.xx.MR \
        -graph  1 rlf.col.$fla.xx.MF \
        -graph  2 rlf.col.$fla.xx.ML \
        -graph  3 rlf.col.$flb.xx.MR \
        -graph  4 rlf.col.$flb.xx.MF \
        -graph  5 rlf.col.$flb.xx.ML \
        -graph  6 rlf.col.$flc.xx.MR \
        -graph  7 rlf.col.$flc.xx.MF \
        -graph  8 rlf.col.$flc.xx.ML \
        -graph  9 rlf.col.$fld.xx.MR \
        -graph 10 rlf.col.$fld.xx.MF \
        -graph 11 rlf.col.$fld.xx.ML \
        -hdevice EPS -p dyn_all.gr -printfile dyn_all.$fla.eps

/bin/rm start.xx
/bin/rm rlf.col.$fla.xx.MR rlf.col.$fla.xx.ML rlf.col.$fla.xx.MF
/bin/rm rlf.col.$flb.xx.MR rlf.col.$flb.xx.ML rlf.col.$flb.xx.MF
/bin/rm rlf.col.$flc.xx.MR rlf.col.$flc.xx.ML rlf.col.$flc.xx.MF
/bin/rm rlf.col.$fld.xx.MR rlf.col.$fld.xx.ML rlf.col.$fld.xx.MF

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