STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)

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Accession:144523
"... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..."
Reference:
1 . Luthman J, Hoebeek FE, Maex R, Davey N, Adams R, De Zeeuw CI, Steuber V (2011) STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron Cerebellum 10(4):667-82 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Temporal Pattern Generation; Short-term Synaptic Plasticity;
Implementer(s): Luthman, Johannes [jwluthman at gmail.com];
Search NeuronDB for information about:  I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I K,Ca;
/
LuthmanEtAl2011
readme.txt
CaConc.mod *
CaHVA.mod *
CalConc.mod *
CaLVA.mod *
DCNsyn.mod *
DCNsynGABA.mod *
DCNsynNMDA.mod *
fKdr.mod *
GammaStim.mod *
h.mod *
NaF.mod *
NaP.mod *
pasDCN.mod *
SK.mod *
sKdr.mod *
TNC.mod *
DCN_mechs.hoc
DCN_morph.hoc *
DCN_recording.hoc
DCN_run.hoc
DCN_simulation.hoc
mosinit.hoc
OutputDCN_soma_1s_ap.dat
OutputDCN_soma_1s_time.dat
OutputDCN_soma_1s_trace.dat
                            
This is the readme for the model associated with the paper

Luthman J, Hoebeek FE, Maex R, Davey N, Adams R, De Zeeuw CI, Steuber
V (2011) STD-dependent and independent encoding of input irregularity
as spike rate in a computational model of a cerebellar nucleus neuron
Cerebellum 10(4):667-82

These NEURON simulator files were contributed by Dr J Luthman.  The
NEURON simulator is available for free download from
http://www.neuron.yale.edu

Usage:

Auto-launch from ModelDB after installing NEURON or download and
extract this archive, compile the mod files with mknrndll (mswin, mac)
or nrnivmodl (unix/linux).  Start the simulation with mosinit.hoc by
double clicking (mswin), or dragging and dropping onto nrngui (mac) or
typing "nrngui mosinit.hoc" on the command line (unix/linux).

Once the simulation has started press the DCNrun() button.  After a
minute (~45 seconds on a 2.4 GHz intel core duo p8600 laptop) sample
data files containing spike information and traces will be written to
disk:

OutputDCN_soma_1s_ap.dat, OutputDCN_soma_1s_time.dat
OutputDCN_soma_1s_trace.dat.

where:

1) the *ap.dat file contains the action potential times in
milliseconds
2) the *trace.dat file contains the membrane potential in column 1 and
GABA conductance (sum of the n GABA synapses divided by n) in column
2, both in the time span of 200 to 500 ms (this time span is specified
in DCN_simulation.hoc)
3) the *time.dat file contains the time points corresponding to each
of the values in *trace.dat (ie, a series from 200 to 500 ms).
 
With a little bit of editing, the code can be used to reproduce
figure 2a in the article. Each data point there was created by setting
(all in DCN_simulation.hoc):

1) inhibitoryHz = 60
2) useGABAsyndep = 1 (for the +STD data) and useGABAsyndep = 0 (for
the -STD data)
3) noiseFractionInhSyn: from 0 to 1 in 0.2 increments, each
corresponding to one of the x axis data points. (as explained in the
code, the 0 setting doesn't work in some circumstances, but 1e-19
does...)

The results of the figure were means of 11 seconds, from 4 to 15
seconds into the simulations.

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