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 10:667-82 [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;
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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
                            
/*  Author: Johannes Luthman

This is the simulation start file, with setting of run-specific variables.

Hoc files called from here:
- DCN_morph.hoc: creates the neuron morphology
- DCN_mechs.hoc: adds ion channels and other biophysics
- DCN_run.hoc: steps through the simulation
- DCN_recording.hoc: contains procedures (called from DCN_run.hoc) for recording
  and saving data.
*/

strdef strFilePrefix, strTemp

// Set (optionally) prefix for the output file names. The following will be suffixed
// automatically: compartment recorded from, number of seconds simulated, ".dat".
strFilePrefix = ""

randomiserSeed = 1

runTime = 1000 // ms

/* Set synaptic input rates (Hz). The default of the model is to receive
40 Hz inhibitory and 20 Hz excitatory input.*/
inhibitoryHz = 40
excitatoryHz = 20

// Set whether to record membrane potential and current traces, and if so, during
// which intervals to record. (The somatic spike times are saved by default)
// For each interval, give the number of milliseconds into the simulation to start
// and stop the recording.
// A non-instantiated vector error occurs if nExtraVars = 0, so to not record any
// traces, set tTraceStop[0] = 0.
nStepsSaveTrace = 1
double tTraceStart[nStepsSaveTrace]
double tTraceStop[nStepsSaveTrace]
tTraceStart[0] = 200
tTraceStop[0] = 500

vInit = -70
dt = 0.025
secondorder = 1

// Set the recording interval.
recInterval = 0.100 // ms

// Set the number of excitatory and inhibitory (Purkinje cell) synapses.
EXCSOMASYNAPSES = 50
EXCDENDSYNAPSES = 100
EXCTOTALSYNAPSES = EXCSOMASYNAPSES + EXCDENDSYNAPSES
INHSOMASYNAPSES = 50
INHDENDSYNAPSES = 400
INHTOTALSYNAPSES = INHSOMASYNAPSES + INHDENDSYNAPSES

// Set convergence of Purkinje cells to the DCN.
PCtoDCNconvergence = 450
nDCNsynsPerPC = int(INHTOTALSYNAPSES / PCtoDCNconvergence)

// Set parameters of the synaptic inputs.
// If noise below is set to 0 (to get fully regular inputs), and the number of GABA inputs 
// (PCtoDCNconvergence) is set to less than 450, then NEURON sometimes gives this error:
// "internal error: Source delay is > NetCon delay"
// The problem is corrected by setting noise to 1e-19 (1e-20 brings back the
// error).
noiseFractionExcSyn = 1 // max=1
noiseFractionInhSyn = 1 // min=1e-19 if PCtoDCNconvergence<450 (see explanation above), max=1

// Set the gamma distribution of the inputs.
// The default value of 3 for gammaOrderPC is based on the values of 2.8 (for patterns)
// and 3.4 (whole train) in Shin SL, Rotter S, Aertsen A, De Schutter E (2007)
// Stochastic description of complex and simple spike firing in cerebellar Purkinje cells.
// Eur J Neurosci 25:785-794.
gammaOrderExc = 3
gammaOrderPC = 3

// refractory periods of the inputs (ms)
refractoryPeriodExc = 1
refractoryPeriodPC = 2

// set useGABAsyndep to 1 (default) to use mech DCNsynGABA.mod, 0 to use DCNsyn.mod
// to instantiate the GABA synapses, with the former giving short-term
// depression as in Shin et al 2007 (PLOSone issue 5, e485)
useGABAsyndep = 1

// Define the length of the synaptic transmission delay (ms) in the PC-DCN synapse
// and its jitter (standard deviation).
gabaTransDelay = 2
gabaTransDelaySD = 0

// Set the temperature of the simulation. The model has been titrated to reproduce
// in vivo like firing at celsius = 37.0 (default), while the original GENESIS
// DCN model was constructed with temp = 32 deg celsius.
celsius = 37.0
TempOrigDCN = 32.0

// Temperature adjustments

// TempAnchisi = the temperature in the middle of the given range of room temperature
// recording in Anchisi D, Scelfo B, Tempia F (2001) Postsynaptic currents in deep
// cerebellar nuclei. J Neurophysiol 85:323-331.
TempAnchisi = 24.0

Q10channelGating = 3.0 // Middle of experimentally shown range (2-4) of ion channel gating, 
        // see Hille 3rd ed (2001), p.51.
Q10synapseGating = 2.0 // (Silver et al., 1996; Otis and Mody, 1992) Synaptic Q10s are given
        // for GABA and excitatory synapses in Otis and Mody (1992), and Silver et al. (1996),
        // respectively (full references below), with both giving Q10s in the region of 2.
Q10conductances = 1.4 // The middle of the range (1.2-1.5) given in Hille 3rd ed (2001) 
        // for ion channel conductances (p.51). Also, eg Milburn et al (1995) 
        // Receptors Channels 3:201-211: “The conductance increases steeply with temperature,
        // with Q10 ranging from 1.4 to 1.5”. However, also note Cao XJ, Oertel D (2005) 
        // J Neurophysiol 94:821-832. They get the results that some conductances have a 
        // Q10 of 2 while other channel conductances don’t change at all by changing 
        // temp (Q10=1).
Q10CaConc = 2.0 // Guesswork: I assume that calcium concentration changes due to a
        // combination of diffusion (Q10 of ca 1.4) and pumping action (Q10 of enzymatic
        // reactions = ca 3)

QdTsynapseTausAnchisi = Q10synapseGating^((celsius - TempAnchisi) / 10.0)
QdTconductanceAnchisi = Q10conductances^((celsius - TempAnchisi) / 10.0)
QdTchannelGating = Q10channelGating^((celsius - TempOrigDCN) / 10.0)
QdTsynapseTaus = Q10synapseGating^((celsius - TempOrigDCN) / 10.0)
QdTconductances = Q10conductances^((celsius - TempOrigDCN) / 10.0)
QdTCaConc = Q10CaConc^((celsius - TempOrigDCN) / 10.0)


// Synaptic conductances

gGABA = 1600e-6*QdTconductances
tauRiseGABA = 0.25 / QdTsynapseTaus // From Dieter Jaeger's code cn6c_const_dj4.g
tauFallGABA = 5.1 / QdTsynapseTaus // Telgkamp P, Padgett DE, Ledoux VA, Woolley CS, Raman IM (2004)
        // Maintenance of high-frequency transmission at purkinje to cerebellar nuclear 
        // synapses by spillover from boutons with multiple release sites. Neuron 41:113-126.

// For the following excitatory synaptic conductances, I'm using the high input gain
// level of Steuber, V., N. W. Schultheiss, et al. (2010). "Determinants of synaptic 
// integration and heterogeneity in rebound firing explored with data-driven models of 
// deep cerebellar nucleus cells." J Comput Neurosci.
// [= AMPA 200 pS, NMDA peak conductance (fast + slow) 172 pS (114+57)]
// The time constants are from Anchisi D, Scelfo B, Tempia F (2001) Postsynaptic currents
// in deep cerebellar nuclei. J Neurophysiol 85:323-331.
gAMPA = 200e-6*QdTconductanceAnchisi
tauRiseAMPA = 0.5 / QdTsynapseTausAnchisi
tauFallAMPA = 7.1 / QdTsynapseTausAnchisi
gfNMDA = 114e-6*QdTconductanceAnchisi
tauRisefNMDA = 5 / QdTsynapseTausAnchisi
tauFallfNMDA = 20.2 / QdTsynapseTausAnchisi
MgFactorfNMDA = 0.002
gammafNMDA = 0.109
gsNMDA = 57e-6*QdTconductanceAnchisi
tauRisesNMDA = 5 / QdTsynapseTausAnchisi
tauFallsNMDA = 136.4 / QdTsynapseTausAnchisi
MgFactorsNMDA = 0.25
gammasNMDA = 0.057

// Passive electrical parameters.
RA = 235.3 // ohm * cm
CM = 1.57 // microfarad / cm2
CMMYEL = CM/100
PASSCOND = 2.81e-5*QdTconductances // S/cm2  passive conductance
PASSCONDMYEL = PASSCOND / 2.81 // passive conductance of the axon
SHELLTHICK = 0.2 // micrometers, the thickness of the calcium-containing shell 
        // defined by CaConc.mod.

// Reversal potentials in mV
SodiumRevPot = 71
PotassiumRevPot = -90
GABARevPot = -75
ExcitSynRevPot = 0
hRevPot = -45
TNCrevPot = -35


// Non-synaptic channel conductances

gNaFsoma = 2.5e-2*QdTconductances
gNaFaxHill = 2*gNaFsoma
gNaFaxIniSeg = 2*gNaFsoma
gNaFpDend = 0.4*gNaFsoma
gNaPsoma = 8e-4*QdTconductances

gfKdrsoma = 1.5e-2*QdTconductances
gfKdraxHill = 2*gfKdrsoma
gfKdraxIniSeg = 2*gfKdrsoma
gfKdrpDend = 0.6*gfKdrsoma

gsKdrsoma = 1.25e-2*QdTconductances
gsKdraxHill = 2*gsKdrsoma
gsKdraxIniSeg = 2*gsKdrsoma
gsKdrpDend = 0.6*gsKdrsoma

gSKsoma = 2.2e-4*QdTconductances
gSKpDend = 0.3*gSKsoma
gSKdDend = 0.3*gSKsoma

permCaLVAsoma = 1.77e-5*QdTconductances
permCaLVAdend = 2*permCaLVAsoma

permCaHVAsoma = 7.5e-6*QdTconductances
permCaHVAdend = permCaHVAsoma / 1.5

tauCaConcSoma = 70/QdTCaConc
kCaCaConcSoma = 3.45e-7
kCaCaConcDend = 1.04e-6

gHsoma = 2e-4*QdTconductances
gHpDend = 2*gHsoma
gHdDend = 3*gHsoma

gTNCsoma = 3e-5*QdTconductances
gTNCaxHill = 3.5e-5*QdTconductances
gTNCaxIniSeg = 3.5e-5*QdTconductances
gTNCpDend = 0.2*gTNCsoma

load_file("DCN_morph.hoc")
load_file("DCN_mechs.hoc")
load_file("DCN_run.hoc")

//quit()

     /* /////////////////////////////////////////////////////
     ///                REFERENCES                        ///
     ////////////////////////////////////////////////////////

@@@ Excitatory inputs @@@
(Gauck and Jaeger, 2003) say:
"The mean activation rate of excitatory inputs was set to 20 Hz
in accordance with in vivo recordings (Eccles et al., 1972; Cazin et al.,
1980; van Kan et al., 1993; Gamlin and Clarke, 1995; Matsuzaki and
Kyuhou, 1997). No difference between mossy and climbing fiber inputs
has been described in the DCN, and we simulate only one homogenous
group of excitatory inputs."

@@@ Inhibitory inputs (from Purkinje cells) @@@
(Savio and Tempia, 1985): spontaneous firing of single spikes = 36.15/s (± 17.04 SD)
in awake rats. (wistar 180-240 grams, a size I believe corresponds to almost adult size
(Stratton et al., 1988) gives a spontaneous simple spike firing rate of normal awake
rats of 35 ± 5.1 spikes/sec, (with SE), and “average complex spike rate, 1.3 spikes/sec”. The age was P20-28.
(LeDoux and Lorden, 2002) give a mean of ca 41 Hz SS frequency in 19.5±0.8 days old awake rats.

Max PC firing rate: 260 Hz in axonal recordings:
    Monsivais P, Clark BA, Roth A, Hausser M (2005) Determinants of action potential propagation
    in cerebellar Purkinje cell axons. J Neurosci 25:464-472.

@@@ Other references in the code @@@
Gauck V, Jaeger D (2003) The contribution of NMDA and AMPA conductances to the control
    of spiking in neurons of the deep cerebellar nuclei. J Neurosci 23:8109-8118.
LeDoux MS, Lorden JF (2002) Abnormal spontaneous and harmaline-stimulated Purkinje cell
    activity in the awake genetically dystonic rat. Exp Brain Res 145:457-467.
Savio T, Tempia F (1985) On the Purkinje cell activity increase induced by suppression
    of inferior olive activity. Exp Brain Res 57:456-463.
Stratton SE, Lorden JF, Mays LE, Oltmans GA (1988) Spontaneous and harmaline-stimulated
    Purkinje cell activity in rats with a genetic movement disorder. J Neurosci 8:3327-3336.
Otis TS, Mody I (1992) Modulation of decay kinetics and frequency of GABAA
    receptor-mediated spontaneous inhibitory postsynaptic currents in hippocampal
    neurons. Neuroscience 49:13-32.
Silver RA, Colquhoun D, Cull-Candy SG, Edmonds B (1996) Deactivation and
    desensitization of non-NMDA receptors in patches and the time course of EPSCs
    in rat cerebellar granule cells. J Physiol 493:167-173.

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