Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)

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This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
1 . Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D (2011) Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. J Comput Neurosci 30:633-58 [PubMed]
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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: Cerebellum;
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I Na,p; I T low threshold; I h;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS;
Model Concept(s): Bursting; Ion Channel Kinetics; Active Dendrites; Detailed Neuronal Models; Intrinsic plasticity; Rate-coding model neurons; Synaptic Integration; Rebound firing;
Implementer(s): Steuber, Volker [v.steuber at herts.ac.uk]; Jaeger, Dieter [djaeger at emory.edu];
Search NeuronDB for information about:  GabaA; AMPA; NMDA; I Na,p; I T low threshold; I h; Gaba; Glutamate;
// genesis
// simulate response of CN model to synaptic input
// Volker Steuber, Nathan Schultheiss, R. Angus Silver, Erik De Schutter
// & Dieter Jaeger (2010). Determinants of synaptic integration and
// heterogeneity in rebound firing explored with data-driven models of
// deep cerebellar nucleus cells. Journal of Computational Neuroscience,
// epub ahead of print.

include cn_const
include cn_chan
include cn_syn
include cn_comp
include cn_fileout

outfilev = "data/cn_v_" @ {simnum} @ "_"
outfilei = "data/cn_i_" @ {simnum} @ "_"
outfilechan = "data/cn_chan_" @ {simnum} @ "_"
outfilesyn = "data/cn_syn_" @ {simnum} @ "_"
outfileitotal = "data/cn_itotal_" @ {simnum}
outfilesyntotal = "data/cn_syntotal_" @ {simnum}

if (!{exists /library})
        create neutral /library
        disable /library

// make the prototypes in the library
ce /library


// read cell morphology from .p file
readcell cn0106c_z15_l01_ax.p {cellpath} -hsolve

// need to add synapses after readcell -hsolve (GENESIS limitation)
ce {cellpath}

// update timetables for synaptic input burst

// set the simulation and output clocks
for (i = 0; {i <= 7}; i = i + 1)
    setclock {i} {dt}
setclock 8 {dtout}
setclock 9 1

// set up Hines solver
silent -1
echo preparing Hines solver
ce {cellpath}
setfield . comptmode 1 chanmode 4 storemode 1 
call . SETUP
echo SOLVE setup done
setmethod 11

// write simulation results to files
write_voltage soma 0

// reset the simulation
echo reset now
echo done

// run the simulation and apply synaptic input
echo applying synaptic input
step {tstop} -time
echo done

echo exiting simulation