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

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Accession:136175
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.
Reference:
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]
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;
Gene(s):
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

// simulation index
int simnum = 1

float PI = 3.14159

int i, j, k
int nextseed
str outfilev, outfilei, outfilechan, outfilesyn
str outfilesyntotal, outfileitotal
str pcincomp, mfincomp
str hstr

str cellpath = "/CN_cell"

// total simulation time
float tstop = 7.5

// simulation time step
float dt = 5.0e-6 

// output time step
float dtout = 1.0e-4

// passive parameters (Steuber et al. 2004)
// assume z-correction factor 1.5 (based on estimated shrinkage of slices after fixation)
float CM = 0.0157
float RMs = 3.556
float RMd = 3.556
float RA = 2.353
float CMmy = {CM}*0.01
float RMax = {RMs}
float RMmy = 10.0

// leakage reversal potentials
float ELEAK = -0.066
float ELEAKax = {ELEAK}

// initialization voltage
float EREST_ACT = -0.07 

// current injection parameters
float cipamp = -150.0e-12
float ciponset = 5.0
float cipdur = 1.5

// voltage clamp parameters
float vcstep = -90e-3 
float vconset = 5.0
float vcdur = 0.5

// temperature - correct this depending on preparation!
float TempC = 32.0 // simulation temperature in centigrade
float TempK = {TempC} + 273.15 // Kelvin
float ZFbyRT = 96480*2/(8.315*{TempK})
float Q10 = 3.0 // from Hille 2001
float TempCchannel = 32.0 // temp for the channel kinetics in cn_chan.g
float QDeltaT = {pow {Q10} {({TempC} - {TempCchannel})/10.0}}

// ion concentrations and Nernst potentials
float CNaO = 150.0 // mM
float CNaI = 10.0 // mM
float CCaO = 2.0 // mM
float CCaI = 50e-6 // mM (= 50nM)
float RbyF = 8.6154e-5
float ENa = {RbyF} * {TempK} * {log {CNaO/CNaI}}
float ECa = {RbyF}/2.0 * {TempK} * {log {CCaO/CCaI}} 
float EK = -0.090 
float Eh = -0.045 
float ETNC = -0.035 // Raman et al. 2000 - could vary between -30mV and -45mV

// initial values for prototype compartments, overwritten by readcell
float soma_d = 50.0e-6
float soma_l = 0.0
float soma_area = {soma_d}*{soma_d}*{PI}
float dend_d = 10.0e-6
float dend_l = 100.0e-6
float dend_area = {dend_l}*{dend_d}*{PI}
float axon_d = 10.0e-6
float axon_l = 100.0e-6
float axon_area = {axon_l}*{axon_d}*{PI}

// initial values for prototype channels, overwritten by make_cn_comps
float Ginit = 1.0

// parameters for tabchannel tables
int tab_calcmode = {LIN_INTERP}
int tab_2dcalcmode = {LIN_INTERP}
int tab_xdivs = 299
int tab_xfills = 300 
int tab_x2dfills = 300
float tab_xmin = -0.15
float tab_xmax = 0.1

// parameters for calcium table	
int tab_ydivs = {tab_xdivs}
float tab_ymin = 0.0
float tab_ymax = 0.01

// channel conductances for soma (s), proximal dendrite (pd), distal dendrite (dd), 
// axon Hillock (axHill) and axon initial segment (axIS)
// NaF - Raman et al. 2000
float GNaFs = 250
float GNaFpd = 100
float GNaFdd = 0.0
float GNaFaxHill = 500
float GNaFaxIS = 500

// fKdr - Surmeier Kv3
float GfKdrs = 150
float GfKdrpd = 90
float GfKdrdd = 0.0
float GfKdraxHill = 300
float GfKdraxIS = 300

// sKdr - Surmeier Kv2
float GsKdrs = 125
float GsKdrpd = 75
float GsKdrdd = 0.0
float GsKdraxHill = 250
float GsKdraxIS = 250

// Sk
float GSks =  2.2
float GSkpd = 0.66
float GSkdd = 0.66

// CaHVA - permeability in m/s, Gauck et al. 2000
float GCaHVAs = 7.5e-8
float GCaHVApd = 5e-8 
float GCaHVAdd = 5e-8 

// tonic non-selective cation current
float GTNCs = 0.3 
float GTNCpd = 0.06 
float GTNCdd = 0 
float GTNCaxHill = 0.35
float GTNCaxIS = 0.35

// rebound conductances - adjust for model Neuron 1-3
// NaP
float GNaPs = 8//6//2
float GNaPpd = 0
float GNaPdd = 0

// h-current - h_slow, inspired by Raman 2000
float Ghs = 2//0.5//2//0.5
float Ghpd = 2*Ghs
float Ghdd = 3*Ghs

// CaLVA - Gauck et al. 2000
float GCaLVAs = 1.5//4.5//3.5
float GCaLVApd = 2*GCaLVAs 
float GCaLVAdd = 2*GCaLVAs

// Ca2+ pool parameters
float shell_thick = 0.2e-6
float catau = 0.07 
float kCas = 3.45477e-7 
float kCad = 1.03643e-6 

// parameters for synapses based on Gauck and Jaeger 2003
// reversal potentials
float E_GABA = -80e-3
float E_AMPA = 0.0
float E_NMDA = 0.0

// synaptic time constants
float tauRise_AMPA = 5.0e-4
float tauFall_AMPA = 7.1e-3
float tauRise_fNMDA = 5.0e-3
float tauFall_fNMDA = 20.2e-3
float tauRise_sNMDA = 5.0e-3
float tauFall_sNMDA = 136.4e-3
float tauRise_GABA = 0.93e-3
float tauFall_GABA = 13.6e-3

// synaptic peak conductances
float G_AMPAd = 1.0e-10//2.0e-10//1.0e-10//5.0e-11
float G_AMPAs = G_AMPAd
float G_GABAd = 1.0e-10//2.0e-10//1.0e-10//5.0e-11
float G_GABAs = G_GABAd
float fNMDA_ratio = 0.57
float sNMDA_fac = 0.5
float G_fNMDAs = G_AMPAs * fNMDA_ratio
float G_sNMDAs = G_fNMDAs * sNMDA_fac
float G_fNMDAd = G_AMPAd * fNMDA_ratio
float G_sNMDAd = G_fNMDAd * sNMDA_fac

// max simulation time to fill synaptic timetables 
float synmaxtime = 10.0 

int no_MF = 100
str MF_infile = "cn_AMPAcomps.txt"
int no_PC = 400
str PC_infile = "cn_GABAcomps.txt"
int MF_seed = 1234567
int PC_seed = 2345678

// synaptic input rates
float ex_rate_d = 20
float inhib_rate_d = 30
float ex_rate_s = {50 * ex_rate_d} 
float inhib_rate_s = {50 * inhib_rate_d}

// onset, duration and rate of excitatory and inhibitory bursts
float exbonset = 0
float exbdur = 0
float exbrate = 0
float inb1onset = 3.0
float inb1dur = 0.25
float inb1rate = 300
float inb2dur = 0
float inb2fac  = 1

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