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

 Download zip file 
Help downloading and running models
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 (2010) Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. J Comput Neurosci [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;
README for the cerebellar nucleus (CN) neuron model described in the paper:

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.

GENESIS files included:

cn0106c_z15_l01_ax.p 	CN neuron morphology 
cn_chan.g      		functions that create ion channel prototypes
cn_comp.g   		functions that create compartments
cn_fileout.g      	functions that provide output to files
cn_syn.g       		functions that create synapse prototypes
cn_vclamp.g      	functions that create a voltage clamp circuit
cn_AMPAcomps.txt      	list of compartments containing AMPA and NMDA receptors
cn_cip_main.g  		main simulation script for current injection
cn_const.g  		simulation parameters
cn_GABAcomps.txt  	list of compartments containing GABAA receptors
cn_syn_main.g  		main simulation script for synaptic input
cn_vclamp_main.g  	main simulation script for voltage clamp simulations


Note: As most biologically detailed neuron models, this model is under
continuous development, and modifications of it (both in GENESIS and
NEURON) have already been used for follow-up publications by Dieter
Jaeger's and Volker Steuber's teams (Rin et al. and Luthman et al.,
submitted). Please contact us (djaeger@emory.edu and
v.steuber@herts.ac.uk) for information about the latest
updates. Moreover, we hope that future updates by others will be made
available on ModelDB and we would appreciate hearing about them. We
would also like to hear about any new experimental data that could be
used to improve the model.

Usage: Download and extract the archive, and create a subdirectory called data
in the directory that contains all simulation files. There are three
separate main scripts to simulate current injection, voltage clamp and
synaptic input. These three types of simulations are run with:

genesis cn_cip_main.g
genesis cn_vclamp_main.g
genesis cn_syn_main.g

How to reproduce the figures in Steuber et al. (2010):

Figure 1 (b)
run with: genesis cn_cip_main.g  
in cn_const.g, set
GNaPs = 0
Ghs = 1.0
GCaLVAs = 0
spontaneous activity:
cipamp = 0
current injection:
cipamp = 50.0e-12 // 50 pA
ciponset = 3.0
cipdur = 1.5

Figure 1 (c)
run with: genesis cn_cip_main.g  	
in cn_const.g, set
GNaPs = 8.0
Ghs = 1.0
GCaLVAs = 2.0
cipamp = 0.0 - 400.0e-12

Figure 2
run with: genesis cn_cip_main.g  	
in cn_const.g, set
GNaPs = 0
Ghs = 0
GCaLVAs = 0
cipamp = 50.0e-12 
ciponset = 3.0
cipdur = 1.5

Figures 3 and 4
run with: genesis cn_cip_main.g  	
in cn_const.g, set
cipamp = -150.0e-12 
ciponset = 3.0
cipdur = 1.5
Neuron 1 model: 
GNaPs = 2
Ghs = 0.5
GCaLVAs = 3.5
Neuron 2 model: 
GNaPs = 6
Ghs = 0.5
GCaLVAs = 4.5
Neuron 3 model: 
GNaPs = 8
Ghs = 2
GCaLVAs = 1.5

Figure 5
run with: genesis cn_vclamp_main.g
in cn_const.g, set
vcstep = -90e-3 
vconset = 3.0
vcdur = 0.5

Figures 6, 7, 8
run with: genesis cn_cip_main.g  	
in cn_const.g, set cipamp and cipdur as specified in the Figure Legends

Figures 9 and 10 
run with: genesis cn_syn_main.g 
in cn_const.g, set
E_GABA = -80e-3 
ex_rate_d, inhib_rate_d, GNaPs, Ghs, GCaLVAs as
specified in the Figure Legends and panels 
low Gsyn:
G_AMPAd = 5.0e-11 
G_GABAd = 5.0e-11 
intermediate Gsyn:
G_AMPAd = 1.0e-10 
G_GABAd = 1.0e-10 
high Gysn: 
G_AMPAd = 2.0e-10 
G_GABAd = 2.0e-10

Figure 11
run with: genesis cn_syn_main.g 
in cn_const.g, set
E_GABA as specified in the panels
ex_rate_d = 20
inhib_rate_d = 30
G_AMPAd = 1.0e-10 
G_GABAd = 1.0e-10 
inb1onset = 3.0
inb1dur = 0.25
inb1rate = 300
Neuron 2 model: 
GNaPs = 6
Ghs = 0.5
GCaLVAs = 4.5
Neuron 3 model: 
GNaPs = 8
Ghs = 2
GCaLVAs = 1.5

Figures 12 and 13
run with: genesis cn_syn_main.g 
in cn_const.g, set
E_GABA = -90e-3
ex_rate_d and inhib_rate_d as specified in the panels
inb1onset = 3.0
inb1dur = 0.25
inb1rate = 300
low Gsyn:
G_AMPAd = 5.0e-11 
G_GABAd = 5.0e-11 
high Gysn: 
G_AMPAd = 2.0e-10 
G_GABAd = 2.0e-10
Neuron 1 model: 
GNaPs = 2
Ghs = 0.5
GCaLVAs = 3.5
Neuron 3 model: 
GNaPs = 8
Ghs = 2
GCaLVAs = 1.5



Loading data, please wait...