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

function make_vclamp(path)
str  path

  create  diffamp /Vclamp
  setfield ^   saturation  999.0 \ 
	       gain        0.002   // 1/R  from the lowpass filter input

  create  RC  /Vclamp/lowpass
  setfield ^   R   500.0   \   // ohm
	       C   0.1e-6      // farad; for a tau of 50 us

  create  PID /Vclamp/PID
  setfield ^   gain    1e-6    \   // off
	       tau_i   {dt}   \   // seconds
	       tau_d   {{dt}/4}  \   // seconds
               saturation  0  // off 

  echo connecting voltage clamp circuitry
  addmsg /Vclamp/lowpass /Vclamp PLUS state
  addmsg /Vclamp /Vclamp/PID CMD output
  addmsg /Vclamp /Vclamp/lowpass INJECT x   
  addmsg {path} /Vclamp/PID SNS Vm  
  addmsg /Vclamp/PID {path} INJECT output 

end

function set_vclamp_on

  setfield /Vclamp/PID saturation 50e-9 // amps

end


function set_vclamp_off

  setfield /Vclamp/PID saturation 0

end

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