Ventromedial Thalamocortical Neuron (Bichler et al 2021)

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Accession:266989
"Biophysical computer modeling of a thalamic neuron demonstrated that an increase in rebound spiking can also be accounted for by a decrease in the M-type potassium current. Modeling also showed that an increase in sag with hyperpolarizing steps found after 6-OHDA treatment could in part but not fully be accounted for by the decrease in M-type current. These findings support the hypothesis that homeostatic changes in BGMT neural properties following 6-OHDA treatment likely influence the signal processing taking place in the BG thalamocortical network in Parkinson's disease."
Reference:
1 . Bichler EK, Cavarretta F, Jaeger D (2021) Changes in Excitability Properties of Ventromedial Motor Thalamic Neurons in 6-OHDA Lesioned Mice. eNeuro [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: Thalamus;
Cell Type(s):
Channel(s): I A; I h; I L high threshold; I T low threshold; I K,Ca; I Sodium; I Potassium; I Na,p; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Pathophysiology;
Implementer(s):
Search NeuronDB for information about:  I Na,p; I L high threshold; I T low threshold; I A; I K; I h; I K,Ca; I Sodium; I Potassium;
import edyta_sim
from header import cell, pynwb_io
import numpy as np



def get_fi_curves(out, key, ic_min, ic_max, ic_step, ic_holding):
  ic_stim = np.arange(ic_min, ic_max+ic_step, ic_step)
  
  recording = edyta_sim.sim_batch(cell,
                                  ic_holding,
                                  ic_stim,
                                  ic_stim_delay=5000.0,
                                  ic_stim_dur=2000.0,
                                  vinit=-64)
  
  for i, data in enumerate(recording):
    out.write(((key+"_%g")%abs(ic_stim[i])),
              data[0],
              data[1],
              ("hold=%gnA,stim=%gnA"%(ic_holding, ic_stim[i])))
  print ("done")


  

if __name__ == '__main__':
  import os
  import pickle
  
  gmax_iM = cell.get_km()
  gmax_iH = cell.get_h()
  
  holding_current = {}

  if os.path.exists('ic_64mv.pkl'):
    with open('ic_64mv.pkl', 'rb') as fi:
      holding_current = pickle.load(fi)
  else:

    holding_current['control'] = edyta_sim.get_holding_current(cell, -64.0)
    
    for perc in [0, 0.5]:
      cell.set_km(gmax_iM*perc, 50)
      holding_current['km%g'%perc] = edyta_sim.get_holding_current(cell, -64.0)
      cell.set_km(gmax_iM, 50)

    for perc in [2, 3]:
      cell.set_h(gmax_iH*perc)
      holding_current['ih%g'%perc] = edyta_sim.get_holding_current(cell, -64.0)
      cell.set_h(gmax_iH)

    with open('ic_64mv.pkl', 'wb') as fo:
      pickle.dump(holding_current, fo)




  

    
  out = pynwb_io.My_NWB_Writer("fi_curve.nwb",
                               "fi-curve",
                               "simulation of the Edyta's experiments")


  for key in holding_current.keys():
    
    if key.startswith("km"):
      perc = float(key[2:])
      cell.set_km(gmax_iM*perc, 50)
    elif key.startswith("ih"):
      perc = float(key[2:])
      cell.set_h(gmax_iH*perc)
      
    get_fi_curves(out, key, 0.01, 0.4, 0.01, holding_current[key])
    
    cell.set_km(gmax_iM, 50)
    cell.set_h(gmax_iH)

    
  out.close()

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