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;
from pynwb import NWBHDF5IO
import numpy as np
import efel
from scipy.stats import linregress
import analysis


def get_sag_curve(nwbfile, fmtkey, ic_stim, stim_start=5000, stim_end=7000):
  ii = []
  ss = []

  for _ic_stim in ic_stim:
    trace = analysis.read_trace(nwbfile, fmtkey % abs(_ic_stim), stim_start=stim_start, stim_end=stim_end)

    ii.append(
      _ic_stim
    )
    ss.append(
      100*(1-efel.getFeatureValues([trace], ['sag_ratio2'])[0]['sag_ratio2'][0])
    )


  return np.array(ii).T, np.array(ss).T





  


if __name__ == '__main__':
  io = NWBHDF5IO('sag.nwb', 'r')
  nwbfile = io.read()


  import matplotlib.pyplot as plt
  ii, ss = get_sag(nwbfile, "km0_%g", np.arange(0.05, 0.2, 0.05))
  plt.plot(ii, ss)
  ii, ss = get_sag(nwbfile, "control_%g", np.arange(0.05, 0.2, 0.05))
  plt.plot(ii, ss, 'r')
  ii, ss = get_sag(nwbfile, "ih1.35_%g", np.arange(0.05, 0.2, 0.05))
  plt.plot(ii, ss, 'g')
  plt.show()



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