Cellular and Synaptic Mechanisms Differentiate Mitral & Superficial Tufted Cells (Jones et al 2020)

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Accession:267013
"To evaluate how... different electrophysiological aspects contributed to spiking of the output MCs and sTCs, we used computational modeling. By exchanging the different cell properties in our modeled MCs and sTCs, we could evaluate each property's contribution to spiking differences between these cell types. This analysis suggested that the higher sensitivity of spiking in sTCs vs. MCs reflected both their larger monosynaptic OSN signal as well as their higher input resistance, while their smaller prolonged currents had a modest opposing effect. Taken together, our results indicate that both synaptic and intrinsic cellular features contribute to the production of parallel output channels in the olfactory bulb."
Reference:
1 . Jones S, Zylberberg J, Schoppa N (2020) Cellular and Synaptic Mechanisms That Differentiate Mitral Cells and Superficial Tufted Cells Into Parallel Output Channels in the Olfactory Bulb. Front Cell Neurosci 14:614377 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb (accessory) mitral cell; Olfactory bulb main tufted cell external;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s):
Search NeuronDB for information about:  Glutamate;
import neuron
from neuron import h
import time
import numpy
from numpy import genfromtxt
import csv

start_time = time.time()
openfile = 'sTC_voltage.hoc'
filename='testspikes.csv'

#define parameters
h('ginputmono=0.00225')
h('ginputpoly=0.00046875')
h('latepolyinput=0.028476563')
h('tau1inputmono=.9')
h('tau2inputmono=7.2')
h('tau1inputpoly=6.2')
h('tau2inputpoly=28.5')
h('membres=0.000108')
h('restV=-56')
h('syn1onset=202')
h('syn2onset=202')
h('syn3onset=202')

h.xopen(openfile)

csv.writer(open(filename,'w',newline='')).writerows(zip(h.volt2))

total_time = (time.time() - start_time)
print('Total time to run ' + str(total_time) + ' seconds')




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