Dendritic Impedance in Neocortical L5 PT neurons (Kelley et al. 2021)

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We simulated chirp current stimulation in the apical dendrites of 5 biophysically-detailed multi-compartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.
1 . Kelley C, Dura-Bernal S, Neymotin SA, Antic SD, Carnevale NT, Migliore M, Lytton WW (2021) Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons. J Neurophysiology 125:1501-1516 [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:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell;
Channel(s): I h; TASK channel;
Gap Junctions:
Simulation Environment: NEURON; Python; NetPyNE;
Model Concept(s): Impedance;
Implementer(s): Kelley, Craig;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell; I h; TASK channel;
from getCells import HayCell
cell = HayCell()
from neuron import h, gui
from synUtils import *

soma_seg = cell.soma[0](0.5)
stim_segs = [cell.apic[2](0.5), cell.apic[36](0.8)]
ampa_weights = [0.125, 0.065]
nmda_weights = [0.03, 0.05]

start = 200
factor = 2

for i, stim_seg in enumerate(stim_segs):
    SC = ampa_weights[i]
    TP, TP_soma = getTp(stim_seg, soma_seg, start, SC / factor)
    sweepLags(stim_seg, soma_seg, SC / factor, SC / (factor*5), SC / (factor*10), start, TP, 1, outpath='/u/craig/L5PYR_Resonance/timeDomainOutput/Hay/')

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