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

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Accession:266851
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.
Reference:
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:
Receptor(s):
Gene(s):
Transmitter(s):
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;
import numpy as np
import sys
import multiprocessing

def chirpForMulti(invar):
    sec_num, loc, filename = invar
    from getCells import HayCell
    pt_cell = HayCell()
    seg = pt_cell.apic[sec_num](loc)
    from neuron import h
    for sec in h.allsec():
        try: sec.uninsert('SK_E2')
        except: pass
    from chirpUtils import applyChirp, getChirp
    amp = 0.0025
    f0, f1, t0, Fs, delay = 0.5, 20, 20, 1000, 5 # for looking at bimodal leading phase response in Hay cell
    I, t = getChirp(f0, f1, t0, amp, Fs, delay)
    print('running chirp on ' + str(seg))
    applyChirp(I, t, seg, pt_cell.soma[0](0.5), t0, delay, Fs, f1, out_file_name=filename)

data = [[0, 0.5, '/u/craig/L5PYR_Resonance/timeDomainOutput/chirpSKE2/apic0_0.5'],
        [2, 0.25, '/u/craig/L5PYR_Resonance/timeDomainOutput/chirpSKE2/apic2_2.5'],
        [14, 0.5, '/u/craig/L5PYR_Resonance/timeDomainOutput/chirpSKE2/apic14_0.5'],
        [36, 0.14, '/u/craig/L5PYR_Resonance/timeDomainOutput/chirpSKE2/apic36_0.14'],
        [36, 0.8, '/u/craig/L5PYR_Resonance/timeDomainOutput/chirpSKE2/apic36_0.8']]

data = tuple(data)

def mp_handler():
    p = multiprocessing.Pool(5)
    p.map(chirpForMulti, data)

if __name__ == '__main__':
    mp_handler()

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