LGMD impedance (Dewell & Gabbiani 2019)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:256024
"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration."
Reference:
1 . Dewell RB, Gabbiani F (2019) Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony. J Neurophysiol [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Locust Lobula Giant Movement Detector (LGMD) neuron;
Channel(s): I h; I M;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration; Membrane Properties;
Implementer(s): Dewell, Richard Burkett [dewell at bcm.edu];
Search NeuronDB for information about:  I M; I h;
#!/bin/bash
# this shell script sets the search path for NEURON to include the following directories 
# and then opens the special executable generated by nrnivmodl

# --- The following 3 lines setup the nrn and python paths. modify and (un)commented as needed ---
export PYTHONPATH=/Applications/NEURON-7.7/nrn/lib/python #added by NEURON installer
export PYTHONHOME="/Library/Frameworks/Python.framework/Versions/2.7" #added by NEURON installer
export LD_LIBRARY_PATH="/Library/Frameworks/Python.framework/Versions/2.7/lib:$LD_LIBRARY_PATH" #added by NEURON installer


currwd=`pwd`	# store the current working directory before changing it

scriptpath=$0

nrndir=${scriptpath%/*}
modelfile=$1

NRNBASE=$nrndir
execdir="$nrndir/x86_64"

echo "Setting NRNBASE to: $NRNBASE" 
echo "Using nrn executable: $execdir"
echo "Using model init file: $modelfile" 

export NRNBASE

# speficy path for local nrn files
export HOC_LIBRARY_PATH=$NRNBASE:$NRNBASE/library

cd ${NRNBASE}

# check if x86_64 directory exists
if [[ ! -d ${execdir} ]]; then
	if [[ -d ./mods ]]; then
		# create new nrn special executable and run init.hoc
		echo "Executable directory 'x86_64' not found. Creating new nrn executable with nrnivmodl"
		nrnivmodl ./mods
	else
		execdir="$nrngui"
	fi
# 	cd ${currdir}
fi

# run special and load libinit.hoc
if [[ -d ${execdir} ]]; then
	if test $# = 0; then
		$execdir/special ./libinit.hoc -
	else
		$execdir/special ./libinit.hoc -c "load_file(\"$modelfile\")" -
	fi
else
	echo "Neuron executable not found.\n"
fi

cd ${currwd}