Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)

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Accession:154096
"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
Reference:
1 . Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Long-term Synaptic Plasticity; Aging/Alzheimer`s; Deep brain stimulation; Homeostasis;
Implementer(s): Lytton, William [bill.lytton at downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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RowanEtAl2014
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#!/bin/sh
#
# Usage:
# runsim.sh [savepath] [args]

# EXAMPLES:

# Basic run with default parameters from alz.hoc (saved in data/<time>):
#    ./runsim.sh

# Basic run with default alz.hoc params, but saved to a defined path:
#    ./runsim.sh data/testname

# Run with one alternative param:
#    ./runsim.sh data/testname infotriallength=8000

# Run with multiple alternative params (must be wrapped in "{ and }" ):
#    ./runsim.sh data/testname "{infotriallength=8000 segmentlength=1600e3}"

echo "Starting runsim.sh"
echo "savepath = $1"
echo "variable = $2"
echo "args = $@"

MODL_INCLUDE="./mod"

if [ $# -lt 1 ]; then
  savepath="data/`date +%Y-%m-%d_%H-%M-%S`" # Default to saving in 'data/<current_time>';
else
  savepath=$1 # Take first argument as the save path
  variable=$2 # Take second argument as the variable / list of variables to be pre-set
  shift # Remove savepath argument from the list
  shift # Remove variable argument from the list
fi

echo "Saving to $savepath"
# If savepath doesn't exist, create it
if [ ! -d $savepath ]; then mkdir -p $savepath; fi


# Run simulation.
# To pass extra args to nrniv, enter each after a '-c' flag, in the form:
#   ./runsim data/test -c "\"filepath=\"hello\"\" [-c ...]
echo nrniv -dll mod/`arch`/.libs/libnrnmech.so $@ -c \"$variable\" -c \"strdef filepath\" -c \"filepath=\\\"$savepath\\\"\" sim.hoc
nrniv -dll mod/`arch`/.libs/libnrnmech.so $@ -c "$variable" -c "strdef filepath" -c "filepath=\"$savepath\"" sim.hoc


# Make graphs
python plot.py $savepath activity noinhib: scale noinhib: deletionscale noinhib #: raster: power  # Don't plot raster, power or info by default as they take a long time
#python plot.py $savepath all noinhib

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