Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)

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Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.
1 . Rowan MS,Neymotin SA (2013) Synaptic Scaling Balances Learning in a Spiking Model of Neocortex Adaptive and Natural Computing Algorithms, Tomassini M, Antonioni A, Daolio F, Buesser P, ed. pp.20
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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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Long-term Synaptic Plasticity; Learning; STDP; Homeostasis;
Implementer(s): Lytton, William [bill.lytton at]; Neymotin, Sam [samn at]; Rowan, Mark [m.s.rowan at];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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# Usage:
# [savepath] [args]


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

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

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

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

echo "Starting"
echo "savepath = $1"
echo "variable = $2"
echo "args = $@"


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

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/ $@ -c \"$variable\" -c \"strdef filepath\" -c \"filepath=\\\"$savepath\\\"\" sim.hoc
nrniv -dll mod/`arch`/.libs/ $@ -c "$variable" -c "strdef filepath" -c "filepath=\"$savepath\"" sim.hoc

# Make graphs
python $savepath activity noinhib: scale noinhib: raster: power