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

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Accession:147141
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.
Reference:
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
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 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV 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;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Long-term Synaptic Plasticity; Learning; STDP; Homeostasis;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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stdpscalingpaper
batchscripts
mod
README
alz.hoc
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
batchcommon
checkirreg.hoc *
clusterrun.sh
col.dot *
col.hoc *
comppowspec.hoc *
condisconcellfig.hoc *
condisconpowfig.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
e2hubsdisconpow.hoc *
e2incconpow.hoc *
filtutils.hoc *
geom.hoc *
graphplug.hoc *
grvec.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotbatch.sh
plotbatchcluster.sh
powchgtest.hoc *
python.hoc *
pywrap.hoc *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
stdpscaling.hoc
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
Introduction:
-------------
This is the source code for the model which was used in the paper "Synaptic
Scaling Balances Learning in a Spiking Model of Neocortex" by Mark Rowan and
Samuel Neymotin.

To generate data for the figures in the paper, follow the instructions under
'compiling' (below) then run the relevant commands from the following list. Be
aware that some of the experiments will take a couple of days to run and will
produce large amounts of data (> 2GB) due to the very long amounts of time being
simulated (~44 hours).


Figures:
--------
Figure 2 (long run):
./runsim data/onlydeletion "{stdpsim=0 scaling=0 deletionstep=3}"
./runsim data/scalingwithdeletion "{stdpsim=0 scaling=1 deletionstep=3}"

Figure 3 (long run):
./runsim data/onlyscaling "{stdpsim=1 scaling=1 PreDur=160000 PostDur=0 LearnDur=0}"

Figures 4/6/7a/7c (couple of hours):
./runsim data/onlytraining "{stdpsim=1 scaling=0}"

Figures 5/6/7b/7d (couple of hours):
./runsim data/trainingandscaling "{stdpsim=1 scaling=1}"


Creating plots:
---------------
Plots can be created using 'python plot.py <datadir> <plot1>: <plot2>'
(requires various Python packages including matplotlib, numpy, and mtspec).

e.g. to create Figure 3:
  python plot.py data/onlyscaling activity noinhib: scaling noinhib
  
or to create Figure 7:
  python plot.py data/trainingandscaling power




Compiling:
----------
Change to the 'mod' directory and run the command 'mkmod' to build the necessary
.mod files.


Running the sim:
----------------
Execute the simulation directly using ./runsim.sh [savepath] [optional args]

  where [optional args] is a list such as   "{variable=value other_variable=value}"
  e.g. "{strdef simfilename simfilename="stdp_scaling.hoc"}" (which runs STDP sim)


Batch runs:
-----------
To run multiple experiments in a batch on a cluster (e.g. to get results over
various different values of activitytau), create a basic script in batchscripts
and fill in the variable name, values, and optional arguments. (See the scripts
already inside batchscripts for examples). This is currently set up to use a
GridEngine cluster, but you can edit clusterrun.sh to change cluster settings.

To execute:

  batchscripts/<script>

To run all scripts in a directory:

  run-parts batchscripts/<directory>/


Code hierarchy:
---------------
batchscripts/<script>
 |_ batchcommon
   |_ clusterrun.sh
     |_ runsim.sh
       |_ sim.hoc
         |  sim setup files
         |  stdp_scaling.hoc OR alz.hoc  (chosen by setting stdpsim=1/0)

20160921 Updates from the Lytton lab that allow the model to run on
mac OS X.

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