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
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;
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 [billl at]; Neymotin, Sam [samn at]; Rowan, Mark [m.s.rowan at];
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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// $Id: setup.hoc,v 1.25 2006/12/26 22:34:47 billl Exp $
// variables normally controlled by SIMCTRL

// load_file("setup.hoc")
strdef simname, filename, output_file, datestr, uname, comment, section, osname
objref tmpfile,nil,graphItem,sfunc
sfunc = hoc_sf_  // from stdlib.hoc
proc chop () { sfunc.left($s1,sfunc.len($s1)-1) }

tmpfile = new File()
simname = "sim"      // helpful if running multiple simulations simultaneously
runnum = 1           // updated at end of run
system("uname -m",uname)  // keep track of type of machine for byte compatibility
system("date +%y%b%d",datestr)
chop(datestr) // may prefer to downcase later
sprint(output_file,"data/%s.%02d",datestr,runnum)  // assumes a subdir called data
if (unix_mac_pc()==1) osname = "Linux" else if (unix_mac_pc()==2) { 
  osname = "Mac" } else if (unix_mac_pc()==3) osname = "PC"
printStep = 0.25 // time interval for saving to vector
xwindows = 0     // can still save but not look without xwindows

// load_file("nrnoc.hoc")

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