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
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alz.hoc
autotune.hoc *
basestdp.hoc *
batch.hoc *
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batchcommon
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clusterrun.sh
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//  $Header: /usr/site/nrniv/simctrl/hoc/RCS/local.hoc,v 1.15 2003/02/13 15:32:06 billl Exp $
//
//  This file contains local modifications to nrnoc.hoc and default.hoc
//
//  Users should not edit nrnoc.hoc or default.hoc.  Any local 
//  changes to these files should be made in this file.

// ------------------------------------------------------------
//* MODIFICATIONS TO NRNOC.HOC
// The procedures declared here will overwrite any duplicate
// procedures in nrnoc.hoc.
// ------------------------------------------------------------

//*MODIFICATIONS TO DEFAULT.HOC
//
// Vars added here may not be handled properly within nrnoc.hoc
//------------------------------------------------------------

//** String defaults

//** Simulation defaults

long_dt     = .001      // msec 

objref sfunc,tmpfile
sfunc = hoc_sf_   // needed to use is_name()
tmpfile = new File()  // check for existence before opening a user's local.hoc file

proc write_comment () {
  tmpfile.aopen("index")
  tmpfile.printf("%s\n",$s1)
  tmpfile.close()  
}

func asin () { return atan($1/sqrt(1-$1*$1)) }
func acos () { return atan(sqrt(1-$1*$1)/$1) }

objref mt[2]
mt = new MechanismType(0)
proc uninsert_all () { local ii
  forall for ii=0,mt.count()-1 {
    mt.select(ii)
    mt.selected(temp_string_)
    if (strcmp(temp_string_,"morphology")==0) continue
    if (strcmp(temp_string_,"capacitance")==0) continue
    if (strcmp(temp_string_,"extracellular")==0) continue
    if (sfunc.substr(temp_string_,"_ion")!=-1) continue
    mt.remove()
    // print ii,temp_string_
  }
}

condor_run = 0  // define for compatability

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

References and models cited by this paper

References and models that cite this paper

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   JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) [Model]

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   Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

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Eguchi A, Neymotin SA and Stringer SM (2014) Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity 8:16. doi: Front. Neural Circuits 8:16 [Journal]

   Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014) [Model]

Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW (2013) Reinforcement learning of 2-joint virtual arm reaching in a computer model of sensorimotor cortex Neural Computation 25(12):3263-93 [Journal] [PubMed]

   Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013) [Model]

Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW (2015) Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model Neural Computation 27(4):898-924 [Journal] [PubMed]

   Neuronal dendrite calcium wave model (Neymotin et al, 2015) [Model]

Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [Journal] [PubMed]

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

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