Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)

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Accession:98017
1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development. 2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are beneficial. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa.
Reference:
1 . Edin F, Macoveanu J, Olesen P, Tegnér J, Klingberg T (2007) Stronger synaptic connectivity as a mechanism behind development of working memory-related brain activity during childhood. J Cogn Neurosci 19:750-60 [PubMed]
2 . Edin F, Klingberg T, Stödberg T, Tegnér J (2007) Fronto-parietal connection asymmetry regulates working memory distractibility. J Integr Neurosci 6:567-96 [PubMed]
Citations  Citation Browser
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 U1 L2/6 pyramidal intratelencephalic GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory; Attractor Neural Network;
Implementer(s):
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
/* This program allows you to conduct various single cell experiments, such
* as calculating fI-curves, etc
*
* Author: Fredrik Edin, 2003.
* Address: freedin@nada.kth.se
*/


load_file( "LabCell.hoc" )

objref cl, locvec, exinvec, nspikevec, intervec, shiftvec, wvec
objref samevec, noisevec

cl = new LabCell(0.05, "plot", 1, 1, 1e-5) // $1=dt, $2=plot, $3=stats, $4=celltype, $5=E

/* START: The following vectors specify signalling through synapses.
* Look at functions in class LabCell for further information */
nsyn = 3
exinvec = new Vector( nsyn )
nspikevec = new Vector( nsyn )
intervec = new Vector( nsyn )
shiftvec = new Vector( nsyn-1 )
wvec = new Vector( nsyn )
samevec = new Vector( nsyn )
noisevec = new Vector( nsyn )

exinvec.x[0] = 3
exinvec.x[1] = 16
exinvec.x[2] = 5

noisevec.x[0] = -1
noisevec.x[1] = -1
noisevec.x[2] = -1

start = 150
del = 1000
tStop_PSPtrains = 10000+del

nspikevec.x[0] = 256 * 6 * (tStop_PSPtrains) / 1000
nspikevec.x[1] = 1000 * (tStop_PSPtrains) / 1000
nspikevec.x[2] = 1024 * 3 * (tStop_PSPtrains) / 1000

intervec.x[0] = tStop_PSPtrains / nspikevec.x[0]
intervec.x[1] = tStop_PSPtrains / nspikevec.x[1]
intervec.x[2] = tStop_PSPtrains / nspikevec.x[2]

shiftvec.x[0] = 0
shiftvec.x[1] = 0

samevec.x[0] = -1
samevec.x[1] = -1
samevec.x[2] = -1

wvec.x[0] = 0.0115
wvec.x[1] = 0.03
wvec.x[2] = 0.001
/* END */

/* Use this to find the right PSC charges 
* Adjusts cell potential to be correct value */
//cl.eCell.setLeak(-60, 2)
//cl.eCell.setGNa(0) 
//cl.eCell.setGK(0)

NI = 128
NE = 512

/* The functions below will perform single cell experiments */
//cl.fICurve(0, 10, 21, 1000, 1000, 1, 0.0001) // $1 = Imin, $2 = Imax, $3 = #, $4 = time, $5 = nCell, $6 = exin, $7 = w
//cl.effFICurve( 6.5, 6.5, 2, 0.08, 1.024/NE, NE, 10000, 2 ) // For ECells
//cl.effFICurve( 0, 6.5, 2, 0.0036, 0.48/NI, NI, 1000, 0 ) // For ICells
//cl.PSC(0, 0, 0, 0, 0.01, "plot") //$1 = nmda, $2 = ampa, $3 = gaba, $4 = plot, $5 = disp 
//cl.PSP(0, 0.1, 1) // $1 = exin, $2 = w, $3 = ext
//cl.PSPtrain(2, 0.00277, 1/40, 800, 1)//$1=exin,$2=w,$3=interval,$4=nSpk,$5 = ext 
//cl.PSPtrain2(1, 0.00278, 1000, 10000)//$1=exin,$2=w,$3=rate,$4=tStop 
cl.PSPtrains( 150, 1000, exinvec, intervec, nspikevec, shiftvec, noisevec, wvec, samevec ) // $1=start, $2 = del
//cl.oneSpike()
//cl.iClamp( 2, 1000, "plot" )