Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)

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Accession:147461
" … Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. … The presented model exhibits properties which are in close agreement with experimental results in working memory. ... "
Reference:
1 . Conde-Sousa E, Aguiar P (2013) A working memory model for serial order that stores information in the intrinsic excitability properties of neurons. J Comput Neurosci 35:187-99 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory;
Implementer(s):
begintemplate Gate_Interneuron


public is_art
public init, topol, geom, biophys
public synlist_P0, synlist_P1, synlist_H, syn_ctrl, x, y, z, position, connect2target
public nclist_P0, nclist_P1, nclist_H  

public soma

objref synlist_P0, synlist_P1, synlist_H  // list of INPUT synapses from various populations
objref nclist_P0, nclist_P1, nclist_H	  // list of netcon's associated with above synapse lists
objref syn_ctrl                           // synapse to control forced (evoked) and stochastic activations

create soma

proc init() {
    topol()
    geom()
    biophys()
    synlist_P0 = new List()
		synlist_P1 = new List()
    synlist_H  = new List()
		nclist_P0  = new List()
		nclist_P1  = new List()
		nclist_H   = new List()		
    synapses()
    x = y = z = 0 // only change via position
}

proc topol() { local i
    soma {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(1, 0, 0, 1)}
}

proc geom() {
    soma {  
        L    = 20
        diam = 20
        nseg = 1 
    }
}

proc biophys() {
    soma {
        //HH channels: iNat and iK
				insert HH2 {
        			gnabar_HH2 = 0.08
	 				gkbar_HH2  = 0.02
	 	  			vtraub_HH2 = -60.0
				}				
				
				//intracellular Ca dynamics
				insert CaIntraCellDyn {
						depth_CaIntraCellDyn = 0.1
						cai_tau_CaIntraCellDyn = 1.0
						cai_inf_CaIntraCellDyn = 50.0e-6
				}
				
				//high-voltage activated calcium current, L-type
				insert iCaL {
						pcabar_iCaL = 3e-5
				}       
				
				//non-specific current dependent on intracellular calcium concentration
				insert iCaAN {
						gbar_iCaAN = 1e-5 
				}
				
				ek = -70.0				
				Ra = 150.0				
				
				insert pas
				g_pas = 3.9e-5
				e_pas = -65.0
    }
}

proc position() { local i
    soma for i = 0, n3d()-1 {
        pt3dchange(i, $1-x+x3d(i), $2-y+y3d(i), $3-z+z3d(i), diam3d(i))
    }
    x = $1  y = $2  z = $3
}

obfunc connect2target() { localobj nc  //$o1 target point process, optional $o2 returned NetCon
    soma nc = new NetCon(&v(1), $o1)
    nc.threshold = 0
    if (numarg() == 2) { $o2 = nc }    // for backward compatibility
    return nc
}

objref syn_
proc synapses() {
		// P inside cluster
		soma syn_ = new ComboSyn(0.5)
		synlist_P0.append(syn_)
		// P outside cluster
		soma syn_ = new ComboSyn(0.5)
		synlist_P1.append(syn_)
		// Control
		soma syn_ctrl = new ExpSyn(0.5)
		syn_ctrl.e   = 0.0
		syn_ctrl.tau = 1.0
}

func is_art() { return 0 }


endtemplate Gate_Interneuron

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