Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)

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Accession:123815
This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses.
Reference:
1 . Cutsuridis V, Cobb S, Graham BP (2010) Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus 20:423-46 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Temporal Pattern Generation; Learning; STDP; Connectivity matrix; Storage/recall;
Implementer(s): Graham, Bruce [B.Graham at cs.stir.ac.uk]; Cutsuridis, Vassilis [vcutsuridis at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; AMPA; NMDA;
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Hipp_paper_code
Results
Weights
readme.txt
ANsyn.mod *
bgka.mod *
burststim2.mod *
cad.mod *
cagk.mod *
cal.mod *
calH.mod *
car.mod *
cat.mod *
ccanl.mod *
gskch.mod *
h.mod *
hha_old.mod *
hha2.mod *
hNa.mod *
IA.mod *
ichan2.mod *
Ih.mod *
kad.mod *
kap.mod *
Kaxon.mod *
kca.mod *
Kdend.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
Naaxon.mod *
Nadend.mod *
Nasoma.mod *
nca.mod *
nmda.mod *
regn_stim.mod *
somacar.mod *
STDPE2Syn.mod *
axoaxonic_cell17S.hoc *
basket_cell17S.hoc *
bistratified_cell13S.hoc *
burst_cell.hoc *
HAM_SR.ses
HAM_StoRec_par.hoc
HAM_StoRec_ser.hoc
mosinit.hoc
olm_cell2.hoc
pyramidal_cell_14Vb.hoc
ranstream.hoc *
stim_cell.hoc *
                            
TITLE Borg-Graham type generic K-A channel
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)

}

PARAMETER {
	v (mV)
        ek (mV)
	celsius 	(degC)
	gkabar=.01 (mho/cm2)
        vhalfn=-33.6   (mV)
        vhalfl=-83   (mV)
        a0l=0.08      (/ms)
        a0n=0.02    (/ms)
        zetan=-3    (1)
        zetal=4    (1)
        gmn=0.6   (1)
        gml=1   (1)
}


NEURON {
	SUFFIX borgka
	USEION k READ ek WRITE ik
        RANGE gkabar,gka, ik
        GLOBAL ninf,linf,taul,taun
}

STATE {
	n
        l
}

INITIAL {
        rates(v)
        n=ninf
        l=linf
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        linf      
        taul
        taun
        gka
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	gka = gkabar*n*l
	ik = gka*(v-ek)

}


FUNCTION alpn(v(mV)) {
  alpn = exp(1.e-3*zetan*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
  betn = exp(1.e-3*zetan*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION alpl(v(mV)) {
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betl(v(mV)) {
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

DERIVATIVE states { 
        rates(v)
        n' = (ninf - n)/taun
        l' = (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,q10
        q10=3^((celsius-30)/10)
        a = alpn(v)
        ninf = 1/(1 + a)
        taun = betn(v)/(q10*a0n*(1+a))
        a = alpl(v)
        linf = 1/(1+ a)
        taul = betl(v)/(q10*a0l*(1 + a))
}


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