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 (2009) Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus 20(3):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 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 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 *
                            
COMMENT
IA channel

Reference:

1.	Zhang, L. and McBain, J. Voltage-gated potassium currents in
	stratum oriens-alveus inhibitory neurons of the rat CA1
	hippocampus, J. Physiol. 488.3:647-660, 1995.

		Activation V1/2 = -14 mV
		slope = 16.6
		activation t = 5 ms
		Inactivation V1/2 = -71 mV
		slope = 7.3
		inactivation t = 15 ms
		recovery from inactivation = 142 ms

2.	Martina, M. et al. Functional and Molecular Differences between
	Voltage-gated K+ channels of fast-spiking interneurons and pyramidal
	neurons of rat hippocampus, J. Neurosci. 18(20):8111-8125, 1998.	
	(only the gkAbar is from this paper)

		gkabar = 0.0175 mho/cm2
		Activation V1/2 = -6.2 +/- 3.3 mV
		slope = 23.0 +/- 0.7 mV
		Inactivation V1/2 = -75.5 +/- 2.5 mV
		slope = 8.5 +/- 0.8 mV
		recovery from inactivation t = 165 +/- 49 ms  

3.	Warman, E.N. et al.  Reconstruction of Hippocampal CA1 pyramidal
	cell electrophysiology by computer simulation, J. Neurophysiol.
	71(6):2033-2045, 1994.

		gkabar = 0.01 mho/cm2
		(number taken from the work by Numann et al. in guinea pig
		CA1 neurons)

ENDCOMMENT

UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
}
 
NEURON {
        SUFFIX IA
        USEION k READ ek WRITE ik
        RANGE gkAbar,ik
        GLOBAL ainf, binf, aexp, bexp, tau_b
}
 
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
 
PARAMETER {
        v (mV)
        p = 5 (degC)
        dt (ms)
        gkAbar = 0.0165 (mho/cm2)	:from Martina et al.
        ek = -90 (mV)
	tau_a = 5 (ms)
}
 
STATE {
        a b
}
 
ASSIGNED {
        ik (mA/cm2)
	ainf binf aexp bexp
	tau_b
}
 
BREAKPOINT {
        SOLVE deriv METHOD derivimplicit
        ik = gkAbar*a*b*(v - ek)
}
 
INITIAL {
	rates(v)
	a = ainf
	b = binf
}

DERIVATIVE deriv {  :Computes state variables m, h, and n rates(v)      
		: at the current v and dt.
        rates(v) : required to update inf and tau values
        a' = (ainf - a)/(tau_a)
        b' = (binf - b)/(tau_b)
}
 
PROCEDURE rates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
        LOCAL alpha_b, beta_b
	TABLE ainf, aexp, binf, bexp, tau_a, tau_b  DEPEND dt, p FROM -200
TO 100 WITH 300
	alpha_b = 0.000009/exp((v-26)/18.5)
	beta_b = 0.014/(exp((v+70)/(-11))+0.2)
        ainf = 1/(1 + exp(-(v + 14)/16.6))
        aexp = 1 - exp(-dt/(tau_a))
	tau_b = 1/(alpha_b + beta_b)
        binf = 1/(1 + exp((v + 71)/7.3))
        bexp = 1 - exp(-dt/(tau_b))
}
 
UNITSON


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