A model of optimal learning with redundant synaptic connections (Hiratani & Fukai 2018)

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Accession:225075
This is a detailed neuron model of non-parametric near-optimal latent model acquisition using multisynaptic connections between pre- and postsynaptic neurons.
Reference:
1 . Hiratani N, Fukai T (2018) Redundancy in synaptic connections enables neurons to learn optimally. Proc Natl Acad Sci U S A 115:E6871-E6879 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L2/6 pyramidal intratelencephalic cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity;
Implementer(s): Hiratani,Naoki [N.Hiratani at gmail.com];
Search NeuronDB for information about:  Neocortex V1 L2/6 pyramidal intratelencephalic cell;
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HirataniFukai2018
data
README.html
ca.mod *
cad.mod *
caL3d.mod *
CaT.mod
exp2synNMDA.mod
h.mod *
HH2.mod *
kca.mod *
kir.mod *
km.mod *
kv.mod *
na.mod *
L23.hoc
libcell.py
md_readout.py
neuron_simulation.py
screenshot.png
                            
COMMENT
T-type Ca channel 
ca.mod to lead to thalamic ca current inspired by destexhe and huguenrd
Uses fixed eca instead of GHK eqn
changed from (AS Oct0899)
changed for use with Ri18  (B.Kampa 2005)
ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX it
	USEION ca READ eca WRITE ica
	RANGE m, h, gca, gbar
	RANGE minf, hinf, mtau, htau, inactF, actF
	GLOBAL  vshift,vmin,vmax, v12m, v12h, vwm, vwh, am, ah, vm1, vm2, vh1, vh2, wm1, wm2, wh1, wh2
}

PARAMETER {
	gbar = 0.0008 (mho/cm2)	: 0.12 mho/cm2
	vshift = 0	(mV)		: voltage shift (affects all)

	cao  = 2.5	(mM)	        : external ca concentration
	cai		(mM)
						 
	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)

	v12m=50         	(mV)
	v12h=78         	(mV)
	vwm =7.4         	(mV)
	vwh=5.0         	(mV)
	am=3         	(mV)
	ah=85         	(mV)
	vm1=25         	(mV)
	vm2=100         	(mV)
	vh1=46         	(mV)
	vh2=405         	(mV)
	wm1=20         	(mV)
	wm2=15         	(mV)
	wh1=4         	(mV)
	wh2=50         	(mV)


}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
	PI	= (pi) (1)
} 

ASSIGNED {
	ica 		(mA/cm2)
	gca		(pS/um2)
	eca		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states
        gca = gbar*m*m*h
	ica = gca * (v - eca)
} 

LOCAL mexp, hexp

PROCEDURE states() {
        trates(v+vshift)      
        m = m + mexp*(minf-m)
        h = h + hexp*(hinf-h)
	VERBATIM
	return 0;
	ENDVERBATIM
}


PROCEDURE trates(v) {  
                      
        LOCAL tinc
        TABLE minf, mexp, hinf, hexp
	DEPEND dt	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable == 1

        tinc = -dt 

        mexp = 1 - exp(tinc/mtau)
        hexp = 1 - exp(tinc/htau)
}


PROCEDURE rates(v_) {  
        LOCAL  a, b

	minf = 1.0 / ( 1 + exp(-(v_+v12m)/vwm) )
	hinf = 1.0 / ( 1 + exp((v_+v12h)/vwh) )

	mtau = ( am + 1.0 / ( exp((v_+vm1)/wm1) + exp(-(v_+vm2)/wm2) ) ) 
	htau = ( ah + 1.0 / ( exp((v_+vh1)/wh1) + exp(-(v_+vh2)/wh2) ) ) 
}


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