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]
Citations  Citation Browser
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
26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
    Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal

kca.mod

Calcium-dependent potassium channel
Based on
Pennefather (1990) -- sympathetic ganglion cells
taken from
Reuveni et al (1993) -- neocortical cells

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
	
ENDCOMMENT

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

NEURON {
	SUFFIX kca
	USEION k READ ek WRITE ik
	USEION ca READ cai
	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL Ra, Rb, caix
	GLOBAL q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

PARAMETER {
	gbar = 10   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
	cai  		(mM)
	caix = 1	
									
	Ra   = 0.01	(/ms)		: max act rate  
	Rb   = 0.02	(/ms)		: max deact rate 

	dt		(ms)
	celsius		(degC)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)
} 


ASSIGNED {
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ninf
	ntau 		(ms)	
	tadj
}
 

STATE { n }

INITIAL { 
	rates(cai)
	n = ninf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)
} 

LOCAL nexp

DERIVATIVE states {   :Computes state variable n 
        rates(cai)      :             at the current v and dt.
        n' =  (ninf-n)/ntau

}

PROCEDURE rates(cai(mM)) {  

        

        a = Ra * cai^caix
        b = Rb

        tadj = q10^((celsius - temp)/10)

        ntau = 1/tadj/(a+b)
	ninf = a/(a+b)

 
:        tinc = -dt * tadj
:        nexp = 1 - exp(tinc/ntau)
}