Molecular layer interneurons in cerebellum encode valence in associative learning (Ma et al 2020)

 Download zip file 
Help downloading and running models
Accession:266578
We used two-photon microscopy to study the role of ensembles of cerebellar molecular layer interneurons (MLIs) in a go-no go task where mice obtain a sugar water reward. In order to begin understanding the circuit basis of our findings in changes in lick behavior with chemogenetics in the go-no go associative learning olfactory discrimination task we generated a simple computational model of MLI interaction with PCs.
Reference:
1 . Ma M, Futia GL, De Souza FM, Ozbay BN, Llano I, Gibson EA, Restrepo D (2020) Molecular layer interneurons in the cerebellum encode for valence in associative learning Nature Communications, accepted
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: Cerebellum; Mouse;
Cell Type(s): Cerebellum Purkinje GABA cell; Cerebellum interneuron stellate GABA cell;
Channel(s):
Gap Junctions:
Receptor(s): AMPA; GabaA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Detailed Neuronal Models;
Implementer(s): Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; Cerebellum interneuron stellate GABA cell; GabaA; AMPA; Gaba; Glutamate;
/
MaEtAl2020
README.html
bkpkj.mod *
cad.mod *
cadiff.mod *
cae.mod *
cap2.mod *
captain.mod *
cat.mod *
cha.mod *
erg.mod *
gkca.mod *
Golgi_Ca_LVA.mod *
Golgi_KA.mod *
Golgi_KV.mod
Golgi_Na.mod *
hpkj.mod *
k23.mod *
ka.mod *
kc3.mod *
kd.mod *
kdyn.mod *
khh.mod *
km.mod *
kpkj.mod *
kpkj2.mod *
kpkjslow.mod *
kv1.mod *
leak.mod *
lkpkj.mod *
myexchanger.mod *
myexchangersoma.mod *
mypump.mod *
mypumpsoma.mod *
nadifl.mod *
narsg.mod *
newnew.mod *
pump.mod *
10480.tmp
2_compartment_template.hoc
full.ses *
lesbos.ses *
mosinit.hoc
mosinit_PC_SC_SminusCNO.hoc
mosinit_PC_SC_SminusSaline.hoc
mosinit_PC_SC_SplusCNO.hoc
mosinit_PC_SC_SplusSaline.hoc
PC_alx.swc
PF_template.hoc
Plot_results.m
SC_morphology.hoc
SC_template.hoc
SC_withoutaxon.swc
screenshot.png
                            
COMMENT
: FORREST MD (2014) Two Compartment Model of the Cerebellar Purkinje Neuron
Longitudinal diffusion of sodium (no buffering)
(equivalent modified euler with standard method and
equivalent to diagonalized linear solver with CVODE )
ENDCOMMENT

NEURON {
	SUFFIX nadifl
	USEION na READ ina WRITE nai, ena
:        USEION Na READ iNa WRITE Nai VALENCE 1
	RANGE D, Nai, Total, neo, tau
}

UNITS {
	(mM) = (milli/liter)
	(um) = (micron)
	FARADAY = (faraday) (coulomb)
	PI = (pi) (1)
}

PARAMETER {
	D = .6 (um2/ms)
         k1buf = 0.01          : 1000                    : 
        k2buf =  0          : 1000                   :   
: the smaller these numbers the less equilibriated these are. 
: 0.001 gets a seperation of 3. though note that the seperation grows over time.  
: 0.0001 see a seperation of 10 after a while
       tau = 5000 (ms)
}

ASSIGNED {
	ina (milliamp/cm2)
	diam (um)
        iNa (milliamp/cm2)
        Total (mM)
        neo (milliamp/cm2)
        ena (mV)
}

STATE {
	nai (mM)
        Nai (mM)
}

INITIAL {
lates()
:	nai = 6
nai = 10
Nai = 10
Total = 20
neo = ina	
}

BREAKPOINT {
	SOLVE conc METHOD sparse
}

KINETIC conc {
        lates()
	COMPARTMENT PI*diam*diam/4 {nai}
	LONGITUDINAL_DIFFUSION D {nai}
~ nai << (-neo/(FARADAY)*PI*diam*(1e4))

:	~ nai << (-ina/(FARADAY)*PI*diam*(1e4))
:        ~ Nai << (-iNa/(FARADAY)*PI*diam*(1e4))
:        ~ nai <-> Nai (k1buf,k2buf)
: Total = nai + Nai + ina + iNa
: ~ nai << ((-ina/(FARADAY)*PI*diam*(1e4))/6)
: ~ nai << ((-ina/(FARADAY)*PI*diam*(1e4))+(10-nai)/0.3)
}


PROCEDURE lates() {
LAG ina BY tau
  neo = lag_ina_tau
if (ena < 70) {ena = 70}
if (nai < 10) {nai = 10}
}

Loading data, please wait...