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

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

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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 Nat Commun . [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:	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;

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myexchanger.mod *

myexchangersoma.mod *

mypumpsoma.mod *

2_compartment_template.hoc

mosinit_PC_SC_SminusCNO.hoc

mosinit_PC_SC_SminusSaline.hoc

mosinit_PC_SC_SplusCNO.hoc

mosinit_PC_SC_SplusSaline.hoc

PF_template.hoc

SC_morphology.hoc

SC_template.hoc

SC_withoutaxon.swc

TITLE Purkinje Leak Current

: A passive purkinje cell leak current
NEURON {
	SUFFIX lkpkj
	NONSPECIFIC_CURRENT i
	RANGE i, e, g
}
PARAMETER {
	g = 5e-5	(siemens/cm2)  < 0, 1e9 >
	e = -60	(millivolt)
}
ASSIGNED {
	i  (milliamp/cm2)
	v  (millivolt)
}
BREAKPOINT { i = g*(v - e) }

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