Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)

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Accession:256140
"Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. ..."
Reference:
1 . Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A (2019) Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation. PLoS Comput Biol 15:e1006298 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell; Cerebellum interneuron granule GLU cell; Vestibular neuron; Abstract integrate-and-fire leaky neuron;
Channel(s): I K; I Na,t; I L high threshold; I M;
Gap Junctions:
Receptor(s): AMPA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: EDLUT; NEURON; MATLAB;
Model Concept(s): Activity Patterns; Sleep; Long-term Synaptic Plasticity; Vestibular;
Implementer(s): Luque, Niceto R. [nluque at ugr.es];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; Cerebellum interneuron granule GLU cell; AMPA; Gaba; I Na,t; I L high threshold; I K; I M;
// Neural type configuration

// The corresponding table file to this neural type has the same name but
// with .dat extension

// Number of neural state variables (not including time)
// and list of numbers of the tables used to update each neural state variable
// (0 is the first table in the .dat file)
4  0 1 2 3

// initialization values of each state variable
-0.070 0.0 0.0 0.0

// Number of the table used to predict when the neuron fires
4

// Number of the table used to predict when the neuron ends firing
5

// Number of synaptic state variables
// and list of number of each of these neural state variables
// (0 is the first defined state variable)
2  2 3

// Number of tables to load
6

// For each table (one line per table):
// We must specify the number of dimensions and the number of variale
// corresponting to each dimension (number 0 is time, 1 is the first
// declared state variable...) and if interpolantion must be used in that
// dimension:
// 0: table_access_direct (interpolatio not used)
// 1: interp bilinear
// 2: interp linear
// 3: interp linear_ex
// 4: interp linear from 2 different positions
// 5: interp linear form n different positions
// Format: Numer_of_dimensions_of_the_table  State_variable_used_for_the_first_dim interpolation_used_for_the_first_dim  State_var_for_second_dim ...
// (look at tab2cfg to know dimension-correspoding variabes)
5   0 5  2 0  3 0  4 0  1 5
5   0 0  2 0  3 0  4 0  1 0
2   0 0  3 0
2   0 0  4 0
4   2 0  3 0  4 0  1 0
4   2 0  3 0  4 0  1 0


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