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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;
/
LuqueEtAl2019
EDLUT
Articulo purkinje
CASE_B
src
neuron_model
BufferedState.cpp *
EgidioGranuleCell_TimeDriven.cpp *
EgidioGranuleCell_TimeDriven_GPU.cu *
EventDrivenNeuronModel.cpp *
LIFTimeDrivenModel_1_2.cpp *
LIFTimeDrivenModel_1_2_GPU.cu *
LIFTimeDrivenModel_1_4.cpp *
LIFTimeDrivenModel_1_4_GPU.cu *
NeuronModel.cpp *
NeuronModelTable.cpp *
NeuronState.cpp *
SRMState.cpp *
SRMTableBasedModel.cpp *
SRMTimeDrivenModel.cpp *
TableBasedModel.cpp *
TableBasedModelHF.cpp *
TimeDrivenNeuronModel.cpp *
TimeDrivenNeuronModel_GPU.cu *
TimeDrivenPurkinjeCell.cpp *
Vanderpol.cpp *
VectorBufferedState.cpp *
VectorNeuronState.cpp *
VectorNeuronState_GPU.cpp *
VectorSRMState.cpp *
                            
/***************************************************************************
 *                           VectorNeuronState.cpp                         *
 *                           -------------------                           *
 * copyright            : (C) 2012 by Jesus Garrido and Francisco Naveros  *
 * email                : jgarrido@atc.ugr.es, fnaveros@atc.ugr.es         *
 ***************************************************************************/

/***************************************************************************
 *                                                                         *
 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 3 of the License, or     *
 *   (at your option) any later version.                                   *
 *                                                                         *
 ***************************************************************************/

#include "../../include/neuron_model/VectorNeuronState.h"
#include <string.h>

VectorNeuronState::VectorNeuronState(unsigned int NumVariables, bool isTimeDriven): NumberOfVariables(NumVariables), TimeDriven(isTimeDriven),Is_Monitored(false),Is_GPU(false){
}

VectorNeuronState::VectorNeuronState(unsigned int NumVariables, bool isTimeDriven, bool isGPU): NumberOfVariables(NumVariables), TimeDriven(isTimeDriven),Is_Monitored(false),Is_GPU(isGPU){
}

VectorNeuronState::VectorNeuronState(const VectorNeuronState & OldState): NumberOfVariables(OldState.NumberOfVariables), SizeStates(OldState.SizeStates), TimeDriven(OldState.TimeDriven),Is_Monitored(OldState.Is_Monitored), Is_GPU(OldState.Is_GPU) {

	VectorNeuronStates = new float[GetNumberOfVariables()*GetSizeState()];
	memcpy(VectorNeuronStates, OldState.VectorNeuronStates, GetNumberOfVariables()*GetSizeState()*sizeof(float));

	LastUpdate=new double[GetSizeState()];
	memcpy(LastUpdate, OldState.LastUpdate, GetSizeState()*sizeof(double));

	LastSpikeTime=new double[GetSizeState()];
	memcpy(LastSpikeTime, OldState.LastSpikeTime, GetSizeState()*sizeof(double));
	
	if(!GetTimeDriven()){
		PredictedSpike=new double[GetSizeState()];
		memcpy(PredictedSpike, OldState.PredictedSpike, GetSizeState()*sizeof(double));

		PredictionEnd=new double[GetSizeState()];
		memcpy(PredictionEnd, OldState.PredictionEnd, GetSizeState()*sizeof(double));
	}
}


VectorNeuronState::VectorNeuronState(const VectorNeuronState & OldState, int index): NumberOfVariables(OldState.NumberOfVariables), SizeStates(1), TimeDriven(OldState.TimeDriven),Is_Monitored(OldState.Is_Monitored), Is_GPU(OldState.Is_GPU) {

	VectorNeuronStates = new float[GetNumberOfVariables()];
	for(int i=0; i<GetNumberOfVariables(); i++){
		VectorNeuronStates[i]=OldState.VectorNeuronStates[index*GetNumberOfVariables()+i];
	}

	LastUpdate=new double[1];
	LastUpdate[0]=OldState.LastUpdate[index];

	LastSpikeTime=new double[1];
	LastSpikeTime[0]=OldState.LastSpikeTime[index];
	
	if(!GetTimeDriven()){
		PredictedSpike=new double[1];
		PredictedSpike[0]=OldState.PredictedSpike[index];

		PredictionEnd=new double[1];
		PredictionEnd[0]=OldState.PredictionEnd[index];
	}
}

VectorNeuronState::~VectorNeuronState() {
	delete [] this->VectorNeuronStates;
	delete [] this->LastUpdate;
	delete [] this->LastSpikeTime;
	if (!TimeDriven){
		delete [] this->PredictedSpike;
		delete [] this->PredictionEnd;
	}else{
		delete [] this->InternalSpike;
	}
}

void VectorNeuronState::SetStateVariableAt(int index, int position, float NewValue){
	if(Is_GPU==false){
		this->VectorNeuronStates[index*NumberOfVariables + position] = NewValue;
	}else{
		this->VectorNeuronStates[this->SizeStates*position + index] = NewValue;
	}
}

void VectorNeuronState::IncrementStateVariableAt(int index, int position, float Increment){
	if(Is_GPU==false){
		this->VectorNeuronStates[index*NumberOfVariables + position]+= Increment;
	}else{
		this->VectorNeuronStates[this->SizeStates*position + index]+= Increment;
	}
}

//void VectorNeuronState::IncrementStateVariableAtCPU(int index, int position, float Increment){
//	this->VectorNeuronStates[index*NumberOfVariables + position]+= Increment;
//}

//void VectorNeuronState::IncrementStateVariableAtGPU(int index, int position, float Increment){
//	this->VectorNeuronStates[this->SizeStates*position + index]+= Increment;
//}

void VectorNeuronState::SetLastUpdateTime(int index, double NewTime){
	this->LastUpdate[index] = NewTime;
}

void VectorNeuronState::SetNextPredictedSpikeTime(int index, double NextPredictedTime){
	this->PredictedSpike[index] = NextPredictedTime;
}

void VectorNeuronState::SetEndRefractoryPeriod(int index, double NextRefractoryPeriod){
	this->PredictionEnd[index] = NextRefractoryPeriod;
}

unsigned int VectorNeuronState::GetNumberOfVariables(){
	return this->NumberOfVariables;
}

//float VectorNeuronState::GetStateVariableAt(int index, int position){
//	if(Is_GPU==false){
//		return VectorNeuronStates[index*NumberOfVariables + position];
//	}else{
//		return VectorNeuronStates[this->SizeStates*position + index];
//	}
//}

//float * VectorNeuronState::GetStateVariableAt(int index){
//	return VectorNeuronStates+(index*NumberOfVariables);
//}

//double VectorNeuronState::GetLastUpdateTime(int index){
//	return this->LastUpdate[index];
//}

double VectorNeuronState::GetNextPredictedSpikeTime(int index){
	return this->PredictedSpike[index];
}

//double VectorNeuronState::GetEndRefractoryPeriod(int index){
//	return this->PredictionEnd[index];
//}

unsigned int VectorNeuronState::GetNumberOfPrintableValues(){
	return this->GetNumberOfVariables()+3;
}

double VectorNeuronState::GetPrintableValuesAt(int index, int position){
	if (position<this->GetNumberOfVariables()){
		return this->GetStateVariableAt(index, position);
	} else if (position==this->GetNumberOfVariables()) {
		return this->GetLastUpdateTime(index);
	} else if(GetTimeDriven()==true){
		return -1;
	}else if (position==this->GetNumberOfVariables()+1){
		return this->GetNextPredictedSpikeTime(index);
	} else if (position==this->GetNumberOfVariables()+2){
		return this->GetEndRefractoryPeriod(index);
	} else return -1;
}

//double VectorNeuronState::GetLastSpikeTime(int index){
//	return this->LastSpikeTime[index];
//}

void VectorNeuronState::NewFiredSpike(int index){
	this->LastSpikeTime[index] = 0;
}

void VectorNeuronState::AddElapsedTime(int index, double ElapsedTime){

	this->LastSpikeTime[index] += ElapsedTime;
}


void VectorNeuronState::SetSizeState(int size){
	SizeStates=size;
}

int VectorNeuronState::GetSizeState(){
	return SizeStates;
}

void VectorNeuronState::SetTimeDriven(bool isTimeDriven){
	TimeDriven=isTimeDriven;
}

bool VectorNeuronState::GetTimeDriven(){
	return TimeDriven;
}


void VectorNeuronState::InitializeStates(int size, float * initialization){
	SetSizeState(size);
	
	VectorNeuronStates = new float[GetNumberOfVariables()*GetSizeState()]();
	LastUpdate=new double[GetSizeState()]();
	LastSpikeTime=new double[GetSizeState()]();
	
	if(!TimeDriven){
		PredictedSpike=new double[GetSizeState()]();
		PredictionEnd=new double[GetSizeState()]();
	}else{
		InternalSpike=new bool[GetSizeState()]();
	}
	

	//For the CPU, we store all the variables of a neuron in adjacent memory positions to
	//improve the spatial location of the data.
	for(int z=0; z<GetSizeState()*GetNumberOfVariables(); z+=GetNumberOfVariables()){
		for (int j=0; j<GetNumberOfVariables(); j++){ 
			VectorNeuronStates[z+j]=initialization[j];
		}
	}

	for(int z=0; z<GetSizeState(); z++){
		LastSpikeTime[z]=100.0;
	}

}


bool * VectorNeuronState::getInternalSpike(){
	return InternalSpike;
}


void VectorNeuronState::Set_Is_Monitored(bool monitored){
	Is_Monitored=monitored;
}


bool VectorNeuronState::Get_Is_Monitored(){
	return Is_Monitored;
}

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