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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_A
include
integration_method
BDFn.h *
BDFn_GPU.h *
BDFn_GPU2.h *
Euler.h *
Euler_GPU.h *
Euler_GPU2.h *
FixedStep.h *
FixedStepSRM.h *
IntegrationMethod.h *
IntegrationMethod_GPU.h *
IntegrationMethod_GPU2.h *
LoadIntegrationMethod.h *
LoadIntegrationMethod_GPU.h *
LoadIntegrationMethod_GPU2.h *
RK2.h *
RK2_GPU.h *
RK2_GPU2.h *
RK4.h *
RK4_GPU.h *
RK4_GPU2.h *
RK45.h *
                            
/***************************************************************************
 *                           BDFn_GPU.h                                    *
 *                           -------------------                           *
 * copyright            : (C) 2013 by Francisco Naveros                    *
 * email                : 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.                                   *
 *                                                                         *
 ***************************************************************************/

#ifndef BDFN_GPU_H_
#define BDFN_GPU_H_

/*!
 * \file BDFn_GPU.h
 *
 * \author Francisco Naveros
 * \date May 2013
 *
 * This file declares a class which implement six BDF (Backward Differentiation Formulas) integration methods (from 
 * first order to sixth order BDF integration method) in GPU (this class is stored in CPU memory and controles the
 * reservation and freeing of GPU auxiliar memory). This method implements a progressive implementation of the
 * higher order integration method using the lower order integration mehtod (BDF1->BDF2->...->BDF6).All integration
 * methods in GPU are fixed step due to the parallel architecture of this one. 
 */

#include "./IntegrationMethod_GPU.h"


/*!
 * \class BDFn
 *
 * \brief BDFn integration method in CPU for GPU
 *
 * This class abstracts the initializacion in CPU of six BDF integration methods (BDF1, BDF2, ..., BDF6) for GPU. 
 * This CPU class controles the reservation and freeing of GPU auxiliar memory.
 *
 * \author Francisco Naveros
 * \date May 2013
 */
class BDFn_GPU: public IntegrationMethod_GPU{
	public:

		/*!
		 * \brief These vectors are used as auxiliar vectors in GPU memory.
		*/
		float * AuxNeuronState;
		float * AuxNeuronState_p;
		float * AuxNeuronState_p1;
		float * AuxNeuronState_c;
		float * jacnum;
		float * J;
		float * inv_J;
		//For Jacobian
		float * AuxNeuronState2;
		float * AuxNeuronState_pos;
		float * AuxNeuronState_neg;
		//For Coeficient
		float * Coeficient;


		/*!
		 * \brief This vector stores previous neuron state variable for all neurons. This one is used as a memory.
		*/
		float * PreviousNeuronState;


		/*!
		 * \brief This vector stores the difference between previous neuron state variable for all neurons. This 
		 * one is used as a memory.
		*/
		float * D;

		/*!
		 * \brief This vector contains the state of each neuron (BDF order). When the integration method is reseted (the values of the neuron model variables are
		 * changed outside the integration method, for instance when a neuron spikes and the membrane potential is reseted to the resting potential), the values
		 * store in PreviousNeuronState and D are no longer valid. In this case the order it is set to 0 and must grow in each integration step until it is reache
		 * the target order.
		*/
		int * state;

		/*!
		 * \brief This value stores the order of the integration method.
		*/
		int BDForder;

		/*!
		 * \brief This constant matrix stores the coefficients of each BDF order.
		*/
		const static float Coeficient_CPU [7*7];

	
		/*!
		 * \brief Constructor of the class with 5 parameter.
		 *
		 * It generates a new BDF object indicating the order of the method.
		 *
		 * \param N_neuronStateVariables number of state variables for each cell.
		 * \param N_differentialNeuronState number of state variables witch are calculate with a differential equation for each cell.
		 * \param N_timeDependentNeuronState number of state variables witch ara calculate with a time dependent equation for each cell.
		 * \param BDForder BDF order (1, 2, ..., 6).
		 * \param intergrationMethod name of the integration method (BDF1, BDF2, ..., BDF6)
		 */
		BDFn_GPU(int N_neuronStateVariables, int N_differentialNeuronState, int N_timeDependentNeuronState, int BDForder, char * intergrationMethod);


		/*!
		 * \brief Class destructor.
		 *
		 * It destroys an object of this class.
		 */
		virtual ~BDFn_GPU();


		/*!
		 * \brief This method reserves all the necesary GPU memory (this memory could be reserved directly in the GPU, but this 
		 * suppose some restriction in the amount of memory witch can be reserved).
		 *
		 * This method reserves all the necesary GPU memory (this memory could be reserved directly in the GPU, but this 
		 * suppose some restriction in the amount of memory witch can be reserved).
		 *
		 * \param N_neurons Number of neurons.
		 * \param Total_N_thread Number of thread in GPU.
		 */
		void InitializeMemoryGPU(int N_neurons, int Total_N_thread);
};

#endif /* BDFN_GPU_H_ */

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