Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)

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This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are a) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough). b) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents. c) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect. d) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)
1 . Nowotny T, Levi R, Selverston AI (2008) Probing the dynamics of identified neurons with a data-driven modeling approach. PLoS One 3:e2627 [PubMed]
2 . Ivanchenko MV, Thomas Nowotny , Selverston AI, Rabinovich MI (2008) Pacemaker and network mechanisms of rhythm generation: cooperation and competition. J Theor Biol 253:452-61 [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:
Cell Type(s): Hodgkin-Huxley neuron; Stomatogastric Ganglion (STG) Lateral Pyloric (LP) cell;
Channel(s): I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Simulation Environment: C or C++ program;
Model Concept(s): Activity Patterns; Bursting; Parameter Fitting; Invertebrate; Methods; Parameter sensitivity;
Implementer(s): Nowotny, Thomas [t.nowotny at];
Search NeuronDB for information about:  I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
   Author: Thomas Nowotny
   Institute: Institute for Nonlinear Dynamics
              University of California San Diego
              La Jolla, CA 92093-0402
   email to:
   initial version: 2005-08-17


  Implementation of a 6-5 Runge Kutta method with adaptive time step
  mostly taken from the book "The numerical analysis of ordinary differential
  equations - Runge-Kutta and general linear methods" by J.C. Butcher, Wiley,
  Chichester, 1987 and a free adaption to a 6 order Runge Kutta method
  of an ODE system with additive white noise


using namespace std;

#ifndef CN_RK65N_H
#define CN_RK65N_H

#include "CN_NeuronModel.h"
#include <cmath>
#include <cfloat>

class rk65n
  double a[9][8];
  double b[9];

  double newdt, dtx, theEps;
  double *Y[9];
  double *F[9];
  double *y5;
  double aF;
  double delta;
  int i, j, k;

  int N;
  double maxdt, eps, abseps, releps; 

  rk65n(int, double, double, double, double);
  double integrate(double *, double *, NeuronModel *, double);
  int Dim() { return N; }


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