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

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Accession:116957
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)
References:
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, Nowotny T, 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:
Receptor(s):
Gene(s):
Transmitter(s):
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 sussex.ac.uk];
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:  tnowotny@ucsd.edu
  
   initial version: 2005-08-17
  
--------------------------------------------------------------------------*/

#ifndef CN_BASE_H
#define CN_BASE_H

#include <cassert>
#include <iostream>
#include <list>
#include <vector>

#define forall(l, it) for (it= (l).begin(); it != (l).end(); it++)
#define pw2(x) x*x
#define pw3(x) x*x*x
#define pw4(x) x*x*x*x

#define NEURTYPENO 33

#define TIMENEURON -1
#define HHNEURON 0
#define HHCANEURON 1
#define FNGMNEURON 2
#define ICANEURON 3
#define STUPIDNEURON 4
#define POISSONNEURON 5
#define VALNEURON 6
#define MULTIFIRE_INPUTNEURON 7
#define IFNEURON 8
#define KOLNEURON 9
#define KOLINNEURON 10
#define KOLMULTIFIRE_INPUTNEURON 11
#define LPNEURON 12
#define LPGNEURON 13
#define LPJNEURON 14
#define LPTNEURON 15
#define HVCE1 16
#define HVCI1 17
#define ECNEURON 18
#define LPANEURON 19
#define LPMNEURON 20
#define HRNEURON 21
#define LPRNEURON 22
#define PNRAMON 23
#define LNRAMON 24
#define PSEUDONEURON 25
#define KCDNEURON 26
#define LMPNEURON 27
#define LTVNEURON 28
#define VDPOLNEURON 29
#define SIN 30
#define DATA 31
#define PNANEURON 32

#define SYNTYPENO 31

#define DCINPUT 0
#define DEMIGAP 1
#define RALL 2
#define ALINSYN 3
#define CRALL 4
#define DYNSTDP 5
#define HERA 6
#define LRNRALL 7
#define GRAD 8
#define KOLSYNAPSE 9
#define HEBBKOL 10
#define KOLGRADSYNAPSE 11
#define HVCSYN 12
#define ABSYN 13
#define ABECPLAST 14
#define SYNAS 15
#define ABECPLAST3 16
#define SYNASPLAST 17
#define RMSYN 18
#define sRMSYN 19
#define IRMSYN 20
#define SMSTDP1 21
#define LTVSYN 22
#define INPUTFUNCTION 23
#define S01SYN 24
#define S01ECPLAST3 25
#define RALLECPLAST3 26
#define T2RALL 27
#define T2RALLECPLAST3 28
#define ABECHEBB3 29
#define DEMIGAPSYNAPSE 30

#endif

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