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Unfit
3.1.1
Data fitting and optimization software
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Fit the voltage dependence of a Hodgkin Huxley rate constant. More...
#include <HodgkinHuxleyBetaN.hpp>
Public Member Functions | |
| HodgkinHuxleyBetaN (const std::vector< double > &vm, const std::vector< double > beta_n) | |
| std::vector< double > | operator() (const std::vector< double > ¶m) |
Public Member Functions inherited from Unfit::GenericCostFunction | |
| virtual | ~GenericCostFunction () |
Private Attributes | |
| const std::vector< double > | vm_ |
| const std::vector< double > | beta_n_ |
Fit the voltage dependence of a Hodgkin Huxley rate constant.
Here the goal is to fit the beta_n rate constant (potassium channel inactivation) using data digitized from Hodgkin & Huxley's landmark 1952 paper, Figure 4 (PMCID: PMC1392413) as a function of the cell membrane potential (vm). The equation that is used to fit the data is:
beta_n = A*exp(-vm/B)
The goal is to find the values of A & B that best fit the data. In terms of the model, A = param[0] and B = param[1].
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inline |
Create the cost function. Here the experimental data must be passed in, and cannot be changed (if you want to, just create another cost function object). Here the experimental data is two vectors, membrane potential (vm) and the rate constant beta_n.
Intended use : HodgkinHuxleyBetaN cost_func(vm, beta_n);
| vm | A vector of membrane potentials |
| beta_n | A vector of experimental beta_n data |
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inlinevirtual |
Calculate the linear distance (residuals) between our model and the data. This method encapsulates the model, and expects the current estimates of the unknown parameters as an input. See the class documentation for details about the model.
Intended use : residuals = cost_func(param)
| param | A vector containing the current estimates of the parameters we are trying to fit |
Implements Unfit::GenericCostFunction.
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private |
A vector to store the experimental rate constant, beta_n
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private |
A vector to store the experimental membrane potential, vm
1.8.13