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FLIPR Calibration with [K+]ec steps and known [K+]ic and known kP
FLIPR Complete Calibration
FLIPR Complete Calibration with known kP
FLIPR Complete Calibration with known kP - Goldman
FLIPR Complete Calibration with known kP - Goldman with Neural Network
FLIPR Complete Iterative Calibration
FLIPR Estimate PN
FLIPR Short Calibration based on known potential during MDC and CDC
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FLIPR Short Calibration between known baseline and MDC
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FLIPR Short Calibration from Zero with fx=0
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FLIPR Complete Calibration with known kP - Goldman with Neural Network ( IAFLIPRCompleteKPGoldmanNeural )

Parameters:

Name	Short Name	Type	Description
Range definitions in time courses	rangedef	string	Defines whether the ranges given below refer to frames or seconds in the recordings
Baseline Range	BaselineRange	string	Defines the range of the baseline (in the above defined time units). Longer baseline results more accurate calibration.
K+ Steps Ranges	KStepsRanges	string	Enter a list of range definitions for the duration of the K+-steps (in the above defined time units), including the MDC treatment.
[K+]ec Steps (mM;mM)	KStepsConcentrations	string	Extracellular (ec) K+ concentrations corresponding to the above defined steps, including MDC, in mM. Use the Wizard tab to calculate concentrations from added volumes.
kP	kP	real	Previously measured value of kp
CDC Range	CDCRange	string	The range of Complete Depolarization Cocktail addition (in the above defined time units).
Take maximum of CDC Range	CDCMax	boolean	If set yes, the maximum FLIPR intensity within the CDC Range is taken as zero calibration value (it error is the local SD of the range). Otherwise mean is calculated.
Quality control by propagated error of baseline	QCPMPrest	boolean	Removes traces where the estimated SE of baseline is greater than the threshold below. Requires error propagation.
Limit SE of baseline (mV)	QCPMPrestMaxSE	real	Traces where the SE of resting ΔψP parameter is larger than this number are removed.
fP0 Training Range	fP0TrainingXRange	string	The neural network will be trained with data in this range. Analyzed data must stay in this range for valid results.
fPX Training Range	fPXTrainingXRange	string	The neural network will be trained with data in this range. Analyzed data must stay in this range for valid results.
PN Training Range	PNTrainingXRange	string	The neural network will be trained with data in this range. Analyzed data must stay in this range for valid results.
[K+]ic+PD Training Range	KICplusPDTrainingXRange	string	The neural network will be trained with data in this range. Analyzed data must stay in this range for valid results. [K+]ic+PD is the denominator of the Goldman equation.
ΔψP Training Range	PMPTrainingRange	string	The neural network will be trained with data in this range. Analyzed data must stay in this range for valid results.
Maximal Number of Examples	trainingExamples	integer	A four-dimensional data set using the above independent values will be built approximately with this maximal number of elelments. The actual count will be less due to constraints.
Maximal Training Rounds	trainingRounds	integer	Maximum number of trainig rounds (epochs) for the neural network. Training is performed at first use of this method, and if the above parameters are changed.
Neural Network Type	netType	string	This defines the architecture of the neural network.
Error Estimation Type	netErrType	string	How the error committed by the neural network calculated.
Training Noise	trainingNoise	real	Gaussian Noise will be added to the training data set with this standard deviation in baseline normalized fluroescence units.

Description:

This method calculates plasma membrane potential (ΔψP) time course by only assuming the value of kP and allowing for ion permeabilities other than K+, using a neural network to calculate the baseline potential. Fluorescence of the FLIPR Membrane Potential Kit from Molecular Devices (aka PMPI, plasma membrane potential indicator) is calibrated. The model used to calculate ΔψP assumes that the plasma membrane permeability for ions other than K+ is significant. The extracellular concentration of these ions is either constant, or are replaced for K+ (e.g. Na+). Furthermore, the model is also compatible by the presence of a constant voltage offset injected by electrogenic ion pumping during K-steps.
The neural network with the selected parameters is trained when the calibration is performed for the first time, or after any change to the neural network or training parameters.The error of calculation of the baseline ΔψP may be calcualted with Monte Carlo simulation taking the noise of the fluorescence traces in account. However, it cannot account for error due to deviation from the model.
The redistribution of FLIPR is relatively fast and the exact value of kP has little effect unless very fast oscillations are looked. Therefore, in many cases the kP=0.38 determined on hippocampal neurons can be used.
Required experimental protocol: any time lapse, followed by establishment of K-equilibrium potential, at least 3 additions or replacements with high K+-medium (or 2 with no error estimation) and finally complete depolarization.
The cells have to be able to maintain a constant hyperpolarized ΔψP before starting K+-steps. Do not use this method if ΔψP depolarizes substantially when adding MDC.