simrun ❭ synaptic_strength_fitting ❭ calculate_optimal_g

calculate_optimal_g¶

simrun.synaptic_strength_fitting.calculate_optimal_g(pdf)¶

Calculate the optimal synaptic conductance such that the EPSP statistics match empirical data.

This function calculates the optimal synaptic conductance by matching empirically observed EPSP statistics (mean, median, maximum) to a linear model. Each statistic provides a different estimate of the optimal g. The final optimal g is then a weighed average of the three statistics, where the weights for mean:median:max are 2:2:1 respectively.

This function is used in PSPs to calculate the optimal synaptic conductance for each celltype.

Parameters:¶

pdf (pd.DataFrame) – A table containing the empirical EPSP statistics (mean, median and maximum), and linear fits for each statistic.

Returns:¶

None. Updates the original table inplace. Adds the columns optimal g, optimal g mean, optimal g median, and optimal g max.

See also

linear_fit_pdf() for the linear model that relates the EPSP statistics to the synaptic conductance.

Example:

>>> pdf = psp.get\_summary\_statistics(method='dynamic\_baseline')
>>> fit = linear\_fit\_pdf(pdf)
>>> fit.head()
    EPSP mean\_offset  EPSP mean\_slope  EPSP med\_offset  EPSP med\_slope  EPSP max\_offset  EPSP max\_slope EPSP\_std\_offset EPSP\_std\_slope
...
>>> measured\_data
            EPSP\_mean\_measured  EPSP\_med\_measured   EPSP\_max\_measured
celltype\_1  ...
celltype\_2  ...
>>> pdf = pd.concat([fit, measured\_data], axis=1)
>>> calculate\_optimal\_g(pdf)
>>> pdf[cell\_type 1']['optimal g']
1.85