get_hay_problem_description¶

biophysics_fitting.hay.specification.get_hay_problem_description()¶

Get the problem description used in Hay et al. [2].

This method returns a dataframe that contains the objective names, the average value per objective, and the std per objective. Each objective is associated with a specific input stimulus, given in the ‘stim_name’ column. This distribution of parametrized responses can be used to quantify a cell’s response to a specific stimulus.

The resulting pd.DataFrame contains the following columns:

• objective: The name of the objective as an acronym, prefixed with the stimulus.

• feature: The full name of the objective.

• stim_name: The name of the stimulus.

• stim_type: The type of the stimulus (bAP, BAC or SquarePulse).

• mean: The empirically observed mean value of the feature.

• std: The empirically observed standard deviation of the feature.

The names of the objectives are prefixed with the stimulus name. The suffix acronyms mean the following:

Objective	Meaning
spikecount	Amount of spikes
APheight	AP height
APwidth	AP width
att2	Attenuation of the bAP between soma and recSite 1
att3	Attenuation of the bAP between soma and recSite 2
ahpdepth	After-hyperpolarization depth
ISI	Interspike interval
caSpike_height	height of the Ca2+-spike
caSpike_width	Width of the Ca2+-spike
mf[123]	Spike frequency
AI[123]	Adaptation index
ISIcv[123]	Interspike interval: coefficient of variation
DI[123]	Initial burst interspike interval (time between first and second AP)
TTFS[123]	First spike latency
APh[123]	AP height
fAHPd[123]	Fast AP depth
sAHPd[123]	Slow after-hyperpolarization depth
sAHPt[123]	Slow after-hyperpolarization time
APw[123]	Ap half-width

Returns:¶

The problem description, containing the objectives, objective names, stimulus type, mean and std for each objective.

Return type:¶

pd.DataFrame

Note

The objectives are specific to the L5PT and the Hay stimulus protocol. See Hay et al. [2].