4.2.1.1. BackpropagatingAPTest

class BackpropagatingAPTest(config={}, observation={'mean_AP1_amp_at_150um': None, 'mean_AP1_amp_at_250um': None, 'mean_AP1_amp_at_50um': None, 'mean_AP1_amp_strong_propagating_at_350um': None, 'mean_AP1_amp_weak_propagating_at_350um': None, 'mean_APlast_amp_at_150um': None, 'mean_APlast_amp_at_250um': None, 'mean_APlast_amp_at_350um': None, 'mean_APlast_amp_at_50um': None, 'std_AP1_amp_at_150um': None, 'std_AP1_amp_at_250um': None, 'std_AP1_amp_at_50um': None, 'std_AP1_amp_strong_propagating_at_350um': None, 'std_AP1_amp_weak_propagating_at_350um': None, 'std_APlast_amp_at_150um': None, 'std_APlast_amp_at_250um': None, 'std_APlast_amp_at_350um': None, 'std_APlast_amp_at_50um': None}, name='Back-propagating action potential test', force_run=False, force_run_FindCurrentStim=False, base_directory=None, show_plot=True, save_all=True, trunk_origin=None)

Bases: sciunit.tests.Test

Tests the mode and efficacy of back-propagating action potentials on the apical trunk.

Parameters

• config (dict) – dictionary loaded from a JSON file, containing the parameters of the simulation

• observation (dict) – dictionary loaded from a JSON file, containing the experimental mean and std values for the features to be tested

• force_run (boolean) – If True and the pickle files containing the model’s response to the simulation exists, the simulation won’t be run again, traces are loaded from the pickle file

• force_run_FindCurrentStim (boolean) – If True and the pickle file containing the adjusted current stimulus parameter exists, the search wont run again, data are loaded from the pickle file

• base_directory (str) – Results will be saved here

• show_plot (boolean) – If False, plots are not displayed but still saved

• save_all (boolean) – If False, only the JSON files containing the absolute feature values, the feature error scores and the final scores, and a log file are saved, but the figures and pickle files are not.

• trunk_origin (list) – first element : name of the section from which the trunk originates, second element : position on section (E.g. [‘soma[5]’, 1]). If not set by the user, the end of the default soma section is used.

Methods Summary

bind_score(score, model, observation, prediction)	For the user to bind additional features to the score.
binsearch(model, stim_range, delay, dur, …)
cclamp(model, amp, delay, dur, section_stim, …)
compute_score(observation, prediction[, verbose])	Implementation of sciunit.Test.score_prediction.
extract_amplitudes(traces, traces_results, …)
extract_somatic_spiking_features(traces, …)
find_current_amp(model, delay, dur, …)
format_data(observation)
generate_prediction(model[, verbose])	Implementation of sciunit.Test.generate_prediction.
plot_features(model, features, actual_distances)
plot_results(observation, prediction, …)
plot_traces(model, traces, dend_locations, …)
run_cclamp_on_soma(model, amp, delay, dur, …)
spikecount(delay, duration, soma_trace)
validate_observation(observation)	Validate the observation provided to the constructor.

Methods Documentation

bind_score(score, model, observation, prediction)

For the user to bind additional features to the score.

binsearch(model, stim_range, delay, dur, section_stim, loc_stim, section_rec, loc_rec)

cclamp(model, amp, delay, dur, section_stim, loc_stim, dend_locations)

compute_score(observation, prediction, verbose=False)

Implementation of sciunit.Test.score_prediction.

extract_amplitudes(traces, traces_results, actual_distances)

extract_somatic_spiking_features(traces, delay, duration)

find_current_amp(model, delay, dur, section_stim, loc_stim, section_rec, loc_rec)

format_data(observation)

generate_prediction(model, verbose=False)

Implementation of sciunit.Test.generate_prediction.

plot_features(model, features, actual_distances)

plot_results(observation, prediction, errors, model_name_bAP)

plot_traces(model, traces, dend_locations, actual_distances)

run_cclamp_on_soma(model, amp, delay, dur, section_stim, loc_stim, section_rec, loc_rec)

spikecount(delay, duration, soma_trace)

validate_observation(observation)

Validate the observation provided to the constructor.

Raises an ObservationError if invalid.