4.2.1.3. ObliqueIntegrationTest

class ObliqueIntegrationTest(observation={'amp_at_th_sem': None, 'amp_at_th_std': None, 'async_nonlin_sem': None, 'async_nonlin_std': None, 'dist_threshold_sem': None, 'dist_threshold_std': None, 'mean_amp_at_th': None, 'mean_async_nonlin': None, 'mean_dist_threshold': None, 'mean_nonlin_at_th': None, 'mean_nonlin_suprath': None, 'mean_peak_deriv': None, 'mean_prox_threshold': None, 'mean_threshold': None, 'mean_time_to_peak': None, 'nonlin_at_th_sem': None, 'nonlin_at_th_std': None, 'nonlin_suprath_sem': None, 'nonlin_suprath_std': None, 'peak_deriv_sem': None, 'peak_deriv_std': None, 'prox_threshold_sem': None, 'prox_threshold_std': None, 'threshold_sem': None, 'threshold_std': None, 'time_to_peak_sem': None, 'time_to_peak_std': None}, name='Oblique integration test', force_run_synapse=False, force_run_bin_search=False, base_directory=None, show_plot=True, save_all=True, trunk_origin=None)

Bases: sciunit.tests.Test

Tests the signal integration in oblique dendrites for increasing number of synchronous and asynchronous inputs.

Parameters

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

• force_run_bin_search (boolean) – If True and the pickle files containing the synaptic weights for each dendritic location exist, the binary search for the proper synaptic weights won’t run again, weights are loaded from the pickle file

• force_run_bin_synapse (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

• 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

add_std_to_observation(observation)
analyse_syn_traces(model, t, v, v_dend, …)
bind_score(score, model, observation, prediction)	For the user to bind additional features to the score.
binsearch(model, dend_loc0)
calcs_plots(model, results, dend_loc000, …)
calcs_plots_async(model, results, …)
compute_score(observation, prediction[, verbose])	Implementation of sciunit.Test.score_prediction.
format_data(observation)
generate_prediction(model[, verbose])	Implementation of sciunit.Test.generate_prediction.
run_synapse(model, dend_loc_num_weight, interval)
syn_binsearch(model, dend_loc, interval, …)
validate_observation(observation)	Validate the observation provided to the constructor.

Methods Documentation

add_std_to_observation(observation)

analyse_syn_traces(model, t, v, v_dend, threshold)

bind_score(score, model, observation, prediction)

For the user to bind additional features to the score.

binsearch(model, dend_loc0)

calcs_plots(model, results, dend_loc000, dend_loc_num_weight)

calcs_plots_async(model, results, dend_loc000, dend_loc_num_weight)

compute_score(observation, prediction, verbose=False)

Implementation of sciunit.Test.score_prediction.

format_data(observation)

generate_prediction(model, verbose=False)

Implementation of sciunit.Test.generate_prediction.

run_synapse(model, dend_loc_num_weight, interval)

syn_binsearch(model, dend_loc, interval, number, weight)

validate_observation(observation)

Validate the observation provided to the constructor.

Raises an ObservationError if invalid.