def do_main_analysis(spike_times, spike_times_optimization,
spike_times_validation, analysis_file, settings):
"""
Determine the history dependence of a neuron's activity based on
spike time data.
"""
utl.save_spike_times_stats(analysis_file, spike_times, **settings)
if settings['cross_validated_optimization']:
settings['cross_val'] = 'h1' # first half of the data
utl.save_history_dependence_for_embeddings(analysis_file,
spike_times_optimization,
**settings)
settings['cross_val'] = 'h2' # second half of the data
utl.save_history_dependence_for_embeddings(analysis_file,
spike_times_validation,
**settings)
utl.compute_CIs(analysis_file,
spike_times,
target_R='R_max',
**settings)
else:
settings['cross_val'] = None
utl.save_history_dependence_for_embeddings(analysis_file, spike_times,
**settings)
utl.compute_CIs(analysis_file,
spike_times,
target_R='R_max',
**settings)
def compute_CIs(spike_times, analysis_file, settings):
"""
Compute bootstrap replications of the history-dependence estimate
which can be used to obtain confidence intervals.
"""
if settings['cross_validated_optimization']:
settings['cross_val'] = 'h2' # second half of the data
else:
settings['cross_val'] = None
utl.compute_CIs(analysis_file, spike_times, target_R='R_tot', **settings)
utl.compute_CIs(analysis_file,
spike_times,
target_R='nonessential',
**settings)
def test_history_dependence_estimation():
estimator_env.settings['cross_val'] = None
for estimation_method in ['bbc', 'shuffling']:
estimator_env.settings['estimation_method'] = estimation_method
utl.save_history_dependence_for_embeddings(estimator_env.analysis_file,
estimator_env.spike_times,
**estimator_env.settings)
utl.compute_CIs(estimator_env.analysis_file,
estimator_env.spike_times,
target_R='R_max',
**estimator_env.settings)
utl.create_CSV_files(estimator_env.analysis_file,
estimator_env.csv_stats_file,
estimator_env.csv_histdep_data_file,
estimator_env.csv_auto_MI_data_file,
estimator_env.analysis_num, **estimator_env.settings)
check_parameters()