brainbox.task.closed_loop

Computes task related output

Functions

compute_comparison_statistics	Compute statistical test between two arrays
differentiate_units	Determine units which significantly differentiate between two task events (e.g. stimulus left/right) by performing a statistical test between the spike rates elicited by the two events.
generate_pseudo_blocks	Generate a pseudo block structure
generate_pseudo_session	Generate a complete pseudo session with biased blocks, all stimulus contrasts, choices and rewards and omissions.
generate_pseudo_stimuli	Generate a block structure with stimuli
get_impostor_target	Generate impostor targets by selecting from a list of current targets of variable length.
responsive_units	Determine responsive neurons by doing a Wilcoxon Signed-Rank test between a baseline period before a certain task event (e.g. stimulus onset) and a period after the task event.
roc_between_two_events	Calcluate area under the ROC curve that indicates how well the activity of the neuron distiguishes between two events (e.g. movement to the right vs left).
roc_single_event	Determine how well neurons respond to a certain task event by calculating the area under the ROC curve between a baseline period before the event and a period after the event.

responsive_units(spike_times, spike_clusters, event_times, pre_time=[0.5, 0], post_time=[0, 0.5], alpha=0.05, fdr_corr=False, use_fr=False)

Determine responsive neurons by doing a Wilcoxon Signed-Rank test between a baseline period before a certain task event (e.g. stimulus onset) and a period after the task event.

Parameters:

• spike_times (1D array) – spike times (in seconds)

• spike_clusters (1D array) – cluster ids corresponding to each event in spikes

• event_times (1D array) – times (in seconds) of the events from the two groups

• pre_time (two-element array) – time (in seconds) preceding the event to get the baseline (e.g. [0.5, 0.2] would be a window starting 0.5 seconds before the event and ending at 0.2 seconds before the event)

• post_time (two-element array) – time (in seconds) to follow the event times

• alpha (float) – alpha to use for statistical significance

• fdr_corr (boolean) – whether to use an FDR correction (Benjamin-Hochmann) to correct for multiple testing

• use_fr (bool) – whether to use the firing rate instead of total spike count

Returns:

• significant_units (ndarray) – an array with the indices of clusters that are significatly modulated

• stats (1D array) – the statistic of the test that was performed

• p_values (ndarray) – the p-values of all the clusters

• cluster_ids (ndarray) – cluster ids of the p-values

differentiate_units(spike_times, spike_clusters, event_times, event_groups, pre_time=0, post_time=0.5, test='ranksums', alpha=0.05, fdr_corr=False)

Determine units which significantly differentiate between two task events (e.g. stimulus left/right) by performing a statistical test between the spike rates elicited by the two events. Default is a Wilcoxon Rank Sum test.

Parameters:

• spike_times (1D array) – spike times (in seconds)

• spike_clusters (1D array) – cluster ids corresponding to each event in spikes

• event_times (1D array) – times (in seconds) of the events from the two groups

• event_groups (1D array) – group identities of the events as either 0 or 1

• pre_time (float) – time (in seconds) to precede the event times to get the baseline

• post_time (float) – time (in seconds) to follow the event times

• test (string) –

  which statistical test to use, options are:

  ’ranksums’ Wilcoxon Rank Sums test ‘signrank’ Wilcoxon Signed Rank test (for paired observations) ‘ttest’ independent samples t-test ‘paired_ttest’ paired t-test

• alpha (float) – alpha to use for statistical significance

• fdr_corr (boolean) – whether to use an FDR correction (Benjamin-Hochmann) to correct for multiple testing

Returns:

• significant_units (1D array) – an array with the indices of clusters that are significatly modulated

• stats (1D array) – the statistic of the test that was performed

• p_values (1D array) – the p-values of all the clusters

• cluster_ids (ndarray) – cluster ids of the p-values

compute_comparison_statistics(value1, value2, test='ranksums', alpha=0.05, fdr_corr=False)

Compute statistical test between two arrays

Parameters:

• value1 (1D array) – first array of values to compare

• value2 (1D array) – second array of values to compare

• test (string) –

  which statistical test to use, options are:

  ’ranksums’ Wilcoxon Rank Sums test ‘signrank’ Wilcoxon Signed Rank test (for paired observations) ‘ttest’ independent samples t-test ‘paired_ttest’ paired t-test

• alpha (float) – alpha to use for statistical significance

• fdr_corr (boolean) – whether to use an FDR correction (Benjamin-Hochmann) to correct for multiple testing

Returns:

• significant_units (1D array) – an array with the indices of values that are significatly modulated

• stats (1D array) – the statistic of the test that was performed

• p_values (1D array) – the p-values of all the values

roc_single_event(spike_times, spike_clusters, event_times, pre_time=[0.5, 0], post_time=[0, 0.5])

Determine how well neurons respond to a certain task event by calculating the area under the ROC curve between a baseline period before the event and a period after the event. Values of > 0.5 indicate the neuron respons positively to the event and < 0.5 indicate a negative response.

Parameters:

• spike_times (1D array) – spike times (in seconds)

• spike_clusters (1D array) – cluster ids corresponding to each event in spikes

• event_times (1D array) – times (in seconds) of the events from the two groups

• pre_time (two-element array) – time (in seconds) preceding the event to get the baseline (e.g. [0.5, 0.2] would be a window starting 0.5 seconds before the event and ending at 0.2 seconds before the event)

• post_time (two-element array) – time (in seconds) to follow the event times

Returns:

• auc_roc (1D array) – the area under the ROC curve

• cluster_ids (1D array) – cluster ids of the p-values

roc_between_two_events(spike_times, spike_clusters, event_times, event_groups, pre_time=0, post_time=0.25)

Calcluate area under the ROC curve that indicates how well the activity of the neuron distiguishes between two events (e.g. movement to the right vs left). A value of 0.5 indicates the neuron cannot distiguish between the two events. A value of 0 or 1 indicates maximum distinction. Significance is determined by bootstrapping the ROC curves. If 0.5 is not included in the 95th percentile of the bootstrapped distribution, the neuron is deemed to be significant.

Parameters:

• spike_times (1D array) – spike times (in seconds)

• spike_clusters (1D array) – cluster ids corresponding to each event in spikes

• event_times (1D array) – times (in seconds) of the events from the two groups

• event_groups (1D array) – group identities of the events as either 0 or 1

• pre_time (float) – time (in seconds) to precede the event times

• post_time (float) – time (in seconds) to follow the event times

Returns:

• auc_roc (1D array) – an array of the area under the ROC curve for every neuron

• cluster_ids (1D array) – cluster ids of the AUC values

generate_pseudo_blocks(n_trials, factor=60, min_=20, max_=100, first5050=90)

Generate a pseudo block structure

Parameters:

• n_trials (int) – how many trials to generate

• factor (int) – factor of the exponential

• min (int) – minimum number of trials per block

• max (int) – maximum number of trials per block

• first5050 (int) – amount of trials with 50/50 left right probability at the beginning

Returns:

probabilityLeft – array with probability left per trial

Return type:

1D array

generate_pseudo_stimuli(n_trials, contrast_set=[0, 0.06, 0.12, 0.25, 1], first5050=90)

Generate a block structure with stimuli

Parameters:

• n_trials (int) – number of trials to generate

• contrast_set (1D array) – the contrasts that are presented. The default is [0.06, 0.12, 0.25, 1].

• first5050 (int) – Number of 50/50 trials at the beginning of the session. The default is 90.

Returns:

• p_left (1D array) – probability of left stimulus

• contrast_left (1D array) – contrast on the left

• contrast_right (1D array) – contrast on the right

generate_pseudo_session(trials, generate_choices=True, contrast_distribution='non-uniform')

Generate a complete pseudo session with biased blocks, all stimulus contrasts, choices and rewards and omissions. Biased blocks and stimulus contrasts are generated using the same statistics as used in the actual task. The choices of the animal are generated using the actual psychometrics of the animal in the session. For each synthetic trial the choice is determined by drawing from a Bernoulli distribution that is biased according to the proportion of times the animal chose left for the stimulus contrast, side, and block probability. No-go trials are ignored in the generating of the synthetic choices.

Parameters:

• trials (DataFrame) – Pandas dataframe with columns as trial vectors loaded using ONE

• generate_choices (bool) – whether to generate the choices (runs faster without)

• contrast_distribution (str ['uniform', 'non-uniform']) – the absolute contrast distribution. If uniform, the zero contrast is as likely as other contrasts: BiasedChoiceWorld task If ‘non-uniform’, the zero contrast is half as likely to occur: EphysChoiceWorld task (‘biased’ is kept for compatibility, but is deprecated as it is confusing)

Returns:

pseudo_trials – a trials dataframe with synthetically generated trials

Return type:

DataFrame

get_impostor_target(targets, labels, current_label=None, seed_idx=None, verbose=False)

Generate impostor targets by selecting from a list of current targets of variable length. Targets are selected and stitched together to the length of the current labeled target, aka ‘Frankenstein’ targets, often used for evaluating a null distribution while decoding.

Parameters:

• targets (list of all targets) – targets may be arrays of any dimension (a,b,…,z) but must have the same shape except for the last dimension, z. All targets must have z > 0.

• labels (numpy array of strings) – labels corresponding to each target e.g. session eid. only targets with unique labels are used to create impostor target. Typically, use eid as the label because each eid has a unique target.

• current_label (string) – targets with the current label are not used to create impostor target. Size of corresponding target is used to determine size of impostor target. If None, a random selection from the set of unique labels is used.

Returns:

impostor_final

Return type:

numpy array, same shape as all targets except last dimension