allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis module

class allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.StimulusAnalysis(ecephys_session, trial_duration=None, **kwargs)

Bases: object

property METRICS_COLUMNS

property conditionwise_psth

For every unit and stimulus-condition construction a PSTH table. ie. the spike-counts at a each time-interval during a stimulus, averaged over all trials of the same stim condition.

Each PSTH will count and average spikes over a time-window as determined by class parameter ‘trial_duration’ which ideally be a similar value as the duration of each stimulus ( in seconds). The length of each time-bin is determined by the class parameter ‘psth_resolution’ (in seconds).

Returns:

conditionwise_psth xarray.DataArray

An 3D table that contains the PSTH for every unit/condition, with the following coordinates

• stimulus_condition_id

• time_relative_to_stimulus_onset

• unit_id

property conditionwise_statistics

Create a table of spike statistics, averaged and indexed by every unit_id, stimulus_condition_id pair.

Returns:

conditionwise_statistics: pd.DataFrame

A dataframe indexed by unit_id and stimulus_condition containing spike_count, spike_mean, spike_sem, spike_std and stimulus_presentation_count information.

property ecephys_session

empty_metrics_table()

get_intrinsic_timescale(unit_ids)

Calculates the intrinsic timescale for a subset of units

property known_spontaneous_keys

classmethod known_stimulus_keys()

Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.

property metrics

Returns a pandas DataFrame of the stimulus response metrics for each unit.

property metrics_dtypes

property metrics_names

property name

Return the stimulus name.

property null_condition

plot_conditionwise_raster(unit_id)

Plot a matrix of rasters for each condition (orientations x temporal frequencies)

plot_raster(condition, unit_id)

property presentationwise_spike_times

Constructs a table containing all the relevant spike_times plus the stimulus_presentation_id and unit_id for the given spike.

Returns:

presentationwise_spike_timespd.DataFrame

Indexed by spike_time, each spike containing the corresponding stimulus_presentation_id and unit_id

property presentationwise_statistics

Returns a table of the spike-counts, stimulus-conditions and running speed for every stimulus_presentation_id , unit_id pair.

Returns:

presentationwise_statistics: pd.DataFrame

MultiIndex : unit_id, stimulus_presentation_id Columns : spike_count, stimulus_condition_id, running_speed

property running_speed

Construct a dataframe with the averaged running speed for each stimulus_presenation_id

property spikes

Returns a dictionary of unit_id -> spike-times.

property stim_table

property stim_table_spontaneous

Returns a stimulus table with only ‘spontaneous’ stimulus selected.

property stimulus_conditions

Returns a table of relevant stimulus_conditions.

Returns:

pd.DataFrame

Index : stimulus_condition_id Columns : stimulus parameter types

property total_presentations

Total nmber of presentations / trials

property trial_duration

property unit_count

Get the number of units.

property unit_ids

Returns a list of unit IDs for which to apply the analysis

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.calculate_time_delayed_correlation(dataset)

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.deg2rad(arr)

Converts array-like input from degrees to radians

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.dsi(orivals, tuning)

Computes the direction selectivity of a cell. See Ringbach 2002, Van Hooser 2014

Parameters:

ori_valscomplex array of length N

Each value the oriention of the stimulus.

tuningfloat array of length N

Each value the (averaged) response of the cell at a different orientation.

Returns:

osifloat

An N-dimensional array of the circular variance (scalar value, in radians) of the responses.

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.fano_factor(spike_counts)

Computers the fano factor (var/mean) for the spike-counts across a series of trials.

Parameters:

spike_countsarray

The spike counts across a series of 2 or more trials

Returns:

fano_factorfloat

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.fit_exp(rsc_time_matrix)

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.get_fr(spikes, num_timestep_second=30, sweep_length=3.1, filter_width=0.1)

Uses a gaussian convolution to convert the spike-times into a contiguous firing-rate series.

Parameters:

spikesarray

An array of spike times (shifted to start at 0)

num_timestep_secondfloat

The sampling frequency

sweep_lengthfloat

The lenght of the returned array

filter_width: float

The window of the gaussian method

Returns:

firing_ratefloat

A linear-spaced array of length num_timestep_second*sweep_length of the smoothed firing rates series.

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.lifetime_sparseness(responses)

Computes the lifetime sparseness for one unit. See Olsen & Wilson 2008.

Parameters:

responsesarray of floats

An array of a unit’s spike-counts over the duration of multiple trials within a given session

Returns:

lifetime_sparsnessfloat

The lifetime sparseness for one unit

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.osi(orivals, tuning)

Computes the orientation selectivity of a cell. The calculation of the orientation is done using the normalized circular variance (CirVar) as described in Ringbach 2002

Parameters:

ori_valscomplex array of length N

Each value the oriention of the stimulus.

tuningfloat array of length N

Each value the (averaged) response of the cell at a different orientation.

Returns:

osifloat

An N-dimensional array of the circular variance (scalar value, in radians) of the responses.

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.overall_firing_rate(start_times, stop_times, spike_times)

Computes the global firing rate of a series of spikes, for only those values within the given start and stop times.

Parameters:

start_timesarray of N floats

A series of stimulus block start times (seconds)

stop_timesarray of N floats

Times when the stimulus block ends

spike_timesarray of floats

A list of spikes for a given unit

Returns:

firing_ratefloat

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.reliability(unit_sweeps, padding=1.0, num_timestep_second=30, filter_width=0.1, window_beg=0, window_end=None)

Computes the trial-to-trial reliability for a set of sweeps for a given cell

Parameters:

• unit_sweeps

• padding

Returns:

allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.running_modulation(spike_counts, running_speeds, speed_threshold=1.0)

Given a series of trials that include the spike-counts and (averaged) running-speed, does a statistical comparison to see if there was any difference in spike firing while running and while stationary.

Requires at least 2 trials while the mouse is running and two when the mouse is stationary.

Parameters:

spike_countsarray of floats of size N.

The spike counts for each trial

running_speeds: array floats of size N.

The running velocities (cm/s) of each trial.

speed_threshold: float

The minimum threshold for which the animal can be considered running (default 1.0).

Returns:

p_valuefloat or Nan

T-test p-value between the running and stationary trials.

run_modfloat or Nan

Relative difference between running and stationary mean firing rates.