brainbox.task.passive

Functions dealing with passive task

Functions

get_on_off_times_and_positions	Prepares passive receptive field mapping into format for analysis
get_rf_map_over_depth	Compute receptive field map for each stimulus onset binned across depth
get_stim_aligned_activity	param stim_events:
get_svd_map	Perform SVD on the spatiotemporal rf_map and return the first spatial components

get_on_off_times_and_positions(rf_map)

Prepares passive receptive field mapping into format for analysis

Parameters:

rf_map (output from brainbox.io.one.load_passive_rfmap) –

Returns:

• rf_map_times (time of each receptive field map frame np.array(len(stim_frames))

• rf_map_pos (unique position of each pixel on screen np.array(len(x_pos), len(y_pos)))

• rf_stim_frames (for each pixel on screen stores array of stimulus frames where stim onset)

• occurred. For both white squares ‘on’ and black squares ‘off’

get_rf_map_over_depth(rf_map_times, rf_map_pos, rf_stim_frames, spike_times, spike_depths, t_bin=0.01, d_bin=80, pre_stim=0.05, post_stim=1.5, y_lim=[0, 3840], x_lim=None)

Compute receptive field map for each stimulus onset binned across depth

Parameters:

• rf_map_times –

• rf_map_pos –

• rf_stim_frames –

• spike_times (array of spike times) –

• spike_depths (array of spike depths along probe) –

• t_bin (bin size along time dimension) –

• d_bin (bin size along depth dimension) –

• pre_stim (time period before rf map stim onset to epoch around) –

• post_stim (time period after rf map onset to epoch around) –

• y_lim (values to limit to in depth direction) –

• x_lim (values to limit in time direction) –

Returns:

• rfmap (receptive field map for ‘on’ ‘off’ stimuli.)

• Each rfmap has shape (depths, x_pos, y_pos, epoch_window)

• depths (depths between which receptive field map has been computed)

get_svd_map(rf_map)

Perform SVD on the spatiotemporal rf_map and return the first spatial components

Parameters:

rf_map –

Returns:

• rf_svd (First spatial component of rf map for ‘on’ ‘off’ stimuli.)

• Each dict has shape (depths, x_pos, y_pos)

get_stim_aligned_activity(stim_events, spike_times, spike_depths, z_score_flag=True, d_bin=20, t_bin=0.01, pre_stim=0.4, post_stim=1, base_stim=1, y_lim=[0, 3840], x_lim=None)

Parameters:

• stim_events (dict of different stim events. Each key contains time of stimulus onset) –

• spike_times (array of spike times) –

• spike_depths (array of spike depths along probe) –

• z_score_flag (whether to return values as z_score of firing rate) –

• T_BIN (bin size along time dimension) –

• D_BIN (bin size along depth dimension) –

• pre_stim (time period before rf map stim onset to epoch around) –

• post_stim (time period after rf map onset to epoch around) –

• base_stim (time period before rf map stim to use as baseline for z_score correction) –

• y_lim (values to limit to in depth direction) –

• x_lim (values to limit in time direction) –

Returns:

• stim_activity (stimulus aligned activity for each stimulus type, returned as z_score of firing)

• rate