Loading Passive Data

Passive stimuli related events. The passive protocol is split into three sections 1. Spontaneous activity (SP) 2. Receptive Field Mapping (RFM) 3. Task replay (TR)

Relevant datasets

• passivePeriods.intervalsTable.csv (SP)

• passiveRFM.times.npy (RFM)

• _iblrig_RFMapStim.raw.bin (RFM)

• passiveGabor.table.csv (TR - visual)

• passiveStims.table.csv (TR - auditory)

Loading

Loading spontaneous activity

from one.api import ONE
one = ONE()
eid = '4ecb5d24-f5cc-402c-be28-9d0f7cb14b3a'

passive_times = one.load_dataset(eid, '*passivePeriods*', collection='alf')
SP_times = passive_times['spontaneousActivity']

Loading recpetive field mapping

from brainbox.io.one import load_passive_rfmap

RFMap = load_passive_rfmap(eid, one=one)

/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/one/alf/files.py:10: FutureWarning: `one.alf.files` will be removed in version 3.0. Use `one.alf.path` instead.
  warnings.warn(

Loading task replay

# Load visual stimulus task replay events
visual_TR = one.load_dataset(eid, '*passiveGabor*', collection='alf')

# Load auditory stimulus task replay events
auditory_TR = one.load_dataset(eid, '*passiveStims*', collection='alf')

More details

• Description of passive datasets

• Decsription of passive protocol

Useful modules

• brainbox.io.one

• brainbox.task.passive

• ibllib.io.extractors.extract_passive

Exploring passive data

Example 1: Compute firing rate for each cluster during spontaneous activity

# Find first probe insertion for session
pid = one.alyx.rest('insertions', 'list', session=eid)[0]['id']

from brainbox.io.one import SpikeSortingLoader
from iblatlas.atlas import AllenAtlas
import numpy as np
ba = AllenAtlas()

# Load in spikesorting
sl = SpikeSortingLoader(pid=pid, one=one, atlas=ba)
spikes, clusters, channels = sl.load_spike_sorting()
clusters = sl.merge_clusters(spikes, clusters, channels)

# Find spike times during spontaneous activity
SP_idx = np.bitwise_and(spikes['times'] >= SP_times[0], spikes['times'] <= SP_times[1])

# Count the number of clusters during SP time period and compute firing rate
from brainbox.population.decode import get_spike_counts_in_bins
counts, cluster_ids = get_spike_counts_in_bins(spikes['times'][SP_idx], spikes['clusters'][SP_idx],
                                               np.c_[SP_times[0], SP_times[1]])
fr = counts / (SP_times[1] - SP_times[0])

Downloading: /home/runner/Downloads/ONE/openalyx.internationalbrainlab.org/histology/ATLAS/Needles/Allen/average_template_25.nrrd Bytes: 32998960

100%|██████████| 31.470260620117188/31.470260620117188 [00:03<00:00,  9.73it/s]

Downloading: /home/runner/Downloads/ONE/openalyx.internationalbrainlab.org/histology/ATLAS/Needles/Allen/annotation_25.nrrd Bytes: 4035363

100%|██████████| 3.848422050476074/3.848422050476074 [00:01<00:00,  2.81it/s]
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/one/util.py:543: ALFWarning: Multiple revisions: "", "2024-05-06"
  warnings.warn(f'Multiple revisions: {rev_list}', alferr.ALFWarning)
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/one/util.py:543: ALFWarning: Multiple revisions: "", "2024-05-06"
  warnings.warn(f'Multiple revisions: {rev_list}', alferr.ALFWarning)

Example 2: Find RFM stimulus positions and timepoints

# Find out at what times each voxel on the screen was turned 'on' (grey to white) or turned 'off' (grey to black)
from brainbox.task.passive import get_on_off_times_and_positions

RF_frame_times, RF_frame_pos, RF_frame_stim = get_on_off_times_and_positions(RFMap)

# Find times where pixel at location x=1, y=4 on display was turned 'on'
pixel_idx = np.bitwise_and(RF_frame_pos[:, 0] == 1, RF_frame_pos[:, 1] == 4)
stim_on_frames = RF_frame_stim['on'][pixel_idx]
stim_on_times = RF_frame_times[stim_on_frames[0][0]]

Other relevant examples

• COMING SOON