allensdk.brain_observatory.ecephys.stimulus_analysis.natural_scenes module¶
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class
allensdk.brain_observatory.ecephys.stimulus_analysis.natural_scenes.
NaturalScenes
(ecephys_session, col_image='frame', trial_duration=0.25, **kwargs)[source]¶ Bases:
allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis.StimulusAnalysis
A class for computing single-unit metrics from the natural scenes stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
- session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) ns_analysis = NaturalScenes(session)
- or, alternatively, pass in the file path::
- ns_analysis = NaturalScenes(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
ns_analysis = NaturalScenes(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
- metrics_table_df = ns_analysis.metrics()
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METRICS_COLUMNS
¶
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frames
¶
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images
¶ Array of iamge labels
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images_nonblank
¶
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classmethod
known_stimulus_keys
()[source]¶ 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.
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metrics
¶ Returns a pandas DataFrame of the stimulus response metrics for each unit.
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name
¶ Return the stimulus name.
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null_condition
¶ Stimulus condition ID for null (blank) stimulus
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number_images
¶ Number of images shown
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number_nonblank
¶ Number of images shown (excluding blank condition)
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allensdk.brain_observatory.ecephys.stimulus_analysis.natural_scenes.
image_selectivity
(spike_means, num_steps=1000)[source]¶ Quantifies how selective a cell is for images, based on Quian Quiroga et al., 2007. A value of 0 indicates the cell responds the same no mater what the image. While if the neuron only responds to a single image it will have a selectivity of 1 - 2/N (1.0 and N goes to inf).
Parameters: - spike_means : array of floats
Averaged spiking responses to a series of images for a given neuron
- num_steps : int
Number of threshold values used to build response distribution (default to 1000 as in Quian paper)
Returns: - selectivity : float
selectivity of neuron to images