NWB Workshops and Hackathons
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Key Investigators
- Aaron Milstein (Stanford)
- Ivan Raikov (Stanford)
- Ben Dichter (Stanford)
Project Description
We would like to define a standard way to associate data points in an NWB file with multiple user-defined categorical labels, and then be able to selectively read from a dataset just the data points that satisfy a boolean filter based on those labels. An example would be a dataset of UnitTimes containing spike trains recorded from an extracellular electrophysiology experiment. Each neuron in the dataset could be assigned a categorical label according to its 1) cell_type, 2) feature_selectivity (grid cell or head direction cell?), 3) active during sleep / wake?, etc. Then the user could read into memory the spikes from all cells that match a query for ‘cell_type’ == ‘basket_cell’, ‘selectivity’ == ‘running_speed’, ‘behavioral_state’ == ‘sleep’, e.g.
Objectives
- Create a CategoricalLabels specification for assigning categorical labels to cell_identifiers in a SpikeTimes NWB dataset.
- Write a python function that takes in 1) a PyNWB SpikeTimes dataset object, 2) a newly-defined PyNWB CategoricalLabels object, and 3) a filter query in the form of a python dictionary with category_names as keys and lists of category_labels as values. Have it return a list of cell_ids that satisfy the filter, possibly with RangeReferences to the actual corresponding values in the data array.