Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning

This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in "Najafi, Farzaneh, Gamaleldin F. Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P. Cunningham, and Anne K. Churchland. "Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning." Neuron 105, no. 1 (2020): 165-179.” Each NWB file represents the data and metadata associated with one recording session. In each NWB file, the metadata related to the session (mouse name, session date/time, lab/institution name, etc.) can be found under "general". Information related to ROI-segmentation such as ROI mask, ROI type (excitatory or inhibitory), poor or good quality, etc. can be found under "modules/Image-Segmentation/pln-seg". Trial information (e.g. start, end times, trial types, trial outcomes, etc.) can be found under "trials". Recorded trial-segmented neuronal responses aligned to different time event (e.g. stimulus start, animal choice, etc.) can be found under "modules/ Trial-based-Segmentation". A jupyter notebook presenting in detail how to work with NWB files is provided at https://github.com/ttngu207/najafi-2018-nwb/blob/master/notebooks/Najafi-2018_example.ipynb.