Training STNeuroNet

For training the network with any data, the data needs to be in the compatible format and naming. Specifically, the data should be saved in name_processed.nii.gz and the neuron masks should be saved as FinalMasks_name.mat formats, in which “name” is the name of the data. Given such data, the demo codes listed below will prepare the data for training (filtering the recordings and generating temporal labeling). The Python codes will launch the network as well, but for MATLAB, the user needs to run the network in train mode after preprocessing is over. Please refer to Section 4 for detailed instructions on how to run STNeuroNet from the command prompt.

Python demos:

• demo_train.py: shows how to prepare training data and train STNeuroNet
• RunThresh.py: demo on how to find the optimal thresholds for the postprocessing step from Allen training data for 10 cross-validation trained networks

MATLAB demos:

• demo_preprocess.m: demonstrates how to apply preprocessing to a data from Allen
• demo_optThresh.m: finding the optimal thresholds for the postprocessing step from a predefined Allen training dataset