Overview of the NDR | About the data used in this tutorial | Data formats | Raster format | Binning the data | Determining how many times each condition was repeated | Performing a decoding analysis | Creating a Datasource (DS) | Creating a feature-preprocessor (FP) | Creating a classifier (CL) | Creating result metrics (RM) | Creating a cross-validator (CV) | Running the decoding analysis | Plotting the results | Plotting the main results | Plotting confusion matrices | Saving the results | Running an analysis using the pipe (|>) operator

Datasources (DS) | Implementing an DS: methods and data formats | Example of internals of DS objects using the ds_basic object | Feature preprocessors (FP) | Implementing an FP: required methods and data formats | training_set | test_set | Example of internals of FP objects using the fp_zscore | Classifiers (CL) | Implementing a CL: required methods and data formats | Example of internals of CL object using the cl_max_correlation | Result metrics (RM) | Implementing an RM: required methods and data formats | aggregate_CV_split_results() method | aggregate_resample_run_results() method | Example of result metrics | Cross-validators (CV) | Implementing a CV: required methods and data formats | Example of cross-validators

Testing invariant neural representations using the NDR | Using the Zhang-Desimone 7 object dataset to test position invariance | Binning the data | Creating a classifier and a preprocessor | Using the ds_generalization to train and test at different locations | Training and testing at all locations | Plotting the results

Zhang-Desimone 7 object dataset | Accessing the dataset | Qi-Constantinidis pre and post training dataset | Isik 26 letter MEG dataset

Raster format | Checking if data is in valid raster format | Example raster-format data | Binned format | Checking if data is in valid binned format | Example binned-format data