Appendix 2: From Neural Recordings to Interpreting Decoding Accuracy
Ethan Meyers1 and Gabriel Kreiman2
1Brain and Cognitive Sciences, MIT

2Children's Hospital Harvard Medical School and Swartz Center for Theoretical Neuroscience, Harvard University

In this appendix we outline a procedure to ‘decode information’ from multivariate neural data.  We assume that neural recordings have been made from a number of trials in which different conditions were present, and our procedure produces and estimate of how accurately we can predict the presents of these conditions in a new set of data.  We call this estimate of future prediction accurately the ‘decoding/readout accuracy,’ and based on this measure we can make inferences about what information is present in the population of neurons and also on how this information is coded.   The steps we cover to obtain a measure of decoding accuracy include: 1) formatting the neural data, 2) selecting a classifier to use, 3) applying cross-validation to random splits of the data, 4) evaluating decoding performance through different measures, and 5) testing the integrity of the decoding procedure and significance of the results.  We also discuss additional topics including:  1) how to examine questions about neural coding using feature selection, different binning schemes, and different classifiers and 2) how to evaluate whether invariant/abstract variables are contained in a dataset by training and testing a classifier using data recorded under different conditions. 

Key words: statistical learning, electrophysiology, neurophysiology, local field potentials, spike trains, classification performance, cross-validation, feature extraction, neural coding