Machine learning for neural decoding

Error bars represent the mean /- ‐ SEM across cross- ‐validation folds. X's represent the R 2 values of each cross- ‐validation fold. Note the different y- ‐axis limits for the hippocampus dataset. Figure 4: Example results with limited training data Using only 2 minutes of training data for motor cortex and somatosensory cortex, and 15 minutes of training data for hippocampus, we trained two traditional methods (Wiener filter and Kalman filter), and two modern methods (feedforward neural network and LSTM). Example decoding results are shown from motor cortex (left), somatosensory cortex (middle), and hippocampus (right), for these methods (top to bottom). Ground truth traces are in black, while decoder results are in the same colors as previous figures. Figure 5: Decoder results with varying amounts of training data Using varying amounts of training data, we trained two traditional methods (Wiener filter and Kalman filter), and two modern methods (feedforward neural network and LSTM). R 2 values are reported for these decoders (different colors) for each brain area (top to bottom).