Spikebench: An open benchmark for spike train time-series classification
Fig 2
Spike train classification metric values (top panel—accuracy, bottom panel—AUC-ROC) for the retinal neuron activity dataset on a range of models.
The task is defined as binary classification of the stimulus type (“white noise checkerboard” or “randomly moving bar”), with the test set balanced in class distribution by undersampling (that is, accuracy = 0.5 corresponds to chance level). Models are ranked in ascending order of the median metric value. The “simple baseline” model tag corresponds to spike trains encoded with 6 basic distribution statistics, the “raw” tag implies that the model has been directly trained on ISI time-series data without feature extraction. The “tsfresh” tag corresponds to encoding with the full set of time-series features. “ISIe” stands for interspike-interval encoding of the spike train, “SCe” stands for spike-count encoding. “ISIe + SPe” means that feature vectors corresponding to both types of encoding are concatenated. InceptionTimePlus, FCNPlus, and ResNetPlus refer to implementations in the PyTorch-based tsai package.