Spike train classification metric values (for imbalance-robust metrics) for the retinal neuron activity dataset on a range of models.

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, ResNetPlus and XceptionTimePlus and refer to implementations in the PyTorch-based tsai package.

“simple baseline”模型标签对应采用6种基础分布统计量进行编码的脉冲序列（spike train）；“raw”标签表示模型直接在峰间期（interspike interval, ISI）时间序列数据上完成训练，未执行特征提取步骤。“tsfresh”标签对应采用全量时间序列特征集进行编码。“ISIe”指代脉冲序列的峰间期编码；“SCe”指代脉冲计数编码。“ISIe + SPe”表示将两种编码方式对应的特征向量进行拼接。InceptionTimePlus、FCNPlus、ResNetPlus以及XceptionTimePlus均为基于PyTorch的tsai工具包中的实现版本。