Publications Conference papers
TRACE: Contrastive learning for multi-trial time series data in neuroscience
Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 79837–79866
Summary
This work presents TRACE, a self-supervised contrastive learning method for multi-trial time series data in neuroscience, which averages across different subsets of trials to generate positive pairs and creates two-dimensional embeddings. The approach demonstrates superior performance on simulated data and captures biologically relevant continuous variation and cell-type-related cluster structure in neural recordings.
Citation
Schmors, L., Gonschorek, D., Böhm, J. N., Qiu, Y., Zhou, N., Kobak, D., Tolias, A., Sinz, F., Reimer, J., Franke, K., Damrich, S., Berens, P. (2026). TRACE: Contrastive learning for multi-trial time series data in neuroscience. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 79837–79866. https://doi.org/10.52202/085713-2667