References

Every paper behind a model, a metric or a training trick in the benchmark.

[nuclr]

Arora et al. (2025). Know Thyself by Knowing Others: Learning Neuron Identity from Population Context. NeurIPS.

[poyo]

Azabou et al. (2023). A Unified, Scalable Framework for Neural Population Decoding. NeurIPS.

[poyo_plus]

Azabou et al. (2025). Multi-session, multi-task neural decoding from distinct cell-types and brain regions. ICLR.

[tfixup]

Huang et al. (2020). Improving transformer optimization through better initialization. ICML.

[iblbwm2025]

IBL et al. (2025). A brain-wide map of neural activity during complex behaviour. Nature.

[autolfads]

Keshtkaran et al. (2022). A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nature Methods.

[no_tears]

Nguyen and Salazar (2019). Transformers without tears: Improving the normalization of self-attention. IWSLT.

[lfads]

Pandarinath et al. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods.

[nlb]

Pei et al. (2021). Neural Latents Benchmark'21: Evaluating latent variable models of neural population activity. NeurIPS D&B.

[possm]

Ryoo et al. (2025). Generalizable, real-time neural decoding with hybrid state-space models. NeurIPS.

[lolcat]

Schneider et al. (2023). Transcriptomic cell type structures in vivo neuronal activity across multiple timescales. Cell Reports.

[cebra]

Schneider et al. (2023). Learnable latent embeddings for joint behavioural and neural analysis. Nature.

[ndt2]

Ye et al. (2023). Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity. NeurIPS.

[ndt]

Ye and Pandarinath (2021). Representation learning for neural population activity with Neural Data Transformers. arXiv:2108.01210.

[nemo]

Yu et al. (2025). In vivo cell-type and brain region classification via multimodal contrastive learning. ICLR.

[rrr]

Zhang et al. (2026). Exploiting correlations across trials and behavioral sessions to improve neural decoding. Neuron.

[neds]

Zhang et al. (2025). Neural Encoding and Decoding at Scale. ICML.

[mtm]

Zhang et al. (2024). Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. NeurIPS.