Figure 1: NeuroLens framework. (A) Model overview. A day-adaptive encoder maps spike counts at each time bin to latent representations, and an autoregressive (AR) predictor shared across days predicts future latents from past latents. The model is trained using a latent prediction loss together with SIGReg loss to prevent representation collapse. (B-C) Day-adaptive cross-attention encoder based on calibration data. At each time bin, an MLP independently projects each neuron’s spike count into a neuron token. These tokens serve as the keys and values of the cross-attention module. Because attention is permutation-invariant, we distinguish neuron identities using calibration trials sampled from each recording day. From these trials, we compute firing-rate statistics, autocorrelograms (ACGs), and population coupling to generate a signature for each neuron. A second MLP maps these signatures to neuron embeddings, which are concatenated with brain-region embeddings and added to the corresponding neuron tokens. Learned queries then aggregate the variable-length sequence of neuron tokens through cross-attention, producing a fixed number of latent embeddings for the AR predictor. Processing each time bin independently prevents temporal leakage, while calibration-based neuron embeddings enable adaptation to previously unseen neural populations across recording days.## 4 Methods 出處: NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings（arXiv）
Figure 3: NeuroLens learns neural representations that generalize across time, enabling rapid adaptation to unseen neural populations and more stable decoding than SOTA baselines. (A) The mouse makes increasingly accurate decisions about the visual stimulus location from April to June, while the number of simultaneously recorded neurons decreases over time. We pretrain on the first 39 days and test generalization on the held-out last 14 days. (B) Across 14 held-out days, NeuroLens outperforms baselines in choice and movement decoding, demonstrating better generalization across time. (C) Comparison of predicted movement-related behavior averaged across all trials (top) and predicted behavior from single trials (bottom), shown to demonstrate that the baselines can predict trial-to-trial variability in behaviors. The R2R^{2} values for both trial-average and single-trial behavior are shown. The dashed line shows the stimulus onset. (D) T12 and T15 show clear day-to-day distribution shifts in neural activity. We hold out the last 5 and 9 days, respectively, for testing and pretrain on the remaining days. (E) NeuroLens (FT, full fine-tuning) consistently outperforms baselines in phoneme and sentence embedding decoding across 5 (T12) and 9 (T15) held-out days, reflecting better generalization over time. Lower phoneme error rate (PER) and higher top-5 sentence retrieval accuracy indicate better decoding performance. (F) Latent trajectories for different phoneme onsets, projected onto the first three PCs, across held-out days in T15. Dark (light) lines show averaged (individual) time windows. NeuroLens shows clearer phoneme separation than causal NDT, indicating better held-out adaptation, quantified by LDA balanced classification accuracy.### 5.2 Generalization Across Time 出處: NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings（arXiv）
