Title: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Venue: NeurIPS 2025
Accepted: yes
Keywords: neural identity

cell type identification

brain region identification

computational neuroscience

systems neuroscience

self-supervised learning

contrastive learning
Abstract: Neurons process information in ways that depend on their cell type, connectivity, and the brain region in which they are embedded.
However, inferring these factors from neural activity remains a significant challenge. To build general-purpose representations that allow for resolving information about a neuron's identity, we introduce NuCLR, a self-supervised framework that aims to learn representations of neural activity that allow for differentiating one neuron from the rest. 
NuCLR brings together views of the same neuron observed at different times and across different stimuli and uses a contrastive objective to pull these representations together. To capture population context without assuming any fixed neuron ordering, we build a spatiotemporal transformer that integrates activity in a permutation-equivariant manner. Across multiple electrophysiology and calcium imaging datasets, a linear decoding evaluation on top of NuCLR representations achieves a new state-of-the-art for both cell type and brain region decoding tasks, and demonstrates strong zero-shot generalization to unseen animals. We present the first systematic scaling analysis for neuron-level representation learning, showing that increasing the number of animals used during pretraining consistently improves downstream performance. The learned representations are also label-efficient, requiring only a small fraction of labeled samples to achieve competitive performance. These results highlight how large, diverse neural datasets enable models to recover information about neuron identity that generalize across animals. Code is available at: https://github.com/nerdslab/nuclr.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper suggests NuCLR, a contrastive self-supervised learning method to obtain time-invariant functional neuronal embeddings. It is followed by an MLP to generate class labels and the authors test several regimes (Transductive, Transductive zero-shot, and  Inductive zero-shot) to emulate if we had different cell types present during NuCLR pretraining or during the classification MLP finetuning.\nThe paper also presents method validation on several datasets coming from different modalities (electrophysiology, calcium data).\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n* New contrastive architecture, which seems to be better distinguishing the cell types across several datasets, coming from better modalities (electrophysiology, calcium data)\n* Statistically significant improvements, the model standard deviations across seeds is estimated \n* The method is carefully designed (eg decoupled contrastive loss to avoid opposing cells from different mice) and clearly explained\n* Three regimes of model application (Transductive, Transductive zero-shot, and  Inductive zero-shot) are tested\n* The ablation to check the impact of cross-neuron interaction is done.\n\nWeaknesses\n* The datasets used are relatively small and very few cells types are analyzed (max 5 for Bugeon et al). For brain areas there were 10 areas analyzed but distinguishing brain areas is supposed to be much easier than distinguishing cell types. **Suggestion**  - Allen observatory dataset from [1] with calcium imaging containing 100,000 neurons in 6 areas and 13 cell types.\n* The baseline of classic clustering (knn, Leiden) on the learn embeddings is missing. (As in NeuPRINT Table 2). Removing MLP might help to remove the need in the labels at all.\n* Some literature links and comparisons are missing, for instance POYO+[2] also did cell types classification, NEDs [3] did brain regions classification on  IBL repeated site dataset (Fig 4), Wang et al [4] predicted cell types and brain regions from time-invariant embeddings as well (Fig 5 d, e), Weis et al [5] used contrastive learning for neuronal morphologies (section 4 \"Contrastive methods in neuroscience\"). Core-readout models like [4] derived from [6] are actively creating optimal stimuli to derive cell types in different species (marmoset, mice, monkeys) and visual areas (retina, brain) - [7-11]  (section 4 \"Cell type classification\")\n* Only single species and brain area is considered in the work while mice retina from [9] is publicly available.\n* Statement in lines 318-319 * In contrast, our method introduces the first spatio-temporal transformer designed and trained for neuron-level embeddings* is technically incorrect, the authors missed STDNT [12], another transformer, who accounted for cross-neuronal interactions. Its embeddings could be used for further cell types classification as well, though they are not time invariant\n* while i provided a lot of references above - I think this is just the proof that the authors should redo the literature review more carefully and integrate its results, also being more careful with the statements.\n \n[1] de Vries, S. E., Lecoq, J. A., Buice, M. A., Groblewski, P. A., Ocker, G. K., Oliver, M., ... & Koch, C. (2020). A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nature neuroscience, 23(1), 138-151.  \n[2] Azabou, M., Pan, K. X., Arora, V., Knight, I. J., Dyer, E. L., & Richards, B. A. Multi-session, multi-task neural decoding from distinct cell-types and brain regions. In The Thirteenth International Conference on Learning Representations.   \n[3] Zhang, Y., Wang, Y., Azabou, M., Andre, A., Wang, Z., Lyu, H., ... & Hurwitz, C. (2025). Neural encoding and decoding at scale. arXiv preprint arXiv:2504.08201.  \n[4] Wang, EY, Fahey, PG, Ding, Z., Papadopoulos, S., Ponder, K., Weis, MA, ... & Tolias, AS (2025). Foundation model of neural activity predicts response to new stimulus types. Nature , 640 (8058), 470-477.  \n[5] Weis, M. A., Hansel, L., Lüddecke, T., & Ecker, A. S. (2021). Self-supervised graph representation learning for neuronal morphologies. arXiv preprint arXiv:2112.12482.  \n[6] Klindt, D. A., Ecker, A. S., Euler, T., & Bethge, M. (2017). Neural system identification for large 579 populations separating “what” and “where.”. Advances in Neural Information Processing 580 Systems.  \n[7] Burg, M. F., Zenkel, T., Vystrčilová, M., Oesterle, J., Höfling, L., Willeke, K. F., ... & Ecker, A. S. (2024). Most discriminative stimuli for functional cell type clustering. ArXiv, arXiv-2401.  \n[8]  Willeke, K. F., Restivo, K., Franke, K., Nix, A. F., Cadena, S. A., Shinn, T., ... & Tolias, A. S. (2023). Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization. bioRxiv, 2023-05.  \n[9] Hofling, Larissa, et al. A chromatic feature detector in the retina signals visual context changes. Elife, 2024, 13. Jg., S. e86860.  \n[10]  Tong, R., da Silva, R., Lin, D., Ghosh, A., Wilsenach, J., Cianfarano, E., ... & Trenholm, S. (2023). The feature landscape of visual cortex. bioRxiv, 2023-11.  \n[11] Ustyuzhaninov, I., Burg, M. F., Cadena, S. A., Fu, J., Muhammad, T., Ponder, K., ... & Ecker, A. S. (2022). Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex. bioRxiv, 2022-02.  \n[12] Le, T., & Shlizerman, E. (2022). Stndt: Modeling neural population activity with spatiotemporal transformers. Advances in Neural Information Processing Systems, 35, 17926-17939.\n\nQUESTIONS:\nMajor:\n* Could you train your model on Allen Observatory dataset [1] and compare region and cell types classification results with POYO+ [2] - specifically Fig4 B and F panels. \n* Could you train your model on  IBL repeated site dataset and make a comparison with NEDs [3] (Fig 4)?\n* While you train an MLP - what would happen in case of classic clustering (eg knn or Leiden clustering) on top of neuronal time-invariant embeddings? This might help to completely remove the need in labels in the dataset.\n* For the ablation to check the impact of cross-neuron interaction - have you replaced the removed *spatial attention layers* with the *temporal attention layers* to make sure that the performance drop is not cause by just reducing the the depth of the model? If no, could you please repeat the ablation in the suggested manner?\n* In lines 319-321 you say *Unlike NeuPrint and NeurPIR, which are limited to static statistics or session-tuned encoders, our approach supports dynamic, context-aware, and transfer-capable inference of neuronal identity.* Could you please elaborate where NeuPrint is dependent to static statistics or session-tuned encoders ?\n\nMinor:\n* Could you provide confusion matrixes for the datasets and prediction done?\n* How would NuCLR perform if we go one level down the Bugeon hierarchy? (eg 11 classes on the type level)\n* In table 3 are the standard deviation also reported across seeds?\n* What exactly is \"multiple seeds\" for the standard deviations in the tables? Two? Three? More?\n\n[1] de Vries, S. E., Lecoq, J. A., Buice, M. A., Groblewski, P. A., Ocker, G. K., Oliver, M., ... & Koch, C. (2020). A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nature neuroscience, 23(1), 138-151.  \n[2] Azabou, M., Pan, K. X., Arora, V., Knight, I. J., Dyer, E. L., & Richards, B. A. Multi-session, multi-task neural decoding from distinct cell-types and brain regions. In The Thirteenth International Conference on Learning Representations.  \n[3] Zhang, Y., Wang, Y., Azabou, M., Andre, A., Wang, Z., Lyu, H., ... & Hurwitz, C. (2025). Neural encoding and decoding at scale. arXiv preprint arXiv:2504.08201.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAuthors proposed NuCLR, a self-supervised framework to infer individual neuron identities (e.g., cell type and brain region) from large-scale population recordings without requiring anatomical labels. NuCLR models both within-neuron dynamics and across-neuron interactions using a spatiotemporal transformer and uses contrastive learning objective that encourages stable and discriminative neuron embeddings over time. Evaluations on multiple open datasets demonstrate it can be zero-shot generalized into completely new population and shows better results over several baseline models.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is technically sound overall and presents a compelling approach. However, not all claims are equally well supported by experimental evidence. For instance, while the authors mention the potential for “real-time”, no experiments are provided to validate latency or deployment efficiency in such a setting.\n\nThe claim of zero-shot generalization to new populations is partially supported. The “Inductive zero-shot” setting appropriately evaluates this scenario, but several baselines in Table 1 are marked as N/A, limiting a full comparison. In the “Bugeon et al. (E vs. I)” setting, a baseline even achieves better performance, which raises questions about the robustness of the claim.\n\nWhile the authors separate “Transductive” and “Transductive zero-shot” settings, these may still involve access to test-distribution features during pretraining, potentially raising concerns about information leakage. A more thorough discussion of this issue would be helpful.\n\nIt would be better to see more detailed descriptions and appropriate citations for key components like the temporal transformer layers and spatio-temporal blocks.\n\nIn the related work section, some statements could be improved to better reflect prior literature. For example, the paper notes that CEED does not incorporate neuronal dynamics or population structure, but it would be helpful to clarify why this omission makes it less relevant or applicable, especially since this work also relies on spike binning, which can reduce temporal resolution and thus may also abstract away detailed neuronal dynamics.\n\nWhile the problem of inferring neuron identity from activity is important, the current form of the paper does not clearly demonstrate a level of significance that stands out for a NeurIPS audience. Improvements in experimental presentation and more comprehensive comparisons may help strengthen this aspect, but at present, the contribution feels incremental rather than transformative.\n\nQUESTIONS:\n1.\tCan the author explain why The results from “Transductive zero-shot” and “Transductive” are technically sound to support their claim or they can add additionally experimental results to support their claim.\n2.\tPlease provide more detail about model architecture. \n3.\tIs there a reason why use Temporal attention followed be spatial temporal attention layer rather than one spatial-temporal layer?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces NuCLR, a self-supervised framework that learns population-aware representations of single neurons directly from large-scale recordings. A spatiotemporal transformer exchanges temporal information within neurons and spatial information across neurons. Training relies on a contrastive loss and neuron dropout. The learned embeddings support zero-shot decoding of both cell type and brain region across four electrophysiology and calcium‐imaging datasets. NuCLR consistently outperforms other baselines and is shown to scale favourably with additional unlabeled data and labeled data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nRecasting functional cell typing as a self-supervised contrastive problem is a novel angle. Performance evaluated on four public datasets shows significant improvements over existing baselines. Data-scaling curves are informative and provide actionable insights. Architecture designs are validated by thorough ablations. Writing is well-structured and easy to follow. \n\nWeaknesses\n\n1.\tDescriptions of baseline methods are too brief. Readers unfamiliar with methods such as NeuPRINT cannot easily compare their differences in algorithm designs.\n\n2.\tIt is unclear how the spike-bin/patch length might blur fast-spiking interneuron dynamics, potentially affecting subclass classification.\n\n3.\tIt is unclear whether there exist different stimulus conditions in these datasets; if so, whether the conditions are shared between training and test sets.\n\n4.\tIt is unclear how the decodability of brain regions and cell types changes in the intermediate transformer layers.\n\nQUESTIONS:\n1.\tCould you provide expanded technical descriptions for NeuPRINT, LOLCAT, and NEMO? \n\n2.\tCould you analyze/discuss the influence of spike-bin/patch length on model performance, especially for different cell types?\n\n3.\tCould you examine the linear decodability of labels from intermediate layers of the trained transformer model?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to infer neuron identity i.e. cell type/region purely from population dynamics via self-supervision. They design their own network, NuCLR, that uses a spatio-temporal transformer encoder with a sample-wise contrastive objective to build representations that can be probed for cell type as well as brain region. Their contrastive loss compares two time windows of the same population of neurons with random neurons dropped. They compare cell types using the neuropixels dataset and include brain regions like IBL to show the efficacy of their approach.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n* Scaling Analysis: The authors show that their approach improves as more unlabeled data is added. This is an impressive finding. Pretraining on unlabeled sessions yields greater downstream gains than doubling labeled examples.\n* Architectural Validation: Ablations show spatial-attention layers are essential for capturing population structure, and neuron-dropout provides meaningful regularization on small datasets.\n* Results: Impressive improvement in neuron and brain region identification in comparison to their chosen baselines. \n\nWeaknesses:\n\n* Additional ablations: From my understanding, the model doesn’t use precise spike-timing patterns but instead uses uniform binning. This makes sense from the perspective of building a network. But this discards subpatch timing. I didn’t see any ablation on patch length and I was thinking this would be important. \n* Additional baseline: I think a good comparison would also come from using POYO+. The encoder in POYO+ [1] has a fairly similar architecture in my view and could also be probed in a similar way to identify cell type or region. If the authors think the comparison is not feasible, I would also be interested in gaining some intuition between the differences in POYO+ and the NuCLR and the benefits of either approach. \n\n[1] Azabou et. al. Multi-session, multi-task neural decoding from distinct cell-types and brain regions. ICLR 2025.\n\nQUESTIONS:\nTemporal Window: Could a discussion on how to choose Tmax? It seems important but I would appreciate what the design considerations were.\n\nLIMITATIONS:\nDiscussed adequately.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}