Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Authors: Vinam Arora
Divyansha Lachi
Ian Jarratt Knight
Mehdi Azabou
Blake Aaron Richards
Cole Lincoln Hurwitz
Josh Siegle
Eva L Dyer
Venue: NeurIPS 2025 poster ()
Keywords: neural identity
cell type identification
brain region identification
computational neuroscience
systems neuroscience
self-supervised learning
contrastive learning
Primary area: neuroscience_and_cognitive_science
TL;DR: We present NuCLR, a self-supervised framework that learns high-quality, population-aware neuron-level embeddings directly from spike train data using a spatio-temporal transformer and tailored contrastive loss.

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.

Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Vinam Arora It Divyansha Lachi', Ian J. Knight', Mehdi Azabou', Blake Richards 3.4 Cole Hurwitz?, Joshua H. Siegle", Eva L. Dyer'
University of Pennsylvania, "Columbia University, McGill University, *Mila, 3 Allen Institute for Neural Dynamics
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 neuron's 's identity, we introduce NuCLR, self-supervised framework that aims to learn representations neural activity that allow for differentiating together views of the same neuron observed at different times and across different stimuli and uses contrastive objective to pull these representations together. To capture population context without assuming any fixed neuron ordering, we build spatiotemporal transformer that integrates activity in a permutation-squiiaaiin manner. Across multiple electrophysiology and calcium imaging datasets, a linear decoding evaluation top of NuCLR representations achieves new state-of-the- art for both cell type and brain region decoding tasks, and demonstrates strong zero-shot generalization unseen animals. We present the first systematic scal- ing 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- subel-efficient, requiring only small fraction of labeled samples achieve competitive performance. These results highlight how large, diverse neural datasets enable models to recover infor- mation about neuron identity that generalize across animals. Code is available https::/ thub. com wwtmm//erdldaannllllmce
neuron from the rest. NuCLR brings
Introduction
Neurons operate in ways that reflect their cell type, connectivity, and the brain region in which they are embedded [5S] 46 37]. Understanding how these structural and physiological factors shape
neural activity an important open question [37] and is central modern theories of neural function
[16 10] Cell type- and circuit-specific effects are also increasingly implicated in neurological disorders, where it has been shown that particular neuronal subtypes can selectively affected in neurodegenerative disease [33 29 and that neuron subtypes can be targeted to improve efficacy in real time in closed-loop brain stimulation [57]. Together, these directions highlight a broader need to understand how features such cell type and brain region contribute to neuron's 's role in the brain, motivating the development of methods that can extract aspects of neuron identity.
Much of what we know about neuronal cell types and their connectivity comes from molecular, anatomical, and morphological labeling techniques that tag neurons based on gene expression profiles
Conttcct vinam @ upenn.edu. dyerl @upennupeeencccc edu Reviewed on OpenReview: http::/wwwwpp//oooo/a//////////a
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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or structural features [55 45, 12 40 These approaches have revealed rich taxonomy of neuronal classes, but they are resouree intensive and typically provide only a sparse labeling of one or a few classes at a time. Currently, there is no molecular access to different cell types or projection classes in nonhuman primates and humans [7 32 22), which has created major roadblocks in studying neuron diversity in humans. For these reasons, there is a growing interest in methods that can infer aspects of neuronal identity directly from large-scale neural population activity.
Recent work has started to explore deep learning approaches for tackling this question
38
30
6
54 [2]
Early methods relied on intrinsic single-neuron features such as waveform shape or interspike interval profiles [38 46 6 54], but these signals capture only part of what distinguishes one neuron from another. More recent approaches have attempted to incorporate some information about the ongoing population activity [30 [2), but reduce the surrounding activity to a fixed low-dimessional summaries, which obscures the fine-grained coordination patterns that are often key to neuronal identity. Furthermore, these models usually require retraining or finetuning on each new set of neurons, limiting their ability to generalize in zero-shot manner across animals. These limitations point to the need for methods that leverage rich population context while producing neuron-level representations that transfer robustly across recordings.
To fill this gap, we introduce NuCLR, a self-supervised approach for learning neuron-level represen- tations from large-scale, neural activity datasets. At a high-level, the goal of NuCLR is to build a representation that is stable across time and makes each neuron distinguishable from other neurons in the population, thereby capturing abstract features that reflect its identity. Within these representa- tions, we expect to uncover information related to cell type, brain region, connectivity, and potentially other latent attributes. To learn these representations in a self-supervised manner, NuCLR constructs two population-level views by sampling and encoding different temporal segments from the same recording, and applies a contrastive objective that treats representations of the same neuron across these views as positives and all other neurons in the population negatives. To allow for integration of rich population context in each neuron's 's representation, we introduce a spatiotemporal transformer that aggregates information from surrounding neurons without assuming fixed ordering requiring session-specific alignment. Together, these components allow NuCLR to learn population-awee aware representations that generalize reliably across animals, enabling zero-shot decoding of neuron-level attributes from previously unseen recordings.
To evaluate the identity-related information learned by NuCLR, we first pretrain the model in an unsupervised manner and then train linear classifiers on the resulting representations to decode cell type and brain region. Across multiple electrophysiology and calcium imaging datasets, NuCLR achieves a new state-of-the-art on both tasks, We show that NuCLR's population-based self-supervision yields representations that transfer robustly across sessions and animals in a zero-shol manner, supporting decoding neuron identities on entirely new subjects without retraining or additional metadata. In addition to strong zero-shot generalization, NuCLR is highly label efficient: using only 12.5% of labels to train the classifier still outperforms all baselines even when they use all labeled data. We further provide the first systematic scaling analysis for neuron-level tasks, demonstrating that increasing the number of animals used in pretraining consistently improves zero-shot cell type and region decoding accuracy. On the Allen Visual Coding Neuropixels dataset [41), doubling the unlabeled pretraining data improves cell-type decoding more than doubling the supervised labels, highlighting the advantages of scaling pretraining across many animals.
Our core contributions are:
We introduce NuCLR, a self-supervised framework for learning neuron-level representations from neural population activity. After unsupervised pretraining, these representations support linear decoding of cell type and brain region and set a new state-of-the-arr across multiple elcctrophysiology and calcium imaging datasets.
We show that NuCLR generalizes in a zero-shot manner: the same pretrained model and em- beddings transfer robustly to entirely new sessions and animals without retraining or requiring additional metadata, enabling out-of-the-box decoding of cell type and brain region.
We provide the first systematic scaling analysis for neuron-level representation learning, demon- strating that increasing the number of animals used during pretraining yields consistent gains in zero-shot cell-type and region decoding. This underscores the value of large, diverse unlabeled neural corpora for inferring neuronal identity from activity alone.

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Lar
D..000
Linear
Temporal Attention
Temporal
X1
Attention
y1
X2
Linear
Temporal
Spatial Attention
Temporal Attention
Attention
X
Linear
Temporal
Temporal Attention
Attention
Neural Activity ibinned & patched]
Activity Tokens
Temporal Layers
Spatio-temporal Layers
Average Poaling
B
T
DROP
DROP
AT's
AT
max
Spatiotemporal
Spatiotemporal Encoder
Encoder
Neuron representations
Sample-wise
Contrastive Loss
Figure Overview of the NuCLR framework. (A) model takes as input the activity neural population over fixed context window. Each neuron's 's activity treated as a patched-token sequence and encoded across time using temporal transformer blocks. These temporally encoded tokens are then passed through spatio-temporal transformer layers that attends across neurons incorporate population context. Finally, the model outputs vector for each neuron in the population.
trained with a sample-wip contrastive objective. We sample two temporally-spaces views of the same population and encode them into neuron-level representations. Neurons are also randomly dropped before encoding to build robustness to partial observations. Representations of the same neuron in both views are pulled closer, and all other neurons in the population considered negatives and pushed apart. As result, each ncuron's representation encouraged stable across time and distinguishable from other neurons the population.
(B) The resulting representations
2 Method
The goal of this work is to design and train deep learning models that, given the activity of population of neurons, produce a latent representation for each neuron that captures its identity within the population. Our approach is grounded in two core principles:
1. Capturing neuron's 's identity requires not only its own activity but also the surrounding population activity, which provides essential contextual information.
neuron's 's identity remains stable over time, so representations computed from different temporal segments of the population activity should be consistent with another.
Designing models that effectively incorporate population context challenging because the number of recorded neurons varies widely across experiments, individuals, and sessions, making non- non-trivial define consistent input structure or population-leys operation. Recordings also span multiple experimental conditions iineluding different stimuli and to generalize across recording sessions. Together, these sources of variability make it difficult to learn neuron-level representations that are truly seneral-purpose and transferable to new recordings. To satisfy our principles while accommodating these sources of variability, we introduce model architecture (Section) 2.1 and training objective (Section 2.2
which makes it hard for model

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2.1 Model architecture
We aim to learn a function that maps the collective activity of a neural population to general-purpost neuron-level representations. To support learning across recordings from different sessions and animals, each with a varying number of neurons, this function must be applicable to populations of arbitrary size. Moreover, we do not assume any relational bias between neurons, and treat them as a -enuuuaatioo-uuivaiiiantt set where all neuron-neuron interactions are modeled. Finally, to enable representations that reflect a neuron's 's role within the circuit, the function should allow for information exchange among neurons, allowing each one to contextualize its activity relative to the population. We formalize this as set-to-set transformation:
(2,. yyN -
where X,, denotes the spike-train sequence of the n-th neuron, and y,, is its corresponding repre- sentation. We implement F using attention treeetion-based transformer blocks (Figure TA), which naturally support set-to-set mappings through their cemmutation-equivrriaa structure [24].
Given spike trains of a population of N neurons over temporal context window Tex. we begin by binning the spike traing] and partitioning the bins into non-overlapping temporal patches of length parch- The resulting binned-and- and-patched activity of n-th neuron is sequence of vectors X, (Xn,1, Xn,2, ...,,...,,,,,)) P), where / = Tax / is the number of patches. Each patch is linearly
projected into D-dimensional latent space, yielding a token sequence (0) = (z (2..1....., 1............ P, for each neuron.
These latent tokens first processed independently per neuron using Lr layers of self-attention. which we refer to as temporal transformer layers. Each temporal layer Tump operates on the patch sequence of single neuron:
(zn,1
(1+1) R.
Twemp ((x.))....,, (PP)
An E [N].
Within these layers, the temporal structure is maintained by the use of rotary position embeddings 3]. which encode the relative timing of each patch without requiring absolute positional indices.
Following the stack of temporal layers, we apply LST layers of spatio-temporal transformer layers, which alternate between spatial and temporal attention (Figure [TA) In the spatial transformer layer, tokens at the same time index interact across the population via shared transformer T Tspatial:
(1+1) 1
(1+1) N. j = Tsparial ( (Iz(.)- 2N.
Vp [P]
No positional embeddings were used in the spatial blocks. The temporal blocks in these layers reuse the structure defined in Equation (2). So, T Tppatistempraal -Taputial Tremp.
Finally, to obtain a fixed-dimensional representation for each neuron, we apply mean pooling over the temporal axis:
1 Yn P
Annal nn
Vn
(4)
The core idea behind this architecture is to first build temporally informed representation of each neuron based solely on its own activity. This is accomplished through the initial stack of temporal transformer layers, which process each neuron's 's token sequence independently. The resulting latent sequence captures the internal dynamics of each neuron over time Next, the spatial transformer layers allow neurons to exchange information at each timepoint, injecting population-leve context into each neuron's 's representation. By alternating spatial and temporal layers, information received from other neurons at a given timepoint can be distributed to all other timepoints, creating more informed tokens to query the population again through the next spatial layer.
This architecture can also be readily adapted for calcium imaging data, with minimal modifications
(see Appendix B.3). Additional details are provided in Appendix B
'We use fixed bin-size of 20ms, and also present sweep over this value in Appendix A.1 We also based approach, and discuss it Appendix(A.2
attempted temporal only layers, while not essential for performance, contribute to improved computational 'The initial
efficiency. Please see Appendix (.oendii(A.61fi for longer discussion.

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What is considered population? For electrophysiology datasets, we treat each probe insertion as a distinct population, both for the spatial transformer layers and for the contrastive loss, where negative pains are limited to neurons recorded on the same insertion (i.e. a single Neuropixels [42] probe). In early experiments, we observed that allowing interactions across insertions led the model to cluster neurons based on probe identity rather than biologically meaningful properties. This is undesirable, as our goal is to produce representations that reflect intrinsic neuronal attributes such as brain region and cell type.
2.2 Self-supervised training objective
Our goal is to learn the transformation FF in Equation (I such that produces neuron-level repre- sentations that capture biologically meaningful properties such as cell type and brain region. Since such labels are costly and difficult obtain at scale, we adopt self-supervised approach. NuCLR leverages the natural spatial and temporal structure present in neural population activity and applies contrastive learning objective to train without requiring any explicit labels.
We begin by sampling two Tax-long windows, or views, of neural population activity within ATmax of each other (Figure(BB) Because identity-related properties such cell type brain region remain static, we encode this temporal -nvvaraance using contrastive loss that encourages corresponding neurons across the two views produce similar representations. At the same time, the representation should capture each neuron's ' distinct identity, so representations of different neurons within a population are pushed apart.
To promote robustness to partial observations and prevent overfitting to specific populations, we apply neuron dropout independently each view, randomly removing up 50% neurons per view (Appendix B.I). As result, the number of neurons present in each view usually differs, and only subset of neurons in one view have a corresponding neuron in the other view. We define the indices of neurons presented in both views as
M {n, m) neuron n view corresponds neuron m in view 2};
(5)
which identifies all valid positive pairs for contrastive training.
Let X = (XI:.... X,,) ) and X² [X?:... ... X2,) } denote the two augmented views after neuron dropout. The encoder maps these views into corresponding sets representations:
F( = (yt (yt...., (.. } and F(L²) = (y?.....,'", y%,
(6)
Similar to SimCLR 9 these representations are passed through a projection head g(.) one- hidden-layer MLP to obtain projected vectors p" = 9(y"), where E {1, 2) indexes the view. We then apply an InfoNCE based contrastive loss over the set of valid positive pairs M:
C(F 1(F,91&,,7
1
Σ
log
exp ((p), /T7//T)
M
exp ((p), ph))/)/))))))))) + Σ exp ((P).P2)/T)
(n.m)EM
(n,
+ symmetric term for view 2 to
where ( denote cosine-siminn simila-ii and r is a temperature hyperparameter This loss encourages representations of the same neuron to be close across views while pushing apart those of different neurons within the same population. As result, the model learns neuron-level representations that temporally stable and discriminative with respect their functional roles.
A key distinction from standard SimCLR 9 lies in how we handle the minibatch setting. In NuCLR, the contrastive loss is computed independently within each view pair. That is, when training on minibatches containing samples from different animals or sessions, we do not treat neurons across samples negatives. This design choice avoids an overabundance of easy negatives in the denominator of Equation 7 which can degrade contrastive learning performance [18]. As result, the number of negatives per sample is limited to the neurons within single recording (typically in the hundreds). This motivates our omission of positive-pai similarity terms from the
'We found slightly better results by using different temperatures for within- view and across-vis view cosine similarities. Please refer Appendix A.5 for more details.
5

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denominator moolmonntor-following the decoupled contrastive loss (DCL) 53 which has been shown to improve performance in regimes with few negatives.
In practice, for a minibatch containing B independently sampled view pairs (potentially from different recordings), the overall loss is computed weighted average across samples:
B Σ No.
Lranch (F,9)
(8)
hh No b=1
where x/.12 is the b-th view pair, and N₂ is the number of valid positive pairs (i.e., neurons present in both views after dropout). The inclusion of the N₂ terms helps to deal with the imbalance otherwise created between views with very different numbers of valid positive pairs. We provide additional details about the model, hyperparameter choices, and other training details in Appendix B
3 Results
3.1
Experimental setup
To test the quality of the learned representations, we follow the standard tinear-vvaluatin protocol used in self-supervised learning 13] We first pre-train the model with our contrastive objective (Section 2.2). then freeze the encoder and compute a single representation per neuron by sequentially sampling windows from each session and averaging the resulting outputs. Using these frozen neuron representations, we train linear classifiers to evaluate performance on two downstream tasks: cell type decoding and brain region decoding.
Evaluation strategies. For each downstream task, we perform evaluation across three generalization settings: (1) Transductive, where the testing populations are seen during self-supervised pretraining. and partial labels from these populations are used to train the classification head; (2) Transductive zero-shor, where the test populations when training the classifier, and (3) Inductive zero-shot, where the test populations are entirely unseen during pretraining, and no fine-tuning is performed on the encoder classifier before evaluation. For all three settings, we use a linear head on the output representations for the classification probe.
present during pretraining, but no labels from them are used
Settings (1) and (2) emulate offline (or post-hoc) analysis of neural recordings, where pretraining is allowed on the test populations. Setting (1) reflects scenario where a subset neurons in a recording is labeled, and the goal is to infer labels for the rest. Setting (2) captures the case where the test population is collected but is not labeled, so used for self-supervised pretraining but cannot be used to train the classification probe. Setting (3) corresponds to the online use-case, where pretrained model must operate on completely new neural populations without any retuning. This final setting is the most stringent and directly tests the model's 's ability to generalize in truly nut-of-the-box fashion.
Baseline methods. We compare NuCLR to several baselines: NeuPRINT 31. a population model that uses summary statistics of neighboring neurons and the recorded behavior; NEMO (54), CLIP-style contrastive approach that encodes individual neurons using their activity autocorrelograms and waveform templates; and LOLCAT (38], supervised model that uses the interspike interval distribution for individual neurons and leverages trial structure in the recording. We refer the reader to Appendix for further details macro Fl -score, which is robust to class imbalance and enables fair comparison across datasets with varying label distributions
implementation of these methods. Our main metric is the
We note that not all methods can be tested across all settings: NeuPRINT learns a neuron embedding table through back-propagation it cannot be tested inductively without re-training. NEMO is designed only for clectrophysiology data and cannot be applied to Bugeon et al. which consists of calcium imaging recordings. Finally LOLCAT is a supervised method so the transductive zero-shot setting is not possible. The performance of baseline methods in such cases will be reported as "N/A".
3.2 Cell type decoding
We begin by evaluating the quality of NuCLR's representations on their ability to predict neuronal cell types. The first dataset used to test our approach is the Allen Brain Observatory Visual Coding
WWe attempted to compare with recent method NeurPIR authors to reproduce their method.
(52) but were not able to receive code from the

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Table 1: Macro Fi-score for cell type classification across different generalization settings. Reported mean = std. dev. across 5 training seeds. N/A indicates the method cannot operate in that evaluation setting.
Dataset
Classes
Setting
NuCLR (Ours)
NeuPRINT
NEMO
LOLCAT
Transd, zero-shot
0.7218 1.0013
0.4020 & 000
0.4256 200144
N/A 0.4121 00000
Allen VC
Ind. zero-shot
0.7200 1 0,0007
N/A
0.4194 a 0.009
Transductive
0,8110 a 0,0025
0.6658 : 0,0000
N/A
0.7205 & 00127
Bugcon
Transd. zero-shot
0.6826 0,0293
0.6362 +
N/A
N/A 0.7463 0.085
Ind. zero-shot
0,6738 0,0663
N/A
N/A
Transductive
0.6101 0,000
0,4952 000022
N/A
0.2900 i: 0078
Bugeon (Subclass)
Transd. zero-shut
0,4014 0,0088
0.3529 1 0,0194
N/A
N/A 0.2418 0.008
Ind. zero-shot
0.3938 à: 0.0656
N/A
N/A
(VC) Neuropixels dataset [1]. which contains 58 sessions, each from a different mouse. Fach animal is presented visual stimuli ranging from drifting gratings to natural images and video. Within the 58 sessions, 16 include optotagged inhibitory neurons labeled as one of 3 subclasses: Pvalb, Sst, or Vip. Since all labeled neurons within session belong to the same subclass, we evaluate performance only in the two zero-shot settings? 5 In the transductive zero-shot setting, all sessions are used during pretraining (including labeled ones), but the decoder is trained and tested on neurons from non- non-overlapping subsets of mice. In the more stringent inductive zero-shot setting, pretraining restricted to unlabeled sessions only. Further details on our data splits and validation methodology are provided in Appendix D
The second dataset that we evaluate is a spatial transcriptomics dataset from Bugeon et. al. [7]. which consists of calcium imaging recordings from 17 sessions across 4 mice, with cell types labeled as excitatory (E) or inhibitory (I), and inhibitory neurons further divided into 5 subclasses (Lamp5, Pvalb, Vip, Sneg, Sst). We test both binary classification (E vs. I) and five-way subclass classification. Additionally, in Appendix A.8 we also test an 11 I-wway subclass classification.
As shown in Table I NuCLR achieves strong performance across both datasets and consistently outperforms all baselines in zero-shot settings. On the Allen VC dataset, NuCLR achieves macro Fl-score of 0.7218 in the transductive zero-shot setting and 0.7200 in the inductive setting setting-more more than 0.29 F1 higher than the next best method (NEMO) in the inductive case. On the Bugeon dataset, NuCLR achieves a macro F1 of 0.6738 on inductive zero-shot E vs. I classification and 0.3938 on the more challenging five-way subclass task on a held-out subject. We also compared with POYO+ [5] on the Allen VC in (Appendix (A.4). a multi-session multi-task decoding method that learns a neuron-level embedding table, and found that NuCLR outperforms these embeddings by strong margin on cell type classification.
These results demonstrate that NuCLR produces stable, transferable neuron representations that generalize to previously unseen populations without retraining, enabling accurate zero-shot decoding of cell type identity across diverse datasets and experimental conditions.
3.3 Brain region decoding
For brain region identification, we evaluate on two electrophysiological datasets with well-curated anatomical annotations: the International Brain Laboratory (IBL) Brain-wide Map [2] and the Steinmetz et al. 2019 dataset (3). The IBI. dataset comprises recordings from 139 mice across approximately 700 probe insertions. Following the evaluation setup used by NEMO, we perform classification across 10 brain regions (Figure (2A). The Steinmetz al. dataset includes 39 recordings from 10 mice, with classification over four regions: HPF, MB, TH, and VIS. Zero-shot test populations correspond to entirely unseen experimental sessions (and thus unseen probe insertions) in the IBL dataset, and to unseen subjects in the Steinmetz et al. dataset. Further details on our evaluation methodology and data folds are provided in Appendix D
As shown in Table 2] NuCLR achieves the highest macro Fl-scores across all evaluation settings. In the inductive zero-shot regime IBL, NuCLR reaches an Fl-score of 0.53 compared to the next best performing method NEMO with a 0.38. We also observe in Figure A that the representations pro-
'The transductive setting cannot allow for proper testing in this case, as a "perfect" classifier only has to infer which of the previously seen sessions does the neuron belong to and the cell- corresponding to that session.
7

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Table 2: Macro Fi-scores for brain region classification across different generalization settings. Reported as mean + std. dev. across 5 training seeds. N/A indicates the method cannot operate in that evaluation selting.
Dataset
Classes
Setting
NuCLR (Ours)
NeuPRINT
NEMO
LOLCAT
Transductive
0.6686 + 0.0044
0.2734 a 00153
0.4188 = 0,0011
0.2851 à 0U000
IBL
10
Transd. zero-shot
0.5343 + 4.0115
0.2531 + 00137
0,3804 = 0.0011
N/A
Ind. zero-shot
0,5295 1 0,0000
N/A
0.3793 1 0.0011
0.2532 1 0.0016
Transductive
0.9594 +: 0,0027
0.4476 +: ource
0.6989
=: 0,0044
0.3205 4 OLDOSS
Steinmetz et. al.
Transd. zero-shot
0.7338 :: 0,0266
0,4122 1 0.0366
0,6681
:: 0,0016
N/A
Ind. zcro-shot
0,5810 + 0.0110
N/A
0,5595 = 0.0048
0.3191 + 0L0111
duced by NuCLR are organized according to brain region in the IBL dataset. Embedding visualizations for the remaining datasets are provided in Appendix C
3.4 Data scaling and label efficiency
We study how zero-shot performance scales with the amount of unlabeled pretraining data and the availability of labeled neurons for training the classification probe (Figure 2B). We increase the amount of data used for pretraining while holding the test populations fixed and distinct from those used in pretraining. To vary the level of supervision, we randomly subsample the labeled neurons used to train the linear classifier (reported as the "label ratio"), where ratio of 1.0 corresponds to using the same full set of labels across all pretraining scales.
We find that for the Allen VC dataset, increasing the number of unlabeled pretraining data leads to dramatic improvements in cell type classification performance. In fact, in some cases, doubling the amount of pretraining data is significantly more beneficial than doubling the number of labeled neurons, as annotated in Figure 33. For the IBL dataset, we observe similar trends: brain region decoding performance continues to improve with additional pretraining data.
As expected, classification accuracy increases when more labeled data is used to train the classifier heads at a fixed pretraining data scale. However, NuCLR is highly label-efficient when only 12.5% of the labeled data is used, it still achieves scores 0.54 and 0.50 on Allen VC and IBL respectively, outperforming all baselines even when they use 100% of the labels. For instance, NEMO reaches only 0.42 and 0.38 under full supervision on these datasets. Taken together, these results show that our approach is both label-efficient and highly scalable, capable of leveraging large unlabeled datasets improve downstream performance with minimal supervision.
B
Allen Visual Coding Neuropixels
IBL Brainwide Map
Brain Regions CE
0.53
craqq
0.70
HB
presoining
HEP
test score
0.65
OLT
NB
0.60
Za Mabokol
0.51
Num prepoining
0.50
seuitono
0.50
0.49-
372
363
0.45
0.2
0.6
0.8
1.5
0.2
0.4
0.6
3.0
label ratio
label ratio
Figure 2: Representation visualization and scaling trends. (A) A 2-D UMAP visualization of NuCLR's neuron representations for the IBL Brainwide Map dataset colored by brain region. (B) Data scaling trends of inductive zero-shot decoding performance for cell type (Allen VC) and brain region (IBL) tasks. The number of animals used pretraining are varied along with the amount of labels used to train the lincar classifier heads (label ratio). Porformance improves with larger pretraining data and amount of supervision. In many cases, increasing the amount of unlabeled pretraining data is more effective than increasing the amount of labeled data.
3.5 Ablations
We conducted a series of ablation experiments to assess the contribution of key architectural and training components in our model (Table 3) First, we removed the spatial attention layers which
8

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enable population-level interactions across neurons Ablating the spatial layers (population context) led large performance drops on both the Allen VC (from 0.72 to 0.55) and IBL datasets (from 0.53 to 0.36). These results highlight the importance of spatial context: integrating information from surrounding neurons provides complementary signals to single-neuron activity and improves the model's is ability to capture features of neuronal identity. It's interesting to note that even with this huge drop in accuracy, our model still outperforms most baselines in this restricted setting.
We also examined the impact of neuron dropout, a regularization technique that randomly masks subset of neurons during train- ing. This component improved performance on the Allen VC dataset but had minimal effect on IBL.. We attribute this difference to dataset scale: Allen VC pre- training dataset includes only 42 populations and therefore benefits more from regularization, whereas IBL comprises over 600 populations and may be less prone to overfitting.
Table 3: Ablation study the Allen Visual Coding and IBL datasets. We test the impact of spatial attention layers and neuron dropout on the Macro-FI score. Values are reported as mean + std. across 5 seeds.
Model Variant
Allen VC (Cell type)
IBL (Brain region)
Full NuCLR
0.7200 i 0.0267
0.5295 1: 0.0010
w/o neuron dropout w/o spatial attn layers
0.6181 = 0.0352
0.5248 +: 0.0164
0.5550 :: 0.0796
0.3573 +: 0.0019
Related Work
Functional organization of brain circuits. From early days of neuroscience and experiments of Hubel and Wiesel [13]. mapping the responses or "tuning" of different neurons has been an important part of building our understanding of how the brain works. At scale, and with modern large-scale datasets, this has allowed for extensive work in characterizing the functional organization in primate V4 [51), in chromatic feature detectors in retina [4), visual cortex in mouse [47, and combinatorial codes in mouse V1 [49]. In vision, maximally exciting or most discriminative inputs [8] 50 47 can be used to find stimuli that help to differentiate neurons with different functional properties.
Deep learning approaches for cell type and brain region classification. Attempts to build deep learning solutions for the problem of extracting cell types and brain regions from neural activity are still nascent (37]. In [38 (46.. interspike interval (ISI) distributions are used for cell type and brain region prediction, respectively. LOLCAT 38 extracts the ISI distribution over individual
trials and builds an multi-head attention layer to attend to subsets of trials and classify neurons in
supervised manner. NEMO (54). PhysMAP (23] and VAE-based models [6] combine waveform
and autocorrelogram views from single neuron; NEMO uses a CLIP-style contrastive loss between both modalities, and Beau al. concatenating both modalities after separate VAB-based encoders are applied to each.
Recent methods have begun explore integration of population context into neuron embeddings. NeuPRINT [30] uses a reconstruction task and stimulus information to learn an embedding table for neurons, and adds population context through set of fixed summary statistics. NeurPIR (52) learns time-invariant neuron representations from population dynamics using a contrastive VICReg loss and pretrained population-level encoder to provide population context for each neuron. While these approaches offer improvements over single-neuron models, their reliance on compressed population features can discard fine-grained structure in the dynamics. Additionally, because these models must be retrained or finetuned for each new recording, they provide limited ability to generalize in a zero-shot manner across sessions or animals.
Channel-level transformer architectures and functional embeddings. A growing line of work applies transformer architectures at the channel or neuron level to generate embeddings from popula- POYO and POYO- learn a unit embedding table and EIT generates a channel-level tokenization; all focus on supervised behavior decoding tasks. NEDS uses both encoding (ncural activity prediction) and decoding (bchavior prediction) objectives, and learns a set of neuron level weights at the decoder which can be used to read out neuron-level attributes. STNDT
tion activity 5 56
27 20
"When ablating the spatial attention layers, the spatial layers were replaced with additional temporal layers match the overall model depth and parameter count. "While POYO+ reports Cre-line and brain-region classification, these results are obtained using session-level averaged latent representations. and are not based on embeddings of individual neurons. See Appendix for more details and an extended analysis of the unit embeddings leamned by POYO+.

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employs a masked modeling objective with both neuron-level and population tokens to achieve strong performance on prediction of neural activity on held-out neurons. However, none of these approaches are designed to learn identity-relevant neuron embeddings or to generalize in zero-shot settings. NuCLR is specifically built for this purpose, with an objective and architecture tailored to recover neuron identity from population activity and transfer robustly across sessions and animals.
Contrastive methods and their use in neural population data analysis. Contrastive learning maps similar examples to nearby points in embedding space while separating dissimilar ones 2). and has achieved broad success across language [1, vision [9], audio [36], and multimodal representation learning [35]. Motivated by these advances, contrastive objectives have increasingly been used to learn representations from neural population activity 26 34 48) [3]. Swap- VAE and MYOW introduce temporal and dropout- coopout-based augmentations for population responses (26 4
Swap-VAE combines contrastive and generative losses across two latent spaces to learn disentangled population embeddings, while MYOW employs a BYOL-based [3] contrastive objective with nearest-neighhor mining to identify harder positives. Urzay et al. apply contrastive metric learning [T] detect change points in neural time series 448). Peterson et al. demonstrate cross-stream self-supervised learning between neural activity and behavior (34). CEBRA uses contrastive learning align recordings across sessions, modalities, and animals 39 While these approaches successfully learn representations of neural activity that can be connected to behavior, they operate at the population level and do not construct embeddings for individual neurons. NuCLR applies contrastive learning at the neuron level: different temporal views of the same neuron serve as positives, all other neurons serve as negatives. This design enables NuCLR learn identity-relevant embeddings for individual neurons in the context of other neurons, rather than global representations which compress the entire population into single embedding.
Discussion
We introduced NuCLR, a self-supervised framework for learning population-aware neuron identity embeddings from both electrophysiology and calcium imaging datasets. Using a spatiotemporal trans- former trained with a contrastive objective, NuCLR captures within-neurow dynamics and population context, and enables zero-shot prediction of neuronal properties in new subjects without supervised labels session-specifie tuning.
Even though we don't 't use stimulus information our pretraining, our current evaluations use datasets with similar underlying task structure. Thus, testing and building robustness to changes in the underlying stimulus distribution will be an interesting line of future research. As we extend the approach to train datasets spanning different tasks, sensory modalities, and behavioral states, may be useful to incorporate task-conditioned encoders, or metadata-aware prompts to support training across heterogeneous datasets.
While our current evaluations show the applicability of NuCLR on both clectrophysiology and calcium imaging, we currently need two different models for each modality. In the future, building a unified pretraining approach for both modalities [9] would allow for a joint model pretraining that could take advantage of both modalities and train at an even larger scale than with a single modality alone.
Our zero-shot scaling results show strong benefits of pretraining with more data on more animals. Thus, we anticipate that NuCLR could be trained on even more data to achieve even more performance gains, and ultimately building a path toward general-purpose neuron identity estimation and foundation for models that integrate across modalities, species. and experimental settings. As larger and more diverse neural datasets emerge, such approaches may help reveal how neuronal diversity shapes computation across brain-wide circuits.
Acknowledgments and Disclosure of Funding
We would like to thank Anna Lakunina, Shivashriganesh Mahato, Alexandre André, and Liam Paninski for insightful discussions and feedback. This project was supported by the NIH (1R01EB029852), the NSF (CAREER IIS-2146072). as well as generous gifts from the CIFAR Learning in Machines & Brains Program and The Hypothesis Fund.
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Appendix
A
Additional Results
A.1 Effeet of bin-size
The encoder used in this work assumes the neural activity is binned with finite bin-size, which sets the minimum time resolution at which the encoder can views the data itself. A fair hypothesis would be that certain cell-types neurons from certain brain-regions would be more (or less) identifiable at a certain bin-size setting.
We test this by performing sweep over the bin-size, and measuring the class-wise and macro Fi-scores. As we see Figure B some kinds neurons do indeed prefer certain bin-sizes, however, the overall effect of this hyperparameter is surprisingly small. In other words, the overall classifier performance relatively robust to the choice of bin-size.
Allen Visual Coding Neuropixels
IBL Brainwide Map
0.92
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Figure 3: Results of bin-size sweep. Classification performance variation for different classes upon sweeping the bin-size hyperparameter of the encoder. "Aggregate" refers to the overall macro-F1 score. Error polygons represents standard error of mean (SEM) measured across pretraining seeds.
A.2 Experiments with spike-based encoder
We also explored a ppkkettokenization strategy inspired by POYO B Unlike POYO, our model aims produce neuron-level embeddings its outputs, and therefore cannot assume learn neuron identities through separate "unit embeddings." Instead, we assign the same learnable embedding to all neurons, that each spike token consists only of its timestamp and this shared embedding. Each neuron's ' spike train is processed using perceiver-style cross-attention layers, which are queried by learnable vectors placed at linearly spaced timestamps-using spacing equal the patch length in our main model. The shared embedding serves only to enable cross-attention over the spike sequence and does not encode any neuron-specits information. The resulting set of tokens is then treated as activity tokens (as shown in Figure and passed through the spatio-temporal transformer identically our main architecture. schematic of this spike-tokenization design is shown in Figure A7A.
We find that this based encoder performs significantly worse the Allen VC cell-type classifi- cation task, while achieving nearly comparable performance on the IBL brain-region classification task (Table We also note that the training stability and convergence speed is much better in the binned-and saneed-and-patched version of the model, as shown in Figure 4B. Alternative implementations of spike tokenization may yield improved results, however, leave that for future work.
Table 4: Doiik-tokenizztion ablation. Values reported mean std. dev. across seeds for the 20ms binning model (main NuCLR), and seeds for spike tokenization based model.
Model Variant
Allen VC
IBL
20ms binning (main NuCLR)
0.7200 0,267
0.5295 00000
Spike-tokenization based
0.6665 0.0177
0.5268 0013
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A
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1300
4030
50030
30099
20000
30000
45000
50093
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Figure 4: Spike-tokenization based encoder. (A) A piike-ookenization layer for the NuCLR encoder. (B) Pretraining-timg time metrics comparison for spike-tokenization version and binning version of NuCLR. These plots are for pretraining on the IBI. dataset.
A.3 Probing intermediate layer representations
In all our experiments, we used a 6-layer implementation of NuCLR, with the first two layers being temporal-only layers followed by spatio-temporal layers (each having 2 transformer blocks). In Figure we measure the cell-type and brain-region decoding accuracies of the embeddings obtained all 6 layers. The results show general improvement of classification performance in the network, with major performance jumps being observed whenever the representation passes through spatial layers. This is another confirmation of the importance of spatial layers, shown in Section
go deeper
Allen Visual Coding Neuropixels
Brainwide Map
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Figure 5: Classification performance of intermediate layer representations. Plots present the classification performance at the outpur of all layers in our 6 layer model for single seed.
A.4 Comparison with POYO+ on Allen VC
POYO+ [5] is a multi-task behavior decoder model that can be trained simultaneously on multiple recordings. It has been shown to perform well Cre-line classification task the Allen Brain Observatory calcium imaging dataset, using the sexsion-averaged latent outputs of its encoder. This possible since each recording (session) in that dataset has neurons with only one cell-type. This analysis method used in the POYO+ manuscript is not directly applicable here since we aim to produce neuron-level embeddings that can identify different types of neurons within a given population.
However, POYO+ learns a "unit embedding" for each neuron, which can, in theory, be used for the purpose of decoding neuron-level properties. We use this strategy to compare POYO+ with NuCLR. We train POYO+ on the Allen VC dataset for decoding the following behaviors: Drifting Gratings Orientation, Drifting Gratings Temporal Frequency, Gabor Orientation, Gabor Position, Natural Scenes, Running Speed, and Static Gratings Orientation.
Since POYO+ can only be tested under transductive evaluations, we choose the transductive zero-shot regime for our comparison, and find that NuCLR outperforms the learned unit embeddings of POYO+ by strong margin (Table 5).
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Table 5: Comparing the unit embeddings POYO+ with NuCLR and other methods. Evaluation done in transductive zero-shot setting on the Allen VC dataset, with result being macro-Fi Fl scores represented as mean = std. dev. over seeds.
POYO+
NuCLR
NeuPRINT
NEMO
0.3521 1002
0.7218 2001
0.3999 a 0.0122
0.4256 1.0144
Pretraining POYO+ required an average of 7.5 hours on NVidia B200s to converge (about 100 epochs), while NuCLR requires only about 1.5 hours on the same hardware. We used the main training configuration from the example provided by the authors We also report the behavior decoding performance achieved by POYO+ after training across five random seeds:
Drifting Gratings Orientation Accuracy: 93.07% + 1.66 (Chance: 12.5%)
Drifting Gratings Temporal Frequency Accuracy: 93.40% + 1.36% (Chance: 20%)
Gabor Orientation Accuracy: 56. 12% + 1.18%
(Chance: 25.0%)
Gabor Position (2D) R²: 0.6888 0.487
Natural Scenes Accuracy: 53.23% 10.18%
(Chance: 0.84%)
Running Speed R²: 0.7681 = 00115
Static Gratings Orientation Accuracy: 75.99% - 0.97% (Chance: 12.5%)
A.5 Asymmetric temperature application
The original SimCLR [9] and DCL (53] losses contain only one temperature hyperparameter. How-
ever, we found that our method performs slightly better if we break the symmetry between within-view and across-viee view cosine similarity. This changes the loss expression in Equation
7 to be like
exp ((pt. p/n Tacmss)
C(F X
Σ
log
M
exp ((ph,, // Twithin)
+
Σ
exp ((ph.: pÊ) T Tacnoss
(n,m)EM
(o.K)EM
+ symmetric term for view 2 to 1,
(9)
where Twithin and Tacross are the separated temperature hyperparameters. We use Tacress 0.2 and Twithin = 1.0 for all our models, and present results from ablation study in Table Note that the results are only marginally better at the cost of introducing another hyperparameter, So, for most practical settings, suggest simply using the same temperature values for both cosine similarity terms.
Table 6: Ablation study for separate temperatures. Values are reported mean + std. dev. across 5 seeds.
Hyperparameter Variant
Allen VC
IBL.
T Tacross = 0.2, Twitiin = 1.0 (main model)
0.7200 +: 00367
0.5295 + 0.0040
Tacross 0.2, Twithin 0.2
0.7033 0.0223
0.5275 0.0059
A.6 Ablating temporal-only attention layers
To assess the importance of dedicated temporal-only attention layers, we replace them with an equivalent number of spatio-tempora layers. Specifically, the first two temporal-only layers in the original model were substituted with one spatio-temporal layer to maintain a similar model capacity,
As shown in Table 7 the inclusion of dedicated temporal-only layers appears to be relatively inconsequential design decision, as performance does not change significantly between the two model configurations. However, benefit of retaining the temporal-only layers is computational efficiency. Given that the number of temporal patches (~ 10) typically much smaller than the number of neurons (~ 100), a temporal layer has less computationally expensive than using separate temporal layers reduces the overall computational complexity, as attention mechanisms scale quadratically with the number of tokens in sequence.
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Table Ablation study for temporal-only layers. Values reported std. dev. seeds for NuCLR, and seeds the ablated model.
Model Variant
Allen
IBL
Full NuCLR
0.7200 0.2267
0,5295
0,0040
w/o temporal attention layers
0.7184
0.5259
A.7 Confusion matrices
We present the confusion matrices achieved by NuCLR in Figure for all datasets evaluated in the inductive zero-shot setting.
Allen Visual Coding
Bugeon et. al (E
Bugeon et. al. (subclass)
Pvalb Lamps
Prolb
52.0
50.0
22.2
18.3
72.1
26.3
20.9
12.5
86
2.3
73.7
37.4
Sncg
6.2
2.0
29.2
0.0
12.8
Set
27.9
73.7
9.7
25.1
54.3
Vip
20.3
3.7
8.3
14.8
38.9
Pvalo
9st
Lamp3
Pvelb
Snigg
Set
Vi
Brainwide Map
65.5
0.0
16.7
0.2
4.7
5.2
0.0
0.1
CNU
0.1
52.4
0.2
7.3
0.2
10.9
3.4
Steinmetz al.
75.7
0.0
5.2
15.1
0.0
144
1.6
3.4
0.1
17.2
0.9
3.2
4.3
19.7
4.3
59.4
6.3
8.3
3.0
9.5
4.7
30.7
87.6
HY
6.5
9.5
2.9
06.4
2.1
16.5
11.8
0.5
13.4
6.3
65.4
1.9
11.9
3.3
M6
15.4
72.3
2.5
0.0
9.8
33.7
0.0
1.9
3.5
8.2
0.0
48.4
0.5
T1
0.0
1.3
0.1
6.2
7.9
2.2
73.5
Isacortex
MB
TH
Figure 6: Confusion matrices achieved by NuCLR on inductive zero-shot evaluation.
A.8 Additional results on Bugeon et. al. dataset
evaluate representations NuCLR and baseline models on an 11-class label set of the Bugeon et. al. dataset. This label set consists of the classes: Lamp5-Chrma?, Lamp5-Npy, Lamp5-Tmem 182, Pvalb-Tac1 Pvalb- Vipr2, Sneg-Pdzr3' Sneg-Vip. Sst-Reln, Sst-Tacl, Vip-Cp, Vip-Reln. As seen in Table 8 NuCLR outperforms both NeuPRINT and LOLCAT basclines on this labelling of neurons.
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Table 8: Macro Fl-score for cell type classification the 11-class label set of Bugeon et. al. Reported mean std. dev. across 5 training seeds. N/A indicates the method cannot operate that evaluation setting.
Dataset
Classes
Setting
NuCLR
NeuPRINT
LOLCAT
Transductive
0.4056 0.0425
0,2748 #0013
O. 0.1559 + 00318
Bugeon et, al. (H-class)
11
Transductive zero-shot
0.2333 0.0129
N/A
Inductive zero-shot
0.1990 = 03191
N/A
0.1482 0,256
B
Additional method details
B.1 View-pair sampling
We sample view pairs during pretraining in three steps:
First view. The first view sampled using the RandomFixedd ixwoiriideddddddSSapllrerr found in torch_ barch_bbarr"" P
This sampler divides each recording into non-overlapping windows of length Tax and randomly samples one window (without replacement throughout an epoch). Across epochs, shared random jitter to the window start times at the beginning of every epoch. This emulates uniform sampling while ensuring complete data coverage each epoch.
2. Second view. The second view sampled relative to the first. Its start time drawn uniformly from range constrained by the AT setting, ensuring the two views come from nearby temporal contexts.
3. Neuron dropout. To increase robustness partial observations, apply neuron dropout independently to each view. Given a view with N neurons, we first sample the number of neurons drop from uniform distribution: Ndoop (O, (0,00.5.)) We then randomly select Ndrop neurons and exclude them from the view during that training step.
B.2 Model implementation details
Transformer. We standard scaled-dot- pre-normalization using LayerNorm. Our feedforward network (FFN) uses the GEGLU activation function, with its hidden dimension being 4x the dimension of the tokens (D). In the temporal transformer layers, we use Rotary Embeddings to incorporate timing information, as described below.
attention as implemented in xformers 25
Rotary Time Embeddings. We use rotary time embeddings following the formulation in Section A.1 of (3), which includes the use of value rotation in addition to query-key rotation. The only difference in our implementation lies in the choice of temporal scaling parameters. Because our input tokens are uniformly spaced with a stride of T patch. we set Tiin = T pacch and Tmax 8 x Tax-
B.3 Adapting to Calcium Imaging Data
To adapt NuCLR's spatio-temporal transformer calcium imaging data, we replace the binning-and patching step used for spike trains with a temporal patching operation applied directly to the calcium fluorescence time series (i.e. the AF/F signal). This modification affects only the input stage of the model: the architecture and training procedure remain unchanged. Unlike clectrophysiology, calcium imaging does not involve physical probes or insertions. Therefore, we treat all simultancously recorded neurons within a session as single population-bol for the spatial transformer layers and for contrastive loss computation.
B.4 Hyperparameters
We use the AdamW (28) optimizer with a linear learning rate warm-up over the first epoch, followed by cosine decay until end of training. All relevant hyperparameters for training NuCLR are listed in Table 9 These values were mainly selected via manual line searches on the IBL development set (for ephys data, Appendix D.2) and the Bugeon al. development set (for calcium imaging data, Appendix D.4). Across all datsets, we pretrain the model for 50,000 steps and use the of 1fat16
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number format throughout. Pretraining takes approximately 3 hours on machine with 4 x NVidia H100 GPUs.
Table 9; Key hyperparameters for NuCLR.
Parameter
Value for Ephys.
Value for Ca+ (dift. only)
Tax
10s
30s
Truch
ATmax
30s 20ms
240s
size
N/A
Lr
2
LST
2
D
256
Num. attention heads
Linear dropout
0.2
Attention dropout
0.0
Max. neuron dropout
50%
Num. training steps
50,000
Batch
128
16
Max learning rate
x 10
1.25 x 10
Weight decay
0.01 (default)
(default)
B2
0.999 (default)
C Embedding Visualizations
We present UMAP-based visualizations of NuCLR's output representations in Figure 7 For the Allen VC dataset, which includes a large number of subjects, the embeddings exhibit clear density modes that align most strongly with brain-region information.
In contrast, the Bugeon et al. and Steinmetz et al. datasets are relatively small, containing only and 10 subjects respectively, with limited total number of recordings. For these datasets, NuCLR's embeddings cluster primarily by subject or session identity. However, within these clusters, we still observe meaningful density modes corresponding cell-type and brain-region structure. Embeddings for the IBL. dataset, shown in Figure 2M, reveal strong region- based organization without noticeable clustering by subject or session.
The subject- and ression-specific clustering observed in smaller datasets (Bugeon et al. and Steinmetz al.) may hinder data-driven discovery, suggests entanglement with recording-specific factors. While this effect appears only in small datasets, mitigating remains an important direction for future work.
D Details on datasets and evaluation methodology
This section contains description of each dataset, and details our train-test splits for all evaluation settings. Additionally, our code-base includes all preprocessing, split preparation, and evaluation scripts.
D.I Allen Visual Coding
The Allen Visual Coding (VC) dataset consists of 58 Neuropixels recordings, each from a unique mouse. Each recording spans approximately two hours, during which various visual stimuli are presented. Of these recordings,
contain optotagged neurons IT with each labeled recording containing neurons from only one of the three inhibitory cell types: Pvalb, Vip, Sst. Since reliable cell-type labels are only available for neurons in the visual cortex, we restrict all models-including NuCLR and baselines to only neurons from the VIS region during pretraining.
httpp:: ///githlbs com/nerdssaabnnccr
We the same set of labeled neurons the original LOLCAT publication 38
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Visual Coding
Type
Region
Wllmam
Bugeon Subdlss
SubjectID
Steinmetz et. al. Subject
Brain Reg
Recordtig ID
Figure UMAP visualizations NuCLR's embeddings colored by various properties.
For zero-shot classifier evaluation, we follow leave-one-subject-out strategy. Specifically, there are 16 test folds corresponding the 16 labeled subjects. each fold, one subject is held out for testing, and the remaining 15 are used for training. Within the training set, we perform 4-fold cross-validation (subject-wise) to select the best training epoch. The final test on all 15 subjects using the selected epoch. We did not test classifier performance on this dataset in the transductive setting "perfect" classifier only has infer which of the previously seen sessions does the neuron belong to, and the cell-type corresponding to that session.
is reported from classifier trained
D.2 IBL Brainwide Map
The IBL Brainwide Map dataset consists of 439 recordings from 139 unique mice, with each recording performed using one two Neuropixels insertions. While performing zero-shot evaluations (both inductive and transductive), we construct train test splits at the recording level, ensuring that no co-recorded neuronal populations appear in both sets. In total, 93 recordings are designated for the test set, and the remaining 346 are used for training and validation. Of the training set, 91 recordings are used as a development set for tuning hyperparameters specific electrophysiology data. During classification, these same 91 recordings also serve as validation set for selecting the best training epoch. Final performance metrics are reported using model trained on 346 training recordings, evaluated on the held-out test set.
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For non-zero-shor evaluation, we create a neuron-wise stratified eest-train split with test size of 20%. The validation set for finding the best epoch is created from the train fold with size of 20%.
D.3 Steinmetz et al.
The Steinmetz al. dataset consists of 39 Neuropixels recordings from 10 unique mice, with each recording comprising 2 or 3 probe insertions. For non-zero-shot evaluation, we create train-test splits following the same procedure as described for the IBL dataset (Appendix D.2
(D2.)
For transductive zero-shot evaluation, we use a 10-fold leave-one-subjectoour strategy. In each fold, one subject is held out for testing, and the remaining nine are used for training. We select the best training epoch by performing a stratified 80/20 train-validation split within the training set. For inductive zero-shot evaluation, we hold out 3 subjects for testing and pretrain on the remaining 7. To select the best classifier epoch, we perform 4-fold subject-wise cross-validation within the training subjects.
D.4 Bugeon et al.
The Bugeon et al. dataset contains 17 spatial transcriptomic calcium imaging recordings from 4 unique mice. We train on stimulus-specine sub-recordings within these sessions. Since this the only optophysiology dataset in our evaluation, we adjusted several hyperparameters specifically for calcium activity. During development, we held out recordings- -ne from each subject- as validation set for tuning model hyperparameters. These recordings are never included in the test set when reporting final performance.
For non-zero-shot evaluation, we create neuron-wise train-test splits following the same protocol as in the IBL dataset (Appendix D.2). For transductive zero-shot evaluation, we adopt 4-fold leave-one-subjece
strategy. In each fold, we perform 3-fold subject-wise cross-validation within the training subjects to select the best epoch. For inductive zero-shot evaluation, we hold out one subject (SB028) for testing and pretrain on the remaining three. As in the transductive case, we perform 3-fold subject-wise cross-validation on the training set to select the best model epoch.
E
Implementation details for baseline models
E.1 NeaPRINT
Calcium Imaging (Bugeon et al.). For the Bugeon dataset, we used the publicly available Ne- uPRINT implementation and followed transductive evaluation setup consistent with the original codebase. The model was first pretrained on the full dataset. For evaluation, the embedding table was reinitialized and optimized again using the self-supervised loss, while keeping the backbone encoder frozen. The resulting neuron embeddings were then used for downstream classification.
Electrophysiology Datasets. For electrophysiology datasets, modified NeuPRINT's training loop and epoch structure to accommodate the larger scale and greater diversity of these datasets. In the original implementation, there is no true notion of ppoch-batchi are drawn from individual sessions without ensuring full dataset coverage, and the sampling strategy is manually biased toward neurons with labeled cell types. This setup does translate well to larger datasets such Steinmetz et. al., IBI., and Allen VC.
We instead adopted the standard definition of epoch, which is a full pass through all training data, and trained for 300 epochs. We also replaced the cession-speciffe sampling strategy in the original implementation with uniform sampling across all sessions to ensure unbiased data coverage.
Furthermore, because these electrophysiology datasets are substantially larger than Bugeon al. we did not re-initialize and releam the embedding table during evaluation. The original NeuPRINT protocol tailored for the Bugeon et al. dataset, where such re-initialization is computationally feasible. However, for large-scale datasets like Steinmetz, IBL, and Allen VC. this procedure becomes too computationally expensive. Therefore, we adopted more scalable and simpler protocol that just uses learned embeddings from pretraining for downstream evaluation.
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All hyperparameters were retained from the original NeuPRINT setup, except for the learning rate, which we to 1 x 10 2 and the backward context window, which was set 10.
NeuPRINT relies on behavior features being provided along with the mean and standard deviations for the population activity. The behavioral features used for electrophysiology dataset were follows:
Steinmetz et al.: face motion, pupil area, and wheel velocity
IBL: wheel velocity
Allen VC: running speed
E.2
NEMO
We evaluated NEMO using the official implementation provided by the original authors upon request. When adapting the method new datasets, we retained all hyperparameters from the original paper, with the exception of the waveform template dimensionality, which we adjusted to match the characteristics of each dataset.
E.3 LOLCAT
We evaluated LOLCAT using the official implementation provided by the original authors upon request. For all experiments (unless otherwise stated below), we used a batch size of 64, learning rate of x 10 -3 weight decay of x 10-5, and a dropout rate of 0.5. The MLP hidden dimensions [64, 32, 16], and attention heads were used. The snippet dropout was 0.45. We trained the models for 300 epochs with the optimizer outlined in the paper. These default parameters were chosen based on manual exploration the "reduced" hyperparameter search range and explicit prescriptions in the LOLCAT paper.
during training
Allen VC: We used the same final hyperparameter selection as the paper with new results on our split of the data.
IBL: We used the same hyperparameters Allen VC.
Steinmetz transductive: Batch size was increased to 1024, and the epochs were set to 1000.
Steinmetz inductive: The minimum factor was set to 0.01, and we initialized the classes undersampling factors to [8 (HPF), 8 (MB), 0.01 (TH), 0.01 (VIS)).
Bugeon subclass: The classes undersampling factors were initialized [0.01 (Lamp5), 0.01 (Pvalb), (Sneg), (Sst), (Vip)) and the minimum factor to 0.01.
Bugeon E vs. Class undersampling factors were initialized to [0.01 (E), (I)) and the minimum factor to 0.01.
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Reviews:
[{"review_id": "LlFr5Cnch8", "reviewer": "Reviewer_6mEF", "summary": "The 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.", "questions": "Temporal Window: Could a discussion on how to choose Tmax? It seems important but I would appreciate what the design considerations were.", "limitations": "Discussed adequately.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "My concerns about comparisons to POYO+ were the biggest share of my concerns. The authors also did requested ablations. I am satisfied with the final response.", "rebuttal": "Thank you for your time, thoughtful review, and suggestions. We appreciate your recognition of our scaling analysis to be \"an impressive finding\", and highlighting that our method leads to \"impressive improvement in neuron and brain region identification.\"  Based upon your suggestions, we have run two new ablations: a sweep of bin size and a comparison with a POYO-like spike timing tokenization. We provide these results and a point-by-point response below.\n\n**\\--**\n\n**\\[W1\\] 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.**\t\n\nThis is a good point as the precision of spike timing provided to the model could indeed be an important factor for overall performance. To study this effect, we performed an additional experiment where we swept the bin-size from 1ms to 40ms and measured model performance on the Allen VC and IBL datasets for the Inductive zero-shot evaluation setting. We provide the results in the table below, and notice that a bin size of 20ms provides the best performance for both datasets. \n\n*Table 1: Bin-size sweep. Results reported below are macro-F1 scores represented as mean ± standard deviation measured across N seeds.*\n\n|  | 1ms bins (N=3) | 5ms bins (N=3) | 10ms bins (N=3) | 20ms bins (N=5) | 40ms bins (N=3) |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| **Allen VC** | 0.6843 ± 0.0286 | 0.6883 ± 0.0260 | 0.7173 ± 0.0036 | **0.7200 ± 0.0267** | 0.7007 ± 0.0104 |\n| **IBL** | 0.5134 ± 0.0047 | 0.5231 ± 0.0118 | 0.5262 ± 0.0093 | **0.5295 ± 0.0040** | 0.5120 ± 0.0036 |\n\nAdditionally, we tested the spike-tokenization based encoder similar to that used in POYO \\[1\\]. In this modified architecture, instead of binning to create a token for a patch, we use a cross-attention transformer layer to encode POYO-like spike tokens within a patch into a single token. The rest of the temporal and spatio-temporal layers are kept the same.\n\n*Table 2: Spike-tokenization ablation*\n|  | Allen VC \\- Cell type decoding | IBL \\- Brain region decoding |\n| ----- | :---: | :---: |\n| **20ms binning (used in the paper)** | **0.7200 ± 0.0267** | **0.5295 ± 0.0040** |\n| **Spike-tokenization based encoder** | 0.6665 ± 0.0167 | 0.5268 ± 0.0136 |\n\nWe will add both, a plot for the bin-size sweep, and details and results for the spike-tokenization encoder ablation in the camera-ready version of our paper. Thank you for the suggestion\\!\n\n\\[1\\] Azabou, Mehdi, et al. \"A unified, scalable framework for neural population decoding.\" Advances in Neural Information Processing Systems 36 (2023): 44937-44956.\n\n**\\[W2\\] Additional baseline: I think a good comparison would also come from using POYO+. The encoder in POYO+ 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\nThe POYO+ architecture is aimed at behavior decoding, and, thus by design, it compresses the population activity into a smaller set of latent tokens to learn population-level dynamics, losing the identity of the individual neurons in the process. This is very different from the goal of the model architecture proposed by us in NuCLR, which is specifically designed to preserve neuron-level tokens throughout the model and output neuron-level embeddings.\n\nWhile it is true that POYO+ was able to perform cell-type classification, it was due to the fact that in calcium recordings, the entire recording is from a specific Cre line and thus reveals only cells from a specific cell class. Due to this dataset-level correspondence to cell type, it was possible to decode Cre line from the encoder latent outputs from each recording to classify the cell-type of neurons included in that recording (not unit embeddings). This is a special case that does not generalize to the electrophysiology datasets considered in our work, as well as the Bugeon et. al. spatial transcriptomics dataset which contains simultaneous recordings from multiple cell-types.\n\n**\\[Q1\\] Temporal Window: Could a discussion on how to choose Tmax? It seems important but I would appreciate what the design considerations were.**\n\nWe performed a manual sweep for all hyperparameters on the IBL dataset and measured performance on a validation subset of insertions (included in the attached code base). This process gave us the best value of $\\Delta T_\\max$ to be 30s. We used the same value for the other two electrophysiology datasets (Allen VC and Steinmetz et. al.) and, found that this choice seems to generalize well. For Bugeon et. al. dataset, which is recorded using calcium imaging and has a different timescale, we performed a similar sweep to reach the best $\\Delta T_\\max$ of 240s."}, {"review_id": "fp7F5J0bWG", "reviewer": "Reviewer_6uXR", "summary": "This 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.", "questions": "1.\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?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "My concerns are adequately addressed.", "rebuttal": "Thanks for your feedback and suggestions\\! Based upon your suggestions, we ran two new experiments: (i) we examined the role that the bin size has on model performance for different cell types, and (ii) studied the decodability of labels at intermediate layers in the model. We provide these results and a point-by-point response to your concerns below.\n\n**\\[Q1\\] Could you provide expanded technical descriptions for NeuPRINT, LOLCAT, and NEMO?**\n\nThank you for pointing out the missing technical descriptions. We will make sure to include detailed technical descriptions of all baseline methods in the appendix of the camera-ready version of our paper. We provide these details below:\n\n*NeuPRINT* is a self-supervised method that learns neuron-embeddings directly by performing causal masked-modelling on neural activity. The method consists of an encoder that takes as input (1) the past activity of a neuron, (2) population-wise mean and standard-deviation of all the neurons in the recording, (3) population mean and standard deviation of the M neurons closest to the target neuron, and (4) a *learnable* neuron-embedding corresponding to the target neuron. The encoder, along with the learnable neuron-embeddings are optimized using gradient descent at the task of predictive future activity over many neurons and populations. At the end training, these learnable neuron-embeddings are then considered the output of the method. These embeddings are used to train linear-classifiers and MLPs for downstream objectives of cell-type and brain-region decoding.\n\n*NEMO* is a CLIP-based self-supervised method. It trains two encoders. One encoder is a convolutional neural network (CNN) that embeds a hand-crafted feature of a neuron's activity \\- its 3D autocorrelogram. The second encoder is also a CNN that embeds the neuron's spike waveform/template. The CLIP objective trains the two encoders to produce similar embeddings. Finally, after training, a neuron's embedding is produced by concatenating the outputs of both the encoders. This concatenated embedding then can then be used for the downstream task of cell-type and brain-region classification.\n\n*LOLCAT* is a supervised method that aims to directly predict the cell-type of a neuron from its neural activity alone. The neural activity is first separated into trial-based snippets, and then converted to inter-spike-interval (ISI) distributions within each snippet. Each snippet's ISI distribution is first passed through an MLP encoder, then all snippets are aggregated using multi-head attention, and finally the aggregated latent is passed through an MLP classifier to output the cell-type prediction. This model is trained in a supervised manner.\n\n\n**\\[Q2\\] Could you analyze/discuss the influence of spike-bin/patch length on model performance, especially for different cell types?**\n\nThank you for this insightful suggestion\\! Based upon your suggestion, we performed an experiment where we varied the bin size and measured the class-wise F1 scores for the Allen VC dataset. Please refer to the results in the table below. We find 20ms bin size to be sufficient for distinguishing Pvalb and VIP neurons. Sst neurons (mostly regular spiking) are slightly more distinguishable at 10ms bin size, however by not much in comparison to a bin size of 20ms.\n\n*Table 1: Effect of bin-size on classification of different cell types. Reported F1 scores below are under the inductive zero-shot evaluation setting represented as mean ± standard deviation measured across 3 seeds.*\n\n|  | 1ms bins | 5ms bins | 10ms bins | 20ms bins | 40ms bins |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| **Pvalb** | 0.8212 ± 0.0387 | 0.8217 ± 0.0285 | 0.8507 ± 0.0191 | **0.8670 ± 0.0235** | 0.8656 ± 0.0159 |\n| **Sst** | 0.7204 ± 0.0121 | 0.7292 ± 0.0210 | **0.7546 ± 0.0201** | 0.7422 ± 0.0263 | 0.7174 ± 0.0295 |\n| **Vip** | 0.5113 ± 0.0409 | 0.5144 ± 0.0340 | 0.5466 ± 0.0120 | **0.5725 ± 0.0559** | 0.5191 ± 0.0052 |\n\nWe will include these results in the appendix of the camera-ready version of our paper. Additionally, we also measured the aggregate macro F1-score for both Allen VC and IBL datasets with different bin-sizes. Please refer to Table 1 in our response to reviewer 6mEF for the same.\n\n**\\[Q3\\] Could you examine the linear decodability of labels from intermediate layers of the trained transformer model?**\n\nWhile we train only the output of our model in a contrastive manner, we agree it would be interesting to assess how the neuron-level representations evolve across the network. We present the results of this analysis below, and thank the reviewer for this suggestion. It is very interesting to see how the quality of embeddings improves as we go deeper in the model. Specifically, we observe a step-function increase in the linear decodability when going from layer 2 to layer 3, i.e. when the first spatial transformer layer is encountered. We will add this result as a plot in the appendix of the camera-ready version of our paper, and we are sure it will be appreciated by many readers.\n\n*Table 2: Linear decoding results from intermediate layers of the model. Macro-F1 score is the reported metric.*\n\n| Layer number | Allen VC | IBL |\n| :---: | :---: | :---: |\n| 1 (Temporal) | 0.4734 | 0.2862 |\n| 2 (Temporal) | 0.4896 | 0.3360 |\n| 3 (Spatial) | 0.6254 | 0.4945 |\n| 4 (Temporal) | 0.6530 | 0.5147 |\n| 5 (Spatial) | 0.7488 | 0.4973 |\n| 6 (Temporal) | 0.7457 | 0.5197 |\n\nNote: We performed this analysis on one training seed here in the interest of time, however we will make sure to do this analysis on 4 more seeds before adding this result to the paper.\n\n**\\[W3\\] It is unclear whether there exist different stimulus conditions in these datasets; if so, whether the conditions are shared between training and test sets.**\n\nThe stimulus conditions varied minimally between datasets. Neural recordings in the Allen Visual Coding and Bugeon datasets were collected from mice passively viewing visual gratings and natural images while running on a treadmill. The Steinmetz and IBL datasets involved mice performing a decision-making task using left and right drifting gratings with varying contrast levels.\n\nEach dataset has its own train and test sets, and within each dataset, the train and test sets share the same stimulus conditions. We will include descriptions of these conditions for all four datasets in the camera-ready version of the paper."}, {"review_id": "CC5ojSJxy5", "reviewer": "Reviewer_4pyJ", "summary": "Authors 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.", "questions": "1.\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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "After reviewing the authors' comments, I'm willing to raise my score.", "rebuttal": "Thank you for your time and review\\! We provide point-by-point responses to your concerns below. \n\n**\\[W1\\] 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\nWe appreciate the opportunity to clarify our use of the term “real-time”. Our original claim may have been misleading, and we apologize for any confusion this caused. By “real-time,” we intended to convey that NuCLR supports zero-shot cell-type classification, enabling the possibility of performing cell-type inference in vivo without the need for retraining. However, we recognize that “real-time” typically often refers to low-latency deployment or online processing speed in machine learning, and our usage may have caused misunderstanding. To avoid confusion, we will remove this claim from the abstract and instead include a brief discussion of the model’s in vivo potential in the Discussion section.\n\n**\\[W2\\] 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\nWe thank the reviewer for highlighting this important point. Existing cell-type classification methods either do not support inductive zero-shot learning (e.g., NeuPRINT) or are specifically designed for electrophysiological data and do not generalize to calcium imaging (e.g., NEMO), which is why we have marked them as N/A in these settings. While direct empirical comparisons are therefore not feasible in this context, the ability of our model to both perform inductive zero-shot cell-type classification and generalize across recording modalities (electrophysiology and calcium imaging) represents a substantial advancement over current approaches. Regarding LOLCAT’s improved performance over NuCLR on the Bugeon et al. dataset for the excitatory vs. inhibitory (E vs. I) classification task, we found that LOLCAT, which is a supervised method, performs well when there is sufficient labeled data and coarse class distinctions. However, across all other settings, particularly those involving fine-grained labels or limited supervision, NuCLR consistently achieves substantially better performance.\n\n**\\[W3\\] While 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\nWe agree that inductive evaluation offers the strongest test of generalization. However, transductive evaluation remains a widely accepted practice in the neuron-type classification literature \\[1,2,3\\], and we include these settings for completeness and comparability. Both the “transductive” and “transductive zero-shot” setups correspond to realistic use cases where users have access to full neural recordings but only partial labels. In such scenarios, one can pretrain using the full dataset, including test recordings, while withholding all test labels. This setup does not involve any label leakage, and typically improves decodability of representations. That said, we agree this nuance deserves clearer discussion, and we will revise the discussion to more explicitly address the motivations and limitations of transductive evaluations.\n\n\\[1\\] Beau, Maxime, et al. \"A deep learning strategy to identify cell types across species from high-density extracellular recordings.\" Cell 188.8 (2025): 2218-2234.  \n\\[2\\] Ye, Zhiwen, et al. \"Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings.\" bioRxiv (2024): 2023-08.  \n\\[3\\] Mi, Lu, et al. \"Learning time-invariant representations for individual neurons from population dynamics.\" *Advances in Neural Information Processing Systems* 36 (2023): 46007-46026.\n\n**\\[W4\\] It would be better to see more detailed descriptions and appropriate citations for key components like the temporal transformer layers and spatio-temporal blocks.**\n\nHere we provide a detailed description of the components of our model. All transformer layers in NuCLR use multi-head scaled-dot-product attention from \\[1\\], with the feedforward network (FFN) being a GEGLU network \\[2\\]. LayerNorm is used for normalization *before* tokens are sent into the attention and FFN layers. For spatial attention layers, no position embeddings are used. And for temporal attention, we use Rotary position embeddings \\[3\\] to incorporate relative timing of the patch-tokens. We will make sure to add these details to our paper. Please let us know if you would like us to include any more details\\!\n\nWe acknowledge the lack of citations for these components. We will also add a citation for PatchTST \\[4\\] for the temporal transformer layer, and STNDT \\[5\\] and Vivit \\[6\\] for the spatio-temporal transformer layer.\n\n\\[1\\] Vaswani, Ashish, et al. \"Attention is all you need.\" *Advances in neural information processing systems* 30 (2017).  \n\\[2\\] Shazeer, Noam. \"GLU variants improve transformer.\" arXiv preprint arXiv:2002.05202 (2020).  \n\\[3\\] Su, Jianlin, et al. \"Roformer: Enhanced transformer with rotary position embedding.\" Neurocomputing 568 (2024): 127063\\.  \n\\[4\\] Nie, Yuqi, et. al. \"A time series is worth 64 words: Long-term forecasting with transformers\" *The 11th International Conference o n Learning Representations (ICLR)*, 2023\\.  \n\\[5\\] Le, Trung, et. al. \"STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers\" *Advances in Neural Information Processing Systems 35* (2022).  \n\\[6\\] Arnab, Anurag, et al. \"Vivit: A video vision transformer.\" *Proceedings of the IEEE/CVF international conference on computer vision*. 2021\\.\n\n**\\[W4\\] In 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\nThank you for the feedback. Based on your comments and those of other reviewers, we agree that our discussion of prior work needs to be strengthened. To clarify, CEED relies solely on extracellular waveforms for classification and does not incorporate spike timing information. In contrast, NEMO builds on CEED by introducing contrastive learning to jointly embed both spiking activity and waveforms. This distinction is why we compare against NEMO, rather than CEED, in our manuscript. We will revise the prior work section to clarify these distinctions.\n\n**\\[W5\\] While 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\nWe respectfully disagree with this assessment. Across multiple open-access datasets, NuCLR **introduces a novel architecture** for the task of cell type and brain region classification, and **consistently outperforms prior methods** on both tasks. Moreover, its ability to **generalize in a zero-shot setting to new populations of neurons** represents a promising step toward making these tools more practical and broadly usable for neuroscientists. To our knowledge, this is also the **first work to demonstrate that cell-type classification performance improves with the addition of unlabeled data**, and that pretraining on unlabeled sessions yields greater downstream gains than simply doubling the number of labeled examples. Together, these empirical findings and methodological innovations address core machine learning challenges, such as generalization under distribution shift, learning from limited labels, and leveraging unlabeled data for improved transfer, which are highly relevant to the NeurIPS community.\n\nIn addition, recent publications such as NeuPRINT (NeurIPS 2023\\) and NEMO (ICLR 2024\\) also focus entirely on the problem of cell-type and brain-region classification from neural recordings. The presence of these works at top machine-learning venues suggests that the topic is of active interest to the NeurIPS audience.\n\n**\\[Q2\\] Please provide more detail about model architecture.**\n\nWe refer to our response to weakness 4 (\\[W4\\]\\) above.\n\n**\\[Q3\\] Is there a reason why use Temporal attention followed be spatial temporal attention layer rather than one spatial-temporal layer?**\n\nWe saw, in early experiments, that including temporal-only attention layers before the spatio-temporal layer slightly improved the performance. Based on your suggestion, we conducted an ablation study where we replace the temporal layers with a spatio-temporal layer and present the results below. While there is a small improvement with our current architecture, we agree that it is reasonable to replace the temporal attention \\+ spatial attention with a spatiotemporal attention layer without losing much performance. Thanks for the valuable insight\\! This is indeed an important ablation and we will include these results in Table 3 of our updated paper.\n\n*Table 1: Ablation for temporal layers in the model in the inductive zero-shot evaluation setup.*\n\n|  | Allen VC | IBL |\n| ----- | :---: | :---: |\n| **Presented architecture** | **0.7200 ± 0.0267** | **0.5295 ± 0.0040** |\n| **Ablation \\- Spatio-temporal layers only** | 0.7184 ± 0.0124 | 0.5259 ± 0.0089 |"}, {"review_id": "pOwjFyysML", "reviewer": "Reviewer_KHtW", "summary": "The 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).", "questions": "Major:\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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "The authors have addressed my concerns and provided additional experiments during the rebuttal period, hence, I updated the score towards more positive", "rebuttal": "Thank you for your time and thoughtful review\\! We appreciate that you highlight that the “method is carefully designed” and “clearly explained”. We provide point-by-point responses to your comments below.\n\n**\\[Q1\\] 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\nThanks for your question and the chance to clarify this important point. The POYO+ architecture is aimed at behavior decoding, and, thus by design, it compresses the population activity into a smaller set of latent tokens to learn population-level dynamics, losing the identity of the individual neurons in the process. This is very different from the goal and architecture of NuCLR, which is specifically designed to preserve neuron-level tokens throughout the model and output neuron-level embeddings.\n\nWhile it is true that POYO+ was able to perform cell-type classification, it was due to the fact that in the calcium recordings that were studied, the entire recording is from a specific Cre line and thus reveals only cells from a specific cell class. Due to this dataset-level correspondence to cell type, it was possible to decode Cre line from the encoder latent outputs from each recording to classify the cell-type of neurons included in that recording (not unit embeddings). This is a special case that does not generalize to the electrophysiology datasets considered in our work, as well as the Bugeon et. al. spatial transcriptomics dataset which contains simultaneous recordings from multiple cell-types. We will make this point more clear in related work in the revised paper.\n\n**\\[Q2\\] Could you train your model on IBL repeated site dataset and make a comparison with NEDs \\[3\\] (Fig 4)?**\n\nFollowing your suggestion, we have trained NuCLR on the IBL repeated sites dataset and report the performance observed in the table below. The results are for classifying the following regions: PO, LP, DG, CA1, VISa. These performances are for the transductive evaluation criteria (similar to the evaluation of NEDS) and standard deviations are measured over 3 seeds.\n\n|  | Accuracy (%, unbalanced) | Macro F1-score |\n| :---: | :---: | :---: |\n| **IBL Repeated Sites** | 87.01% ± 0.61% | 0.8449 ± 0.0032 |\n\nNEDS reports an unbalanced accuracy of 83.0% on this task on a single split. While the results are not directly comparable because we couldn’t gain access to the specific splits reported in NEDS, our results suggest that NuCLR outperforms NEDS, even though NuCLR relies on learning only from the spike trains, while NEDS uses behavior to train the model. \n\nWe will make sure to include this result in the appendix of the camera-ready version of our paper, and will include the splits in the released code-base so that comparisons could be made on this dataset by future works.\n\n**\\[Q3\\] 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\nThanks for your suggestion. We want to clarify that there is a single linear layer used to read out the cell type or brain region information, and thus it measures how well the pretrained model has captured relationships across cells that is biologically meaningful and interpretable. \n\nWe agree that clustering is an interesting line for future work \\- and imagine that through a harder constraint on collapse or a small number of labeled cell types to use as supervised labels for contrastive sampling, that our approach could be adapted to this setting.\n\n**\\[Q4\\] 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\nYes, in the ablation for the spatial-temporal attention layers, we replace those layers with an equal number of temporal layers. In other words, the depth and parameter count of the model is the same in both cases, making it a fair comparison. We will update the text near lines 259-260 to indicate this more clearly.\n\n**\\[Q5\\] 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\nWe apologize for the confusion here. We would revise that line in text as:  \n\\> Unlike NeuPrint and NeurPIR, which are limited to *simple low-dimensional* statistics or session-tuned encoders, our approach supports dynamic, context-aware, and transfer-capable inference of neuronal identity.\n\nWe meant to say that NeuPRINT relies on simple and low-dimensional statistics of the population (across-neuron mean and standard deviation of the population activity). The \"session-tuned encoders\" phrase was meant only for NeurPIR. Specifically, both NeuPRINT and NeurPIR incorporate population activity using  (1) population-wise mean and standard-deviation of all the neurons in the recording, and (2) population mean and standard deviation of the M neurons closest to the target neuron. This low-dimensional nature of the population activity that is incorporated in these methods is what we are trying to indicate here.\n\n**\\[Q6\\] Could you provide confusion matrixes for the datasets and prediction done?**\n\nThank you for pointing this out\\! Unfortunately we cannot include confusion matrices in our rebuttal as NeurIPS does not allow posting images. However, we do have the confusion matrices for all datasets and will make sure to include them in the camera-ready version of our paper.\n\n**\\[Q7\\] How would NuCLR perform if we go one level down the Bugeon hierarchy? (eg 11 classes on the type level)**\n\nThanks for the suggestion. Unfortunately, due to time and space we decided to only report on 5 classes as this was the baseline provided in NeuPrint. We leave this fine-grained analysis for future work.\n\n**\\[Q9\\] In table 3 are the standard deviation also reported across seeds?**\n\nYes, the standard deviations reported in Table 3 are also across 5 training seeds.\n\n**\\[Q10\\] What exactly is \"multiple seeds\" for the standard deviations in the tables? Two? Three? More?**\n\nThank you for bringing this up\\! We apologize for our oversight here. For the results in Tables 1 and 2, we trained NuCLR, NEMO, and LOLCAT on 5 seeds, and NeuPRINT on 3 seeds (due to time limitations and longer training times for this method). Similarly, the ablation experiments in Table 3 were also done on 5 seeds. We will ensure that the final paper has 5 seeds for all models and will make sure to clarify the number of seeds in the main text of the camera-ready version of our paper.\n\n**\\[W5\\] 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\nThanks for pointing out this work\\! You are correct that STNDT accounted for cross-neuronal interactions in their architecture. We will revise our statement to read “designed and trained for learning neuron-level properties like cell type and brain region”, as STNDT focused on forecasting and was not tested for cell type decoding or other neuron-level decoding tasks. We will include a citation to STNDT in the related work and discuss its connection to our approach in the revised manuscript.\n\n**\\[W6\\] 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\nThanks for pointing us to these great references\\! In our related work, we primarily focused on methods that directly performed cell-type classification (NEMO, NeuPRINT, LOLCAT, etc.). However, you are right that several other approaches include cell-type classification analyses, even if that was not their main focus (POYO+, NEDS, Wang et al., etc.). We missed these works and will add a section in the related work to discuss them. Overall, we definitely agree that our literature review needs to be revised, and we plan to incorporate the papers you mentioned."}]

Author rebuttal:

Meta-review: This paper introduces NuCLR, a novel self-supervised learning framework for functional cell-typing and neuron localization. The approach, which incorporates fine-grained population dynamics through a spatio-temporal transformer and contrastive learning, is well-motivated and addresses a clear unmet need in neuroscience. The reviewers agree that the paper's core findings are impressive and well-supported. The scaling analysis shows that NuCLR improves with more unlabeled data, a critical property for practical use. The ablations and extra experiments during the rebuttal were convincing.
Please follow through on your commitment to update the manuscript by incorporating valuable references and a revised related works section was also a significant factor in the recommendation.
Decision: Accept (poster)