Generalized and Invariant Single-Neuron In-Vivo Activity Representation Learning
Authors: Wei Wu
Yuxing Lu
Zhengrui Guo
Chi Zhang
Can Liao
Yifan Bu
Fangxu Zhou
Jinzhuo Wang
Venue: NeurIPS 2025 poster ()
Keywords: neuroscience
neural activity
single neuron
in vivo
neurobiology
self-supervised learning
domain adaptation
Primary area: neuroscience_and_cognitive_science

Abstract: In computational neuroscience, models representing single-neuron in-vivo activity have become essential for understanding the functional identities of individual neurons. These models, such as implicit representation methods based on Transformer architectures, contrastive learning frameworks, and variational autoencoders, aim to capture the invariant and intrinsic computational features of single neurons. The learned single-neuron computational role representations should remain invariant across changing environment and are affected by their molecular expression and location. Thus, the representations allow for in vivo prediction of the molecular cell types and anatomical locations of single neurons, facilitating advanced closed-loop experimental designs. However, current models face the problem of limited generalizability. This is due to batch effects caused by differences in experimental design, animal subjects, and recording platforms. These confounding factors often lead to overfitting, reducing the robustness and practical utility of the models across various experimental scenarios. Previous studies have not rigorously evaluated how well the models generalize to new animals or stimulus conditions, creating a significant gap in the field. To solve this issue, we present a comprehensive experimental protocol that explicitly evaluates model performance on unseen animals and stimulus types. Additionally, we propose a model-agnostic adversarial training strategy. In this strategy, a discriminator network is used to eliminate batch-related information from the learned representations. The adversarial framework forces the representation model to focus on the intrinsic properties of neurons, thereby enhancing generalizability. Our approach is compatible with all major single-neuron representation models and significantly improves model robustness. This work emphasizes the importance of generalization in single-neuron representation models and offers an effective solution, paving the way for the practical application of computational models in vivo. It also shows potential for building unified atlases based on single-neuron in vivo activity.

Generalized and Invariant Single-Neuron Vivo Activity Representation Learning
Wei Wu', Yuxing Lu', Zhengrui Guo', Chi Zhang', Can Liao², Yifan Bu', Fangxu Zhou't !, Jinzhuo Wang
Peking University. Beijing, China Hong Kong University of Science and Technology, Hong Kong, China University of Georgia, Athens, USA
$ Corresponding author: wangjinzhuo@plee edu. en, zhouf www.uuannxuuuuuu..mmm edu.eppppuuuuuueeee edu.cm
Abstract
In neuroscience, models that learn representations single-neuron in-vivo activity are essential for understanding the functional identities of individual neurons. The primary goal of these models spannning Transformer-basee contrastive, and varia- tional autoencoder frameworks, is not to predict neural activity, but to distill it into a stable, low-dimensional embedding that captures a neuron's intrinsic features. These learned identity embeddings should be invariant to changing experimental conditions while reflecting the neuron's 's molecular type and anatomical location, thus enabling downstream tasks like in-vivo cell type prediction. However, current models suffer from limited generalizability due to batch effects: non-biological vari- ations arising from differences in experimental design, animal subjects, or recording platforms. These batch effects cause overfitting, reducing model robustness and utility. Crucially, previous work has not rigorously evaluated model performance on unseen, or "out-of-de -oomain,,, animals and stimuli, creating a significant gap in the field. To solve this, first introduce comprehensive benchmark protocol that explicitly evaluates generalization to unseen batches. Second, we propose a model-ag adversarial training strategy where discriminator network forces the primary model to learn embeddings that are invariant to batch information. Our approach is compatible with all major single-neuron representation models and significantly improves their robustness. This work highlights the critical need for generalization in such models and offers effective solution, paving the way for the creation of unified neural atlases from in-vivo activity.
Introduction
In the field of neuroscience, the exploration of the intricate mechanisms underlying neural activity has been an enduring endeavor. A key challenge is to understand the stable, functional identities of individual neurons from their dynamic activity patterns. To this end, single-neuron representation models have emerged as a crucial tool [ The central goal of these models is not to predict moment-te tonttttto-oonn to-moment neural activity, but rather distill the complex activity of neuron into stable, low-dimensional embedding that represents its intrinsic functional identity. This learned identity should be independent of transient factors like the specific stimuli an animal receives, and is instead related to fundamental properties such as the neuron's 's molecular type, anatomical position, and connectivity status.
There are three main data-driven paradigms for learning these identity embeddings. The first encom- passes models featuring implicit representations, typically built on Transformer architectures, which elegantly define the representation of each neuron as learnable identity vector, as elaborated [5
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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The second paradigm centers on contrastive learning, which learns a robust embedding by maximizing the similarity of representations derived from different modalities of a single neuron's activity (e.g.. its waveform and firing patterns) 7 'hee third paradigm relies on variational autoencoders (VAEs), which learn a compressed latent embedding by training on an activity reconstruction task 8
The primary output of these models is a learned feature vector-the identity embedding that encapsulates the neuron's time-invariant and intrinsic properties. The utility of this embedding is then validated by its performance on downstream tasks, such as facilitating the prediction of molecular cell types and anatomical locations for single neuron. These predictive capabilities are critical for enabling advanced, long-term closed-loop experiments, for instance, by allowing for targeted perturbations of specific, annctionally-identiin neurons [T].
However, existing single-neuron representation models face significant challenges in terms of gen- eralizability. Batch effects (non-biological variations arising from different experimental designs or animal subject) can contaminate the learned embeddings (Figure Ta). These batch effects cause models to overfit to specific experimental conditions, severely compromising their ability to general- ize. For instance, an embedding model trained on data from one animal may f'il on another, even for the same cell type in the same brain region. Previous studies have often overlooked this issue, failing to rigorously evaluate model generalization on new animals and stimuli cout-of-domaing This overlooks the critical aspect of model robustness, which is essential for the practical application of these models in broader neuroscience research.
To address these issues, this paper introduces comprehensive benchmark and robust training strategy. In the following sections, we will present a series of experiments, such as cell-type prediction across different stimuli across different animals. For each experiment, we will first detail the specific setup and protocol, and then immediately present the results, providing a clear and self- contained evaluation of model generalization. This structure is designed rigorously assess model performance on previously unseen data, filling a key gap in prior research.
Moreover, we propose a madel-agm strategy (Figure[]b) to mitigate the influence of batch effects. Adversarial training has proven effective in various machine learning domains for removing unwanted variations [2]. In our context, an adversarial discriminator attempts to predict batch information from the learned neuron embeddings. In response, the primary model is trained to produce embeddings that are invariant to the batch identity meaning the representations are statistically indistinguishable regardless of which animal or stimulus condition they came from. This forces the model to focus on intrinsic biological features rather than experimental artifacts. Our framework is designed to be compatible with all three major representation learning paradigms, enhancing their generalizability.
In summary, this study underscores the significance of generalization in single-neuron representation models. By leveraging this training, we aim to enhance the robustness of the learned identity embeddings, mitigating batch effects. This approach not only advances the development of single- neuron representation models but also paves the way for their broader practical applications in neuroscience research.
2 Related Work
The pursuit of implicit single neuron representations has been driven by the recognition that the in vivo physiology of a neuron comprises two distinct elements: the neuron's 's inherent properties and the synaptic and modulatory inputs receives. To disentangle these components, recent studies have proposed self-supervised approaches for inferring identity vectors for neurons. These methods rely on models of neuronal dynamics with exogenous inputs, leveraging the temporal structure of neural activity to extract neuron-specifi features. For instance, by formulating a model of activity dynamics that depends solcly on a neuron's 's past activity and population-level statistics invariant to individual ordering. researchers have been able to infer neuronal identities from population recordings. This approach capitalizes on the assumption that the underlying dynamics of each neuron, despite being influenced by external inputs, possess unique signatures that can be captured through self-supervision. These implicit representations offer powerful way to summarize neuron's functional characteristics without explicitly defining its features, but they may still be affected by factors such as batch-specific variations in experimental conditions.
NeuPRINT-Implict Single Neuron Representations [5:
NEMO-Multinodal Contrastive Learning 77:
Multimodal contrastive learning has emerged as promising strategy for single neuron representation learning. By jointly embedding different
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modalities of single- nunle-neuron data, such as activity autocorrelations and waveforms, these methods aim to capture the shared information across modalities while discarding sppeeit noise. The underlying assumption is that neurons with similar functional roles will exhibit similar patterns across multiple modalities, and by maximizing the similarity of their representations in a shared latent space, a more robust and discriminative representation can be obtained. For example, recent studies have utilized advanced contrastive learning frameworks to map these diverse modalities into a common embedding space, effectively enhancing the representational power of single-neuron models. This approach not only leverages the complementary information from multiple sources, but also helps in reducing the impact of noise within individual modalities. However, like other methods, it remains vulnerable to batch effects, which can introduce confounding variations that distort the learned representations and limit their generalizability.
VAE-Based Method [0]: VA have also been widely employed for unsupervised pre-training
in single-neuron representation learning. VAFs compress high-dimensional input data, such as neuronal waveforms and activity autocorrelations, into oower-dimensionll latent spaces. Through the optimization of reconstruction loss and KL-divergence, VAEs learn to represent the essential features of single neuron data while discarding redundant information. These learned latent representations provide compact summary of single neuron activity patterns, which can be further utilized for tasks such as cell type classification and brain region prediction.
End2End Method: End-to-end supervised training methods are the common approaches for pre- dicting cell types/brain regions directly based on individual neuronal activities. We have adopted the well-established LOLCAT as the end-to-end basemodel 13]. The end-to-end method attempts to directly extract and map molecular type labels or anatomical location information from neuronal activities. However, it cannot comprehensively represent the functional identity information of neurons in advance like the self-supervised method. The prediction of labels is merely a verification and partial application of the learned functional identity representation.
Adversarial strategies for eliminating batch effects have been used in the field of single-cell omics before [1]. To our knowledge, we are the first to treat in vivo activity an omics and apply adversarial strategies, but the goal of this paper is demonstrate the effectiveness of adversarial strategies. In-depth comparison and selection of adversarial methods is promising future work.
3 Method
We introduce model-agnostic adversarial training framework to mitigate batch effects in single- neuron representations, thereby improving generalization across experimental conditions and animal subjects. Our approach is compatible with mainstream single-neuron representation paradigms, including implicit Transformer-basee models, contrastive learning, and VAE).
3.1 Problem Formulation
Let X (x be raw neuronal observations, each associated with intrinsic label y y (e.g., cell type, region) and a batch label b, E B (c.g., animal ID, stimulus, session). The objective to learn an encoder fa : X + Rd that maps x; to an embedding z; fo (x;), such that z; preserves yi-relevant features while being invariant to batch effects. Standard models often overfit to batch-specific information, resulting in poor generalization to unseen batches.
3.2 Adversarial Training Framework
Our framework consists of two modules: (1) representation encoder fe that extracts embeddings z,, and (2) batch discriminator D& that predicts batch labels from z, Training is adversarial: D& is optimized to identify batch information, while fe is trained to remove it, encouraging batch-invarian representations.
3.3 Objective Functions
The total loss combines a task-specific base loss and an adversarial loss:
Base Loss Liwe: Defined by the specific single-neuron representation paradigms, e.g.. cross-entropy for classification (LOLLCAT), reconstruction loss for VAE, or contrastive loss for self-supervised learning (NEMO).
Adversarial Loss Lratch: Cross-entropy loss for batch prediction. D& minimizes this loss, while fe maximizes it to confuse the discriminator.
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TralinVVaiiation Dataset
Test Dalaset
Dritiig
Statk
Natural
Natural Movies
Noiso
Single Neuron Representation Model
Downstream Task
Out of Distribution
Single Neuron Representation Model
Discriminator
Adversarial Training
Figure 1: Schematic overview of the experimental protocol and adversarial training framework for evaluating and improving single-neuron representation models. (a) Illustration the experimental protocol designed to rigorously assess the generalization ability of single-neuron representation models. The training and validation dataset comprises diverse visual stimuli, including drifting gratings, static gratings, natural scenes, and natural movies, which are used to train the single-ncuron representation model. The learned representations are subsequently evaluated on a downstream task. To assess oittr---iistibuttion generalization, a distinct test dataset consisting of locally sparse noise stimuli is employed, which is not present the training/validation set. This protocol enables explicit evaluation of model performance novel stimulus conditions, providing stringent test of generalizability. (b) Schematic the model aonell-ggnoticc adversarial training framework. Detail in Method Section and Figure 2.
The joint objective is:
min max
(oo) - A (Do(fo)),
where controls the adversarial regularization strength. We employ a Gradient Reversal Layer (GRL) [12 to enable efficient end-to-end optimization.
3.4
Model-Agnostie Integration
Our framework is aechtecture-agnostie fo can be any single-neuron activity encoder (e.g., implicit transformer, contrastive based MIP,, autoencoder), and Do is two hidden layer MLP operating on z, Hyperparameters are selected via cross-validation.
3.5 Intuitive Explanation
Our adversarial approach is intuitively illustrated by figure 2 Without adversarial training, model can be confounded by batch effects (colors), learning decision boundary that is not based on true biological features (shapes) and thus fails to generalize to unseen test data (left panel). Adversarial training corrects this by forcing the model to create batch-invariant embeddings that are indistinguishable to a discriminator network. As a result, the model is compelled to learn a more robust and generalizable decision boundary based only on the intrinsie features of the cell types, leading to correct classification on novel data (right panel). This process aligns with the principle of invariant risk minimization, ensuring the model relies on biologically meaningful signals rather than experimental artifacts.

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Adversarial Training
Call Type
TraineVal
Test
Batch
Batch 3
Model hyperplane for chassilying cell types
Type
Batch
Figure 2: Illustration of adversarial training for improving the generalizability of single-neuron representation models, Each shape represents neuron from specific cell type, and colors indicate different experimental batches. Left: Without adversarial training, the model hyperplane (dashed line) for cell type classification is confounded by batch effects, Ieading to poor generalization on unseen batches. Right: Adversarial training encourages the model to learn batch-invariant representations, resulting a more robust and generalizable decision boundary across batches. This approach enables reliable cell type classification even in novel experimental conditions.
Benchmark Experiment Setup
We focus on evaluating the impact of adversarial training on the performance of the original model. Detail implementation can be found in appendix LOLCAT, NeuPRINT, VAE. Key implemen- tation details (optimizer, schedule, discriminator capacity) sit in Appendix A.5.
4.1 Cell Type Prediction Across Visual Stimulus
For this task, use the VI-CellType dataset.
V1-CellType [[]
This dataset is from multimodal recordings of the mouse primary visual cortex (V1). 2-photon calcium imaging with a 4.3Hz temporal sampling frequency was used to obtain population activity recordings, and the spatial coordinates of recorded neurons were provided. Functional recordings were obtained from four mice, namely SB025, SB026, SB028, and SB030. Recordings were perfomed under sets visual stimuli. In total, these recordings encompass 9728 neurons. Each session endured approximately 20 minutes and documented around 500 neurons. Subsequently, single cell spatial transcriptomics was conducted on the tissue. The mRNA expression of 72 selected genes in ex-vivo tissue profiled to classify neurons excitatory or inhibitory. Neurons were further subclassified into Lamp5, Pvalb, Vip, and Sst subtypes.
We conduct experiments on this dataset to evaluate cell type prediction across the four distinct visual stimulus conditions: spontaneous activity, drifting gratings, and two different natural scene image sets. Previous studies have typically either pooled data from all stimulus conditions and performed random splits for training and validation, or focused on single stimulus type. In contrast, our experimental protocol is designed to rigorously assess the generalization ability of models to novel stimulus conditions. Specifically, we train and validate both the representation model and the cell type classifier using neurons recorded under three of the four stimulus conditions, and then evaluate the model on neurons recorded under the remaining, held-out stimulus condition. This out-of-domain evaluation protocol ensures that the model is tested on data distributions not encountered during training, providing a stringent assessment of its robustness and generalization to unseen stimuli.
We systematically compare model performance under three experimental settings: (i) without adver- sarial training and without splitting by stimulus condition (i.e., neurons from all stimulus conditions are mixed and randomly split). (ii) without adversarial training but with splitting by stimulus condition (cross-stimulus evaluation), and (iii) with adversarial training under the cross-stimulus evaluation protocol. This comprehensive comparison allows us to quantify the impact of adversarial training on model robustness and generalization across different stimulus conditions. As with the cross-animal experiments, we do not evaluate the effect of adversarial training the NEMO approach for this
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dataset due to the complexity of preprocessing the V1-CellTyyp dataset into the multimodal input format required by the NEMO model.
4.2 Cell Type Prediction Across Animals
We continue to conduct experiments on the VI-CellType dataset to assess cell type prediction across animals. Previous studies have either performed experiments on individual animals or mixed neurons from all animals together before randomly splitiing them into training and validation sets. As described above, the VI- VI-CellType dataset contains recordings from four mice. We train and validate both the representation model and the cell type classifier using neurons from three mice, and then evaluate the model on neurons from the remaining mouse, which is held out entirely from model optimization. This out-of-domain testing protocol allows us to rigorously assess the generalization ability of the learned representations and classifiers unseen animals.
We systematically compare the model performance under three conditions: (i) without adversarial training and without splitting by animal ID (i.e., neurons from all animals are mixed and randomly split), (ii) without adversarial training but with splitting by animal ID (cross-animal evaluation), and (iii) with adversarial training under the arimsl evaluation protocol. This enables us to quantify the effect of adversarial training on model robustness and generalization across animals. Due the challenges in preprocessing the V1-Cell'Type dataset into the multimodal input format required by the NEMO model, we do not evaluate the effect of adversarial training on the NEMO approach for this dataset.
4.3 Anatomical Brain Region Prediction Across Animals
To further evaluate the adversarial training, we conduct experiments on the IBL Brain-wide Map dataset, focusing on anatomical brain region prediction across animals.
IBL Brain-wide Map [6]] This dataset is a comprehensive set of recordings from 115 mice performing a decision makinn task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. Following recordings, probe tracks were reconstructed using serial-section 2-photon microscopy, and each recording site and neuron was assigned a region in the Allen Common Coordinate Framework. Annotations divided into 10 broad areas by Cosmos hierarchical grouping: isocortex, olfactory areas (OLF), cortical subplate(CTX,, cerebral nuclei (CNU), thalamus (TH), hypothalamus (HY), midbrain (MB), hindbrain (HB), cerebellum (CB) and hippocampal formation (HPF).
In previous studies using this data, models have often been trained and validated data pooled from multiple animals, with random splits that do not account for inter-animal variability. In contrast, our experimental protocol designed rigorously assess the ability of models to generalize anatomical region predictions to entirely unseen animals. Specifically, we train and validate both the representation model and the anatomical region classifier using neurons recorded from a subset (0.8) of animals, and then evaluate the model on neurons from a held-out animal that is excluded from all stages of model optimization. This oft-o--dommin evaluation protocol ensures that the model is tested on data distributions that reflect biological variability across individual animals, providing a stringent assessment of its robustness and generalization.
We systematically compare model performance under three experimental settings: (i) without adver- sarial training and without splitting by animal subjects (i.e., neurons from all animals are mixed and randomly split), (ii) without adversarial training but with splitting by animal subjects (cross-animal evaluation), and (iii) with adversarial training under the cross-animal evaluation protocol. This com- prehensive comparison enables us to quantify the impact of adversarial training on model robustness and generalization across animals in the context of anatomical brain region prediction. We do not evaluate the effect of adversarial training on the NeuPRINT approach for the IBL Brain-wide Map dataset due to the complexity of preprocessing required for NeuPRINT input formats.
5
Result
Cell Type Prediction Across Visual Stimulus (V1-CellType)
Cell Type Prediction Across Visual Stimulus (V1-CelIType) In Table 1 we evaluate the perfor- mance of different models on cell type prediction under various visual stimulus conditions. The results reveal distinct patterns of model behavior across the three experimental settings. The NeuPRINT

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Table 1: Cell Type Prediction Across Visual Stimulus (VI-CelIType). Each cell shows the top-1 accuracy. The "Out-of-domain " columns show the performance on the generalization task and, in parentheses, the percentage decrease relative to the "In-domain" result.
In-domain (Random Split)
Out-of-domain
Out-of-domain (+Adversarial)
Model
(Cross-Stimulus
LOLCAT (End2End)
0.746
0.358 (-52.0%)
0.566 (-24.1%)
NeuPRINT (Implicit)
0.787
0.644 (-18.2%)
0.743 (-5.6%)
VAE (Explicit)
0.732
0.569 (-22.3%)
0.669 (-8.6%)
(Implicit) model demonstrates the strongest baseline performance with an in-domain accuracy of 0.787, followed by LOLCAT (End2End) at 0.746 and VAE at 0.732. However, when tested under cross-stimulus conditions, all models experience significant performance degradation, with LOLCAT showing the most dramatic drop (-52.0%) to 0.358, while NeuPRINT maintains relatively better performance with a more modest decrease (-18.2%) to 0.644. The introduction of adversarial training leads to substantial improvements across all models, with LOLCAT showing the most remarkable recovery 1+58.1 1%) to 0.566. This suggests that while End2End is more sensitive to stimulus varia- tions, benefits the most from adversarial training in terms of relative improvement. NeuPRINT, despite its initial robustness, still shows meaningful improvement (+15,4%) with adversarial training. achieving the highest final accuracy of 0.743.
Table 4 Table 5 and Table 6 provide detailed breakdown of model performance when each visual stimulus condition is held out as the test set. Across all models, we observe a consistent trend: prediction accuracy is lowest when spontancous activity is used as the test condition, moderately higher for drifting gratings, and highest for both natural scene image sets. This pattern highlights the particular challenge posed by spontancous activity, which appears to be the most distinct from the other stimulus types terms of neural response patterns.
Interestingly, we believe that the relatively poor performance on spontaneous and drifting grating conditions is not only due to their distributional differences. but also because these stimulus types relatively simple and less diverse. As result, neuronal activity under these conditions may not fully show the their computational roles.
Table 2: Cell Type Prediction Across Animals (V1-Cel/Type). Each cell shows the top-1 accuracy. The "Out-of-domain" columns show the performance the generalization task and, in parentheses, the percentage decrease relative to the "In-domain" result.
In-domain (Random Split)
Out-of-domain
Out-of-domain
Model
(Cross-Animals)
(+Adversarial)
LOLCAT (End2End)
0.746
0.452 ( (-39.4% 39.4%)
0.694 (-7.0%)
NeuPRINT (Implicit)
0.787
0.582 (- 26.0%)
0.739 (-6.1%)
VAE (Explicit)
0.732
0.557 ( ( 23.9%)
0.668 (877%
Cell Type Prediction Across Animals (V1-CellType) Table presents the performance of models on cell type prediction across different animals using the VI-CellType dataset. The results reveal interesting patterns in model generalization capabilities. Similar to the cross-stimulus scenario, all models show significant degradation under cross-animal conditions. LOLCAT experiences the most severe performance drop (-39.4%) to 0.452, while VAE shows relatively better preservation of performance with a -23.9% decrease to 0.557. The application of adversarial training leads to substantial improvements across all models, with LOLCAT again showing the most dramatic recovery (+53.5%) to 0.694. Notably, while NeuPRINT maintains the highest absolute accuracy throughout, LOLCAT demonstrates the most significant relative improvement with adversarial training, suggesting that adversarial training is particularly effective for models that are initially more sensitive to animal-specific variations.
Anatomical Brain Region Prediction Across Animals (IBL Brain-wide Map) In Table ??, we assess the performance of different models on anatomical brain region prediction across animals using the IBI. Brain-wide Map dataset. The results reveal a more balanced performance landscape compared to the V1 V1-CellType experiments. All models show similar baseline performance, with
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Table 3:
Anatomical Brain Region Prediction Across Animals (IBL Brain-wide Map). Each cell shows the top-1 accuracy. The "Out-of-domain" columns show the performance on the generalization task and, in parentheses, the percentage decrease relative to the "In-domain" result.
In-domain
Out-of-domain
Out-of-domain
Model
(Random Split)
(Cross-Animals
(+Adversarial)
LOLCAT (End2End)
0.507
0.389 (-93.11% 23.1%%
0.482 (-4.9%)
NEMO (Contrastive)
0.511
0-400(-21.7)
0.496 (-2.9%)
VAE
0.488
(-1.99%))
(-457(-6.4%)) (6.4%%
NEMO (Contrastive) slightly leading 0.511, followed by LOLCAT at 0.507 and VAE 0.488. Under cross-animal conditions, the performance degradation is more moderate compared to the V1- CellType experiments, with decreases ranging from -18.9% -23.15 1%. The application adversarial training leads to consistent improvements across all models, with NEMO showing the most significant relative improvement (+24.0%) to 0.496. Interestingly, while the absolute improvements are smaller than in the V1 V1-CellType experiments, the relative improvements are more consistent across models, suggesting that adversarial training may particularly effective for anatomical region prediction tasks. The final performance levels also more closely clustered, with all models achieving accuracies between 0.457 and 0.496, indicating that this task may be inherently more challenging or that the models have reached common performance ceiling.
UMAP by Cell Type
UMAP by Batch
UMAP by Cell Type
UMAP by Batch
winet chos
steist
Figure 3: UMAP visualizations of single-neuron embeddings learned by the NeuPRINT model, colored by cell type (left) and batch/stimulus condition (right). Batchl and Batch2 correspond to the two natural scene stimulus conditions (natural 01 and natural 02), while Batch3 and Batch4 correspond to spontaneous activity and drifting gratings, respectively. (a) Without adversarial training. (b) With adversarial training.
Visualization To qualitatively assess the impact of adversarial training on the batch invariance of single-neuron representations, we visualized the learned embeddings of the NeuPRINT model using UMAP, colored by both cell type and batch (stimulus condition), as shown in Figure 3 In the baseline setting without adversarial training (Figure 3p), the UMAP projection colored by cell type reveals partial separation between neuronal subtypes, indicating that the model captures some intrinsic cell type information. However, when colored by batch, the embeddings exhibit clear clustering according to the four stimulus conditions, demonstrating a pronounced batch effect. This suggests that the learned representations are confounded by stimulus-specific information, which may hinder generalization to novel conditions. In contrast, after applying our adversarial training framework (Figure the UMAP visualization shows that the separation between cell types preserved or even enhanced, while the clustering by batch is substantially diminished. The embeddings corresponding to

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different stimulus conditions are now well mixed, indicating that batch-related information has been effectively removed from the representations. This qualitative result demonstrates that adversarial training successfully disentangles intrinsic neuronal properties from batch effects, enabling the model learn more robust and generalizable single-neuron representations. These findings are consistent with our quantitative results, highlighting the effectiveness of adversarial training in improving cross-condition generalization.
Disscusion
Conclusion: This work emphasizes the importance generalization in single-neuron representation models for reliable real-world neuroscience applications. We established rigorous evaluation proto- col assessing performance on unseen animals and stimuli, offering a stringent robustness check. Our proposed model-agnostic adversarial training framework, which eliminates batch-related information from representations, consistently enhances cross-condition and cross-animal generalization across state-of-the-an models. This advancement promotes more robust practical use of such models in neuroscience research.
Limitation: Our adversarial training framework has notable limitations. First, experiments are restricted to mouse data, leaving cross-species generalization (e.g.. rodents to primates/humans) intested-critien for translational research. Second, evaluations are limited to specific recording platforms and labs; batch effects from varying acquisition devices or protocols across labs pose unaddressed challenges for universal models. Third, some neuronal subtypes show poor cross-animal generalization post-training, potentially due to genuine biological variability or low evolutionary con- servation. Additionally, comparisons with other batch-invariance methods (including non-adversarial and single-cell omics approaches) remain underexplored. Fourth, in this work, we did not treat different trials as distinet batches, which would require more complex experimental design. This is indeed a highly promising area for exploration, as some neurons exhibit reduced response strength upon repeated exposure to the same stimulus [17]
Broad Impact: At present, our study limited to mouse datasets, and the broader societal impact is correspondingly constrained. However, the proposed adversarial training framework represents significant step toward the development of more robust and generalizable single-neuron representation models. By mitigating batch effects and improving model generalization, our approach has the potential to facilitate the deployment of computational models in vivo experiments, enabling more precise and reliable identification of neuronal cell types and anatomical locations. In the long term, this could accelerate the translation of computational neuroscience advances into experimental and clinical applications, ultimately contributing to a deeper understanding of brain function and discase.
7 Reproducibility
The process of reproducing these models was very difficult for us. and made certain changes to the original code to ensure that runs and converges. The original code github..com Eethuub.com/Iune coom//ummmii///euPRRIII and NEMO at hhttps://githe http://shhho.cccc/aaaaaaaalla/ap ICLR The data be found at the links: IBI. at http:hhhhhpr/.//...necocccoooaaa and VI-CellType at http:///w/apre....aaa....o.////a predicts_steee sttrssstttte_eeeeeeeeeeeeeeeeeet mtt.uaatiioooooooottttaaaaaaaaa
be accessed via the following links: NeuPRINT at httpp:// LOLCAT at lttps://githhb. cowm/eeddlaabllllc....
All work can be com-
pleted on single A100.
Acknowledge
This research was supported by National Key Research and Development Program of China (2024YFF0507400) and National Natural Science Foundation of China (6220071694).

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[7] Han Yu, Hanrui Lyu, YiXun Xu, Charlic Windolf, Eric Kenji Lec, Fan Yang, Andrew M Shelton, Olivier Winter, Eva L Dyer, Chandramouli Chandrasckaran, et al. In vivo cell-type and brain region classification via multimodal contrastive learning. In The Thirteenth International Conference on Learning Representations.
[8] Maxime Beau, David J Herzfeld, Francisco Naveros, Marie E Hemelt, Federico D' Agostino, Marlies Oostland, Alvaro Sánchez-López, Young Yoon Chung, Michael Maibach, Stephen Kyranakis, et al. A deep learning strategy to identify cell types across species from high-density extracellular recordings. Cell, 188(8):2218 2222224441222. 2025.
[9] Jordi Abante, Angelo Piga, Berta Ros, Clara López-León, Josep M Canals, and Jordi Soriano. Single-neuron deep generative model uncovers underlying physics of neuronal activity in ca imaging data, 2025.
[10] David Herzfeld, Nathan Hall, and Stephen G Lisberger. Strategies to decipher neuron identity from extracellular recordings in the cerebellum of behaving non-human primates. bioRxiv, pages 2025 00, 2025.
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[12] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. networks, 2016.
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A Detail Methods
In this section, present model-agnostic adversarial training framework designed to mitigate batch effects single-neuron representations, thereby enhancing generalization across experimental conditions and animal subjects. Our approach integrates seamlessly with existing single-neuron representation paradigms, including Transformer-based models, contrastive learning frameworks, and variational autoencoders (VAEs).
A.1 Problem Formulation
Let == {x, M denote a collection of raw neuronal observations, where each x, corresponds to single neuron. Each neuron is associated with: An intrinsic property label yi e (e.g., cell type, anatomical region),- batch label b, E B encoding experimental metadata (e.g., animal ID, stimulus condition, recording session).
The goal of single-neum neuron representation learning is learn an encoder fo X Rd that maps to a low-dimensiondl embedding = fe (x,), such that z; captures y:-relevant intrinsic features while being invariant to by-related batch effects. Downstream tasks (e.g., classification of y.) are then performed using 2,,
Key challenge: Standard models overfit to b, leading to poor generalization when tested neurons from unseen b & support(training batches).
A.2 Adversarial Training Framework
Our framework consists of two components: R.eppesentttionn Encoder fo: Maps raw data x; to embeddings 2,, optimized to preserve intrinsic features relevant yı- Batch Discriminator Dz: A classifier that takes 2; as input and predicts the batch label b, optimized to detect batch-specific patterns Z,
Training proceeds adversarially: fe aims to produce embeddings that "fool" D& (i.e., z; contains no information about by), while D3 aims accurately predict b, from z;. This min-max game enforces z; to be invariant to batch effects.
A.3
Objective Functions
The total training objective combines downstream task-specific base loss and an adversarial loss:
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Base Task Loss Dosse This loss depends on the specific representation learning paradigm and down- stream task:
Suppervised/sef--suerrvviii learning: For classification tasks (e.g., cell type prediction), Druse is cross-entropy between predicted y/ and ground truth.
Reconstruction- models (e.g., VAES): Lrase includes reconstruction loss (e.g., mean squared error) and latent distribution regularization.
Contrastive learning: Lisse contrastive loss maximizing similarity of augmentations from the same neuron and minimizing similarity across different neurons.
Adversarial Loss Lady The discriminator Do is trained to minimize the batch classification loss Laach Z2 bi), typically cross-entropy:
Dratch - (x, b,)-D llog D (z,, b;)],
where is the data distribution. The encoder fo is trained to maximize Laucch (i.e., confuse Da), leading to the adversarial objective:
Ludv B(., [log(1 Dz b:))]
3.3.3 Joint Optimization The combined objective balances preserving task-relevant features and removing batch information:
min max
(fas Cbonch(()) o fa).
where > controls the strength of adversarial regularization. To simplify training, we use Gradient Reversal Layer (GRL) [12), which applies gradient sign reversal during backpropagation from D& to fe. enabling end-to-end optimization without explicit min-max max alternation.
A.4 Model-Agnostic Architecture Design
Our framework is compatible with diverse single-neuron representation models by treating fo pluggable component:
Transformer-based models (c.g., (5): fe is Transformer encoder that processes time-series spike data, with Coase defined by downstream classification or self-supervised objectives.
Contrastive learning frameworks (e.g., [7): fo encodes multimodal inputs (waveform, 3D ACG), and Losse includes contrastive losses across modalities, with D& operating on the unified embedding space.
Variational autoencoders (e.g.. 8 fo is the VAE encoder, Lasse combines reconstruction loss and KL divergence, and D& regularizes the latent space to be hatch-invariant.
The discriminator D/ is
multi-layer perceptron (MLP) with architecture
(embedding dimension + hi + h2 + |B]]. where ht, h2 are hidden layers.
A.5 Implementation Details
For all experiments, models were trained using the Adam optimizer with an initial learning rate of le-4. The learning rate was reduced by factor of 0. if the validation loss platcaued for 5 consecutive epochs. We used batch size of 512 and trained with early stopping criterion based on validation accuracy to prevent overfitting. The discriminator was implemented as two-hidden-layer MLP with 256 units per layer and ReLU activations. The adversarial weight, which balances the loss of primary tasks with the loss of adversarial, was selected among 8 values between 0.00001 and 100 on a logarithmic scale based on reverse cross-validation.
B LOLCAT Framework
LOLCAT is a supervised framework for predicting cell types from individual neuronal activities using a multi-head attention network, consisting of three main components.
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Local segment feature extractor: First process segments before aggregating the information globally: For each segment, the inter-event interval (IEI) distribution is computed using D log-spaced bins. Each segment is 256 timepoints. Segments are sampled randomly from raw calcium traces.
X₁ R. TET
The resulting D-dimensional input vector is fed to the local feature extractor, which is multi-layer perceptron (MLP) equipped with batch normalization layers and rectified linear units.
Yt E (x)e) Mocal
R + R
The same local feature extractor is shared across segments, as tasked to extract features that locally characterize the signature of a neuron's 's activity.
Multi-head attention module: We aggregate the extracted local features produce cell-level representation that describes its global distribution. To allow the network to seek out or attend to specific segments, an attention network generates an attention it to weight the segment's contribution to the final global feature vector.
for each segment, and then uses
softmar ((gate,((t)))
lgate, TR - R
(0)
Σ di (0 (v:),
rEtriols
This is simply a weighted sum of the segment-level local features. The use the softmax operator ensures that the attention scores up All the pooled feature vectors are concatenated to produce a final global feature vector, which can simultancously include features describing the average statistics of the neuron's activity and other features encoding the presence of segments that are characteristic of a particular cell type (or group).
z concat 21)
(n)
After concatenating all of the features from the different attention heads, we then pass this global representation to a final classification network, which uses the information aggregated at multiple scales, with different degrees of selectivity, to predict the cell type. We use an MLP with two hidden layers.
C NeuPRINT Framework
Sampling: For each neuron randomly sample 2 segments of 512 timesteps from its continuous calcium flourescence traces. Each resulting sample for the 7th neuron is denoted (
We use the transformer encoder architecture and the masking strategy. Random timesteps in (+) are masked (zero-out) with probability 0.25 and concatenated with the permutation- invariant population summary P and lime-invariant representation (+) along the feature dimension
Multihead attention:
to form input (1) Note that the same learnable (x)) is repeated at every timestep, enforcing time-
invariance. We embed input (1) and employ sinusoidal positional embedding to encode the temporal order in the input sequence, resulting in
( Emb(X(() E.
For each input (1), a set of weights Wº E Txy, W E Rxxde WV Txds are learned to transform input (4) to a set of query, key, and value (Q, K, V), where
Q = x=xw == W = WV.
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Attention between temporal tokens for one attention head is computed as:
QK Vd
Attention(44 K, K) = softmax
V
(8)
Each head will find different pattern in the data and produce an output of size dg. The final attention output will be concatenation of these single-head outputs. We use 2 heads in our model.
Feedforward layers and residual connections subsequently applied to attention output:
Z X MSA((X + FF(X MSA(X()))
where MSA represents the multihead attention operation, FF represents the feedforward layer with ReLU activation, and ( represents the reconstructed calcium trace with masked timesteps recovered.
D VAE Framework
We adapt variational autoencoder paradigm from the LOLCAT framework, enabling self-supervised learning of cell-level representations from neuronal activity segments, followed by downstream classification. The process is as follows:
Local Segment Feature Extractor: As in the original LOLCAT, each segment is first processed to obtain the inter-event interval (IEI) distribution, resulting in a D-dimensional input vector:
X1 L ERR,
LET
This vector fed into local feature extractor multi-layer pereeptron with batch normalization and ReLU activations):
Ye hoeal xx):
Nooca RRD. R
2. Multi-head Attention Module:
The extracted local features are aggregated using multi-head attention mechanism to produce global cell-level representation. For each attention head i:
(oftmaxx(ggate,fie ((v:),
hyate, RR. R
(6)
(1) (y:).
(
R
+ RF
tEtrials
The global representation is obtained by concatenating the outputs of all attention heads:
concat 1 (1)
(m)
3. Variational Autoencoder (VAE) Module: Instead of directly using for classification, treat it as the input to a VAE. The VAE encodes z into latent distribution:
(ulz))) Nuu pzz),, diag(o"(())
where u is the latent variable, and px-)), o are neural networks parameterized by ø.
The decoder reconstructs the original segment-level IEI features from the latent variable:
Po Pp(fx;
The VAE is trained by maximizing the evidence lower bound (ELBO):
f (uj() Po (xxH))))) - DxL (ulz) (p(u))
where p(u) is standard normal prior.
Downstream Classification: After self-supervised training, the learned latent representation is
used for downstream cell type classification. two-layer MLP is trained on u to predict the cell type:
MLP Pele (u)
where MLP consists of two hidden layers with non- -ineirr activations.
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Table 4: LOLCAT (End2End) performance when different stimulus conditions are used as test set.
Spontancous as test
Drifting as test
Natural01 test
Natural02 test
Setting
Variance
Out-of-domain )rooss-Stimulu)
0.251 0.462
0.312 0.523
0,432 0.631
0.437 0.648
0.0082 0.0074
Out-of-domain (+Adversarial)
Table 5: NeuPRINT (Implicit) performance when different stimulus conditions are used as test set.
Spontaneous as test
Drifting as test
Natural01 as test
Natural02 as test
Setting
Variance
Out-of-domain Ccross-Stimllne
0.542 0.648
0.612 0.708
0.713 0.812
0.709 0.804
0.0069
Out-of-domain (+Adversarial)
0.0063
E
Introduction of Different Stimuli
Drifting gratings centred the mean receptive field of the microscope's field of view. The coatings had a duration of 0.5 s, temporal frequency of 2 Hz, and a spatial frequency of 0.15 cycles per degree. The gratings drifted in 12 different directions (from 0 330", separated by 30") and were 3 different sizes (5", 15 and 60" diameter).
Spontaneous activity recorded in front of uniform grey screen, set steady cyan level equal to the background of all the stimuli presented for visual responses protocols. The duration these grey screen presentations was typically between 15 and 20 min.
Natural scenes from the ImageNet database were centrast-normalizerr Each image was presented for 0.5 with an interstimulus interval uniformly distributed from 0.3 to 1.1 s. Five per cent of the total presentations was grey stimuli. During each session we presented given of 1,000 different natural images twice.
F Detailed Cross-Stimulus Evaluation Results
Table Table and Table 6 provide detailed breakdown of model performance when each visual stimulus condition is held the test set.
For the LOLCAT (End2End) model, performance under the cross-stimulus protocol drops most sharply when generalizing spontaneous activity, and is also relatively low for drifting gratings. In contrast, the model achieves higher accuracy when evaluated on natural scene stimuli, indicating that complex scenes are more informative for cell type prediction. Adversarial training leads to substantial recovery in performance, particularly for the more challenging spontaneous and drifting conditions, while the accuracy for natural scenes remains robust.
The NeuPRINT (Implicit) model demonstrates overall higher robustness, with less dramatic drops in accuracy across all stimulus types. However, it still shows lower performance spontaneous and drifting conditions compared natural scenes. Adversarial training further boosts its perfor- mance, especially for the more difficult spontaneous and drifting conditions, thereby narrowing the performance gap between stimulus types.
The VAR model exhibits a similar trend: its baseline cross-stimulus accuracy is lower than that of NeuPRINT, with the lowest performance on spontaneous activity and drifting gratings. Adversarial training leads to substantial improvements across all conditions.
Overall, these results underscore the importance of evaluating models under diverse and challenging stimulus conditions. The relatively poor performance on spontaneous and drifting conditions may be attributed to the limited diversity of activity elicited by these stimuli, which may not fully reveal the computational roles of different cell types. Adversarial training not only improves average performance but also helps models generalize more evenly across different types of visual input, with the most pronounced benefits observed for the most challenging test conditions.
22

---

Table 6: VAE performance when different stimulus conditions are used as test set.
Spontaneous as test
Drifting as test
Natural01 as test
Natural02 as test
Setting
Variance
Out-of-domain (Cross-Stimullee Stimulus)
0.468 0.573
0.532 0.634
0.637 0.732
0.639 0.737
0.0075
Out-of-domain (+Adversaiia)
0.0068
Table LOLCAT (End2End) performance, comparing training on two stimulus conditions versus the baseline of training all three other conditions. The results illustrate the performance decrease when using less diverse training set.
Test Set
Training Set Combination
Train: All Others
0.462
Drifting, Natural01
0.410
Spontaneous
Drifting, Natural02
0.425
Natural01, Natural02
0.445
Train: All Others
0.523
Spontaneous, Natural01
0.485
Drifting
Spontaneous, Natural02
0.499
Natural01, Natural02
0.512
Train: All 3 Others
0.631
Spontaneous, Drifting
0.530
Natural01
Spontaneous, Natural02
0.595
Drifting, Natural02
0.615
Train: All 3 Others 0.648
Spontaneous, Drifting
0.542
Natural02
Spontaneous, Natural01
0.605
Drifting, Natural01
0.628
23


Reviews:
[{"review_id": "7uXLCP6HO8", "reviewer": "Reviewer_NvrJ", "summary": "The manuscript addresses a practical weakness of recent single-neuron in-vivo representation models: their tendency to over-fit \"batch\" factors such as stimulus set, recording session, animal identity or hardware platform. The authors (i) define a rigorous OOD protocol, (ii) propose a model-agnostic adversarial remedy, (iii) demonstrate compatibility with LOLCAT, NeuPRINT, VAE and NEMO, and (iv) show robust improvements across mouse datasets.", "questions": "* What is the motivation for picking a test set that is visually and statistically so unlike the training stimuli? I couldn't find a reference to Figure 1 in the main text. Is the goal to probe the most extreme distribution shift, or does this particular stimulus have biological relevance?\n* Primate cortex is notably different from mouse cortex, in terms of hierarchy, computation, and level of success that the deep learning community has had at modeling it. Will this translate to primate cortex and beyond? Which aspects of the adversarial objective do you expect to transfer or fail?\n* Currently the contribution is framed for single-neuron representation learning. Please explain explicitly how the findings might feed back into mainstream AI.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "This is a technically solid, clearly written contribution that offers an actionable remedy and a much-needed evaluation protocol for OOD robustness in neural representation learning. Methodological novelty is modest, but the work's practical value and clean empirical validation make it a worthwhile addition to the literature.\n\nQUALITY\n\nStrengths:\n* Model-agnostic intervention (the gradient-reversal discriminator plugs into 4 different baselines)\n* Both accuracy/ROC and UMAP are reported, and improvements are consistent\n\nWeaknesses:\n* Only two mouse datasets are used; no check on primate or synthetic benchmarks, so the claim of \"cross-lab variation\" is still speculative\n* No results on alternative batch-invariance methods (e.g., mix-style, batchnorm-free) or sensitivity to $\\lambda$ (adversarial weight)\n* While embeddings preserve coarse cell-type groups, the paper does not test whether trial-level neural tuning curves remain interpretable after adversarial training\n\nCLARITY\n\nStrengths:\n* Writing is concise; the motivation for OOD splits is immediately clear\n* Architecture diagrams and tables are clear\n\nWeaknesses:\n* Key implementation details (optimizer, $\\lambda$ schedule, discriminator capacity) sit in Appendix D; surfacing them earlier would aid replication\n* The paper cites “single A100 @ 10 h” but omits batch size and sequence length, which is important for those without A100s\n\nSIGNIFICANCE\n\nStrengths:\n* addresses a practical painpoint for neuroscientists\n* provides a model-agnostic evaluation protocol for single-neuron representation work\n\nWeaknesses:\n* Impact is confined to neural-representation learning; broader ML domains have long used domain-adversarial training\n\nORIGINALITY\n\nStrengths:\n* First paper (to my knowledge) to marry domain-adversarial training with single-neuron time-series models *and* to quantify OOD gaps systematically across stimulus and animal factors\n* The dual OOD axes (stimulus-held-out vs animal-held-out) create a novel benchmark\n\nWeaknesses:\n* Gradient-reversal layers are standard in domain adaptation; the technical novelty lies more in application than in method\n* No new theoretical insights into why adversarial erasure of batch cues preserves task-relevant neuron structure", "was_revised": true, "final_justification": "I had minor concerns, which the authors have now addressed", "rebuttal": "We extend our sincere gratitude to the reviewer for their thorough and insightful feedback. We are encouraged that the reviewer recognized our work as \"technically solid, clearly written,\" and a \"worthwhile addition to the literature\" that \"addresses a practical painpoint for neuroscientists.\" We were particularly pleased that the reviewers appreciated the novelty of our contributions, noting that this is the \"first paper... to marry domain-adversarial training with single-neuron time-series models\" and \"to quantify OOD gaps systematically across stimulus and animal factors.\"\n\nWe have carefully considered all comments and have revised the manuscript accordingly. Below, we address each of the points raised.\n\n---\n\n***Weaknesses***\n\n**Response: Limited Datasets and Cross-Lab Claims**\n\n> *Only two mouse datasets are used; no check on primate or synthetic benchmarks, so the claim of \"cross-lab variation\" is still speculative.*\n\nWe thank the reviewer for this important point. We agree that our study, being based on two mouse datasets, represents the first crucial step in addressing generalization, and that demonstrating robustness across labs and species is a critical future direction. We have explicitly acknowledged this in the Limitations section of our manuscript. We agree that the claim of \"cross-lab variation\" is speculative at this stage. Accordingly, we have revised the manuscript to confine the discussion of cross-lab variation to the Future Work section, ensuring our claims accurately reflect the scope of our current results. Our immediate future work will focus on extending this framework to more diverse, multi-lab, and multi-species datasets, such as the Allen Institute’s Visual Coding Neuropixels dataset and the Steinmetz dataset. This will allow for a rigorous evaluation of the cross-lab and cross-species generalization capabilities of our proposed method.\n\n**Response: Alternative Methods and Hyperparameter Sensitivity**\n\n> *No results on alternative batch-invariance methods (e.g., mix-style, batchnorm-free) or sensitivity to $\\lambda$ (adversarial weight).*\n\nWe thank the reviewer for raising these two important points.\n* **On alternative batch-invariance methods:** We agree that a comparative analysis with other methods is an important area for future research. Our primary contribution is to be the first to identify, systematically benchmark, and provide a viable solution to the critical generalization problem in the emerging field of single-neuron identity models. While other batch-invariance methods exist, we believe that demonstrating the effectiveness of a well-established technique like adversarial training provides a strong and essential baseline for this new domain. We have now expanded our Discussion section to include these alternative approaches as a promising avenue for future investigation.\n* **On sensitivity to the adversarial weight $\\lambda$:** We apologize for not making this clearer in the main text. The adversarial weight $\\lambda$ was not arbitrarily chosen but was selected through rigorous cross-validation, as detailed in our appendix. Specifically, we tested 8 values on a logarithmic scale from $10^{-5}$ to $100$. We observed that downstream task accuracy consistently increased with the adversarial weight before stabilizing for values of 0.01 and higher, with no subsequent drop in performance. This demonstrates the robustness of our approach to this hyperparameter. We have clarified this selection process in the revised manuscript.\n\n**Response: Preservation of Trial-Level Tuning Curves**\n\n> *While embeddings preserve coarse cell-type groups, the paper does not test whether trial-level neural tuning curves remain interpretable after adversarial training.*\n\nThis is an excellent point that touches on a more fine-grained level of analysis. Our current study focuses on mitigating batch effects at the level of entire stimulus *conditions* (e.g., natural scenes vs. gratings) and *animals*, which we argue is the foundational step for building generalizable models. Analyzing trial-to-trial adaptation within a stimulus block—such as the response decrements observed with repeated stimuli in works like Kehl et al. (2024)—is a fascinating, orthogonal problem. This would require a more complex experimental design where individual trials or trial blocks are treated as distinct batches. While this is outside the scope of our current work, we recognize its importance. We have added a discussion of this topic to our Future Work section to inspire further research in this direction.\n\nKehl, M.S., Mackay, S., Ohla, K. et al. Single-neuron representations of odours in the human brain. Nature 634, 626–634 (2024). https://doi.org/10.1038/s41586-024-08016-5\n\n**Response : Accessibility of Implementation Details**\n\n> *Key implementation details (optimizer, schedule, discriminator capacity) sit in Appendix D; surfacing them earlier would aid replication.*\n\nWe appreciate the reviewer’s suggestion to improve the paper's accessibility and reproducibility. To address this, we have surfaced the key implementation details by creating a new **Appendix A.5: Implementation Details**. Furthermore, we have added a prominent reference to this new section in the first paragraph of the main **Section 5: Experiment Setup**. This change ensures that researchers can more easily find the necessary information to replicate our work.\n\n---\n\n***Questions:***\n\n**Response : Question1**\n\nThank you for pointing out the missing reference to Figure 1; we have now added it. The figure is a schematic to illustrate our out-of-distribution testing principle, not a literal depiction of our test set. Our actual experiment did not use one single \"unlike\" test set. Instead, we used a leave-one-out protocol with the four diverse stimulus types in the dataset (natural scenes, drifting gratings, spontaneous activity). We trained on three conditions and tested on the held-out one, repeating for all four. Your intuition about an \"extreme distribution shift\" is correct. We found that models struggled most when generalizing from complex stimuli (like natural scenes) to simple ones (like spontaneous activity). The likely biological reason is that complex stimuli are required to reveal the unique computational roles of different neurons, making the functional signatures learned during training much richer than the simple patterns observed in spontaneous activity.\n\n**Response : Question2**\n\nThat is a critical question. We believe the success of our method in primates will depend on the evolutionary conservation of the features being studied.\n\nWhat we expect to transfer: The method should generalize well for broad, conserved features. This includes classifying major brain regions (e.g., isocortex vs. cerebellum) or high-level cell types (e.g., excitatory vs. inhibitory), as their fundamental roles are shared across species.\n\nWhat we expect to fail: Generalization will likely be challenging for highly specific neuron subtypes or anatomical sub-regions that have diverged significantly between mice and primates. Our adversarial method cannot force a common representation if the underlying biological link between a neuron's identity and its activity patterns has fundamentally changed.\n\n**Response : Question3**\n\nOur work provides a framework for understanding the specific function of an individual computational unit (a biological neuron) by isolating its intrinsic properties from confounding variables. This has a direct analogy in mainstream AI.\n\nOne could apply our adversarial framework to an artificial neural network (ANN) to learn a robust representation for each individual node or neuron. By training the model to perform its primary task while simultaneously making it impossible for a discriminator to tell which training batch or input type an individual node's activation came from, we could uncover the node's core, invariant function.\n\nThis would help us understand the \"division of labor\" within large AI models, improving interpretability by revealing the specific role of their most basic components.\n\n\nWe thank the reviewer once again for their constructive feedback, which has helped us significantly improve the clarity and impact of our manuscript. We are confident that the revised paper now more robustly supports our contributions and provides a solid foundation for future work on generalizable single-neuron representation learning. In light of these revisions, which we believe address the initial concerns, we would be grateful if you would reconsider your assessment of our work."}, {"review_id": "1qgNJIfvY2", "reviewer": "Reviewer_a9Fa", "summary": "The paper \"Adversarial Training for Generalized and Invariant Single-Neuron In-Vivo Activity Representation\" addresses out of domain generalization problems in neuron models that present single neurons for clustering and cell type identification. The authors establish an out of domain test protocol and show that combining existing methods with domain adversarial training improves the out of domain robustness.", "questions": "Please see weaknesses. In short:\n\n- Clarify better what your paper addresses. Predicting neuronal activity or learning neuronal fingerprints. \n- If its predicting neuronal activity: Then you need to account for related work (I listed examples above). \n- Improve clarity of the presentation of the material.", "limitations": "Limitations are addressed in the final paragraphs.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Biological clustering and cell type identification are important problems for neuroscience. Showing that existing methods suffer from out of domain generalization problems addresses an important problem. \n- I like that the paper is straightforward and doesn't try to be fancy when it's not necessary: Takes existing methods, shows that there is a problem, combines it with existing domain adversarial methods which largely fixes the problem. \n\nWeaknesses:\n- There is no novel algorithm here, but I don't think this is a major issue as the contribution is rather the finding and the fix with straightforward algorithms. \n- The paper could be improved in clarity of writing. Unless I completely misunderstood the paper, the main purpose is not to predict neuronal activity but rather to train an embedding to identify cell types. This is not very clear from the introduction and should be clarified. If the paper is about encoding model that predict neuronal activity, then a lot of the relevant literature is missing (a few examples are listed below).\n- The paper could also profit from another iteration of proofreading and structure refinement. For instance: On page 5 there is the paragraph on Theoretical Motivation. This paragraph then states that it does not \"want to introduce it too theoretically\" and refers to appendix A. Appendix A5 with the title \"theoretical motivation\" is 4 lines long, does not include any theoretical motivation and is not really informative by itself. I would ask the authors to make the presentation of the material more coherent. \n\nI. Ustyuzhaninov, S. A. Cadena, E. Froudarakis, P. G. Fahey, E. Y. Walker, E. Cobos, J. Reimer, F. H. Sinz, A. S. Tolias, M. Bethge, A. S. Ecker Rotation-invariant clustering of functional cell types in primary visual cortex International Conference on Learning Representations (ICLR), 2020\n\nEric Y. Wang, Paul G. Fahey, Zhuokun Ding, Stelios Papadopoulos, Kayla Ponder, Marissa A. Weis, Andersen Chang, Taliah Muhammad, Saumil Patel, Zhiwei Ding, Dat Tran, Jiakun Fu, Casey M. Schneider-Mizell, R. Clay Reid, Forrest Collman, Nuno Maçarico da Costa, Katrin Franke, Alexander S. Ecker, Jacob Reimer, Xaq Pitkow, Fabian H. Sinz, Andreas S. Tolias Foundation model of neural activity predicts response to new stimulus types Nature\n\nPolina Turishcheva, Max Burg, Fabian H. Sinz, Alexander Ecker Reproducibility of predictive networks for mouse visual cortex NeurIPS\n\nKonstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Friedrich Willeke, Akshay Kumar Jagadish, Eric Wang, Edgar Y Walker, Santiago Cadena, Taliah Muhammad, Eric Cobos, Andreas Tolias, Alexander Ecker, Fabian Sinz Generalization in data-driven models of primary visual cortex ICLR \n\nA Spectral Theory of Neural Prediction and Alignment Abdulkadir Canatar, Jenelle Feather, Albert Wakhloo, SueYeon Chung", "was_revised": true, "final_justification": "The authors addressed my comments. Even though the paper does not introduce a novel method, I think describing the problem and fixing it will have a high impact on everyone else who is trying to catalogue cell types. Thus the paper meets the criteria for 5: accept and I increased my score.", "rebuttal": "We sincerely thank the reviewer for their valuable feedback and for appreciating the straightforward nature of our work—identifying an important generalization problem and applying a direct, effective solution. We agree with the points raised regarding clarity and presentation and have revised the manuscript to address them.\n\n\n**Clarifying the Paper's Core Contribution and Goal**\n\nWe agree entirely that the paper's primary goal—learning robust representations for neuronal identity, not predicting neural activity—was not stated clearly enough in the original manuscript. This lack of clarity is the most critical point to fix, and we thank you for highlighting it.\n\nTo address this, we have significantly rewritten parts of the abstract and introduction. The revised text now explicitly states that our focus is on \"representation learning for neuronal identity from in-vivo activity.\" This clarification sharpens the paper's core contribution: identifying a critical generalization gap in this emerging area and demonstrating that an established method offers a robust, model-agnostic solution. This also makes it clear why the literature on predictive neural activity is not the focus of our work.\n\n**Improving Presentation and Coherence**\n\nThank you for rightly pointing out the weakness and lack of coherence in the \"Theoretical Motivation\" section. We agree completely that the original section and its corresponding appendix were uninformative and detracted from the paper's quality.\n\nWe have substantially revised this part of the paper to improve clarity and coherence:\n* We have removed the uninformative appendix on theoretical motivation.\n* We have retitled the original Section 4.5 to \"Intuitive Explanation\" to more accurately reflect its purpose.\n* We have completely rewritten this section to provide a clear, intuitive walkthrough of how the adversarial method works, grounding the explanation in the visual illustration provided in Figure 2, rather than making unfulfilled promises of a deep theoretical dive.\n\nWe have also merged the \"Dataset\" and \"Experiment Setup\" sections into a single, unified \"Benchmark Experiments Setup\" section. This improves the paper's flow, as each experiment is now a self-contained subsection that introduces its dataset and protocol together, preventing the reader from having to switch back and forth.\n\n---\n\nWe believe these revisions make the manuscript clearer, more coherent, and more accurately focused on its primary contribution. We thank you again for your constructive feedback, which has been invaluable in helping us strengthen the paper. We would be grateful if you would reconsider your assessment of our work."}, {"review_id": "f593wXKS3t", "reviewer": "Reviewer_ti1Y", "summary": "Current computational models of single-neuron activity commonly face generalization issues, often failing to accurately generalize to new stimulus types and unseen subjects.  The authors of this paper seek to address this limitation faced by current models by proposing an adversarial training strategy that encourages a model to predict single-neuron representations (usable for some downstream task such as predicting cell type) meanwhile fooling a discriminator that explicitly tries to predict batch-specific attributes that the representation model is susceptible to overfitting.  The authors demonstrate the efficacy of this proposed approach on two datasets: predicting cell type from using the V1-CellType dataset and predicting anatomical brain region using the IBL Brain-Wide Map dataset.  When training single-neuron representation models (LOLCAT, NEMO, and VAE models) with the proposed adversarial training strategy, the model generalizes much better (as compared to the same models trained without the adversarial learning strategy) to data from stimuli or subjects (animals) excluded from the training data.", "questions": "- In regard to weakness bullet point 3: How does expect this technique would scale with the number of batch-level attributes?  Neuroscience datasets are typically very limited in this regard.  A concrete experiment, for instance, may vary the number of stimulus types or animals available in training and evaluate as was done in the main text (evaluating performance across the remaining held-out stimulus types or animals).\n- Does this approach also boost in-distribution performance?  That is, if all stimulus types or animals are available in training (but we still maintain a held-out test split, as in the \"random split\" baselines of the paper), does the adversarial training still improve generalization?\n- Minor suggestion about data presentation in tables 1-3: In each table, the way the relative performance change is presented is unintuitive to me.  It seems that percent change in standard cross-attribute model results are presented relative to the random-split model, whereas cross-attribute + adversarial training results are presented relative to the standard cross-attribute model.  It would be more clear for both of these performance results to be presented relative to the random-split baseline.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Out-of-distribution robustness has historically been challenging in single-neuron representation models.  The results provided in this paper demonstrate the proposed approach as a relatively simple and flexible way to achieve greater out-of-distribution generalization, across subjects and stimulus types.\n- The proposed framework is generalizable and would be applicable to a wide variety of neuroscience modeling tasks.  It is not specific to any architecture, it can be applied to different batch-level attributes, etc.\n- Intuitive motivation and logical framework to solve this problem\n- Text and figures are clear and well-presented\n\nWeaknesses:\n- Technical novelty is limited: novel application but adversarial training with a discriminator to mitigate model biases (e.g., protected attributes) has been proposed in a variety of prior works\n- In depth evaluations beyond aggregate classification accuracies would are lacking.  For instance, the discussion section indicates that certain neuronal subtypes are poorly predicted, and it would be helpful if the paper showed this and any potential tradeoffs that resulted from using the adversarial training strategy.  Additionally, for transparency, I would like to see cross-animal evaluation results when different animals are used in the test set.\n- Related to the above, further analysis and probing into the efficacy of this method under constraints like dataset size, number of batch-level attributes, etc. would be appreciated.  For instance, would we still see similar improvements in generalization when only 2 stimulus types are used in training?  Does the method remain effective beyond classification tasks (e.g., predicting spiking activity from a subset of neurons to a given stimulus)?", "was_revised": true, "final_justification": "The methodology proposed in this paper is a practical solution to a very common challenge in neuroscience data modeling.  The authors have addressed my remaining questions and suggestions with detail and I believe this work provides a meaningful contribution to the field.", "rebuttal": "We sincerely thank the reviewer for their positive and constructive feedback. We are particularly grateful that you found our approach to be a \"simple and flexible way to achieve greater out-of-distribution generalization\" and appreciated the intuitive motivation and clear presentation of our work.\n\nWe agree with the weaknesses you have identified and have performed additional analysis and revisions to the manuscript to address them.\n\n---\n\n***On Technical Novelty***\n\nWe agree entirely with your assessment. The core contribution of our work is not the development of a new adversarial algorithm, but rather the novel application of this established technique to address a critical and previously un-benchmarked problem in computational neuroscience: the poor generalization of single-neuron representation models across different subjects and stimulus conditions. We believe that formally identifying this gap and demonstrating that a straightforward, model-agnostic framework can effectively solve it is a significant and practical contribution for neuroscientists who wish to apply these models in real-world experiments.\n\n***Efficacy with Fewer Training Batch-Level Attributes***\n\nFollowing the your suggestion, we trained the LOLCAT model with an adversarial loss using only two stimulus conditions and evaluated its performance on a held-out third condition. The results consistently demonstrated lower performance compared to the model trained on all three stimulus conditions, which indicates that employing a less diverse training set leads to performance degradation. For future work, we suggest exploring the impact of the number of batch-level attributes in the training set to further enhance the model's robustness. These discussions have been incorporated into the Discussion section.\n\n| **Test Set** | **Training Set Combination** | **Acc** |\n| :--- | :--- | :--- |\n| Spontaneous | **Train: All 3 Others** | **0.462** |\n| Spontaneous | Drifting, Natural01 | 0.410 |\n| Spontaneous | Drifting, Natural02 | 0.425 |\n| Spontaneous | Natural01, Natural02 | 0.445 |\n| Drifting | **Train: All 3 Others** | **0.523** |\n| Drifting | Spontaneous, Natural01 | 0.485 |\n| Drifting | Spontaneous, Natural02 | 0.499 |\n| Drifting | Natural01, Natural02 | 0.512 |\n| Natural01 | **Train: All 3 Others** | **0.631** |\n| Natural01 | Spontaneous, Drifting | 0.530 |\n| Natural01 | Spontaneous, Natural02 | 0.595 |\n| Natural01 | Drifting, Natural02 | 0.615 |\n| Natural02 | **Train: All 3 Others** | **0.648** |\n| Natural02 | Spontaneous, Drifting | 0.542 |\n| Natural02 | Spontaneous, Natural01 | 0.605 |\n| Natural02 | Drifting, Natural01 | 0.628 |\n\n***Efficacy in boost in-distribution performance***\n\nWe also tested the scenario you alluded to, where there is no out-of-domain challenge (i.e., all stimulus types and animals are present in the training data, with only a standard random test split). We did not observe significant and consistent improvement in performance, whether adversarial training was applied with respect to stimulus types or animal subjects.\n\n| Model | In-domain (Random Split) | + Adversarial Loss (Animal) | + Adversarial Loss (Visual Stimulus) |\n| :--- | :---: | :---: | :---: |\n| LOLCAT (End2End) | 0.507 | 0.501 (-1.18%) | 0.514 (+1.38%) |\n| NEMO (Contrastive) | 0.511 | 0.515 (+0.78%) | 0.503 (-1.57%) |\n| VAE | 0.488 | 0.484 (-0.82%) | 0.493 (+1.02%) |\n\n***Performance for each neuronal subtype***\n\nTo provide a more in-depth evaluation beyond aggregate accuracies, we analyzed the performance for each neuronal subtype. The results from NeuPRINT are in the tables below.\n\n### Cross-Stimulus Generalization by Cell Type\n| Setting | Pvalb | Vip | Lamp5 | Sst |\n| :--- | :---: | :---: | :---: | :---: |\n| **In-domain (Random Split)** | **0.787** | **0.743** | **0.761** | **0.371** |\n| Out-of-domain (Cross-Stimulus) | 0.644 | 0.607 | 0.424 | 0.131 |\n| + Adversarial Loss (Visual Stimulus) | 0.743 | 0.616 | 0.613 | 0.217 |\n\n### Cross-Animal Generalization by Cell Type\n| Setting | Pvalb | Vip | Lamp5 | Sst |\n| :--- | :---: | :---: | :---: | :---: |\n| **In-domain (Random Split)** | **0.787** | **0.743** | **0.761** | **0.371** |\n| Out-of-domain (Cross-Animals) | 0.535 | 0.549 | 0.582 | 0.103 |\n| + Adversarial Loss (Animal) | 0.689 | 0.711 | 0.739 | 0.177 |\n\nWe have added this detailed analysis to the manuscript. Thank you for helping us strengthen our work.\n\n***Breakdown of results for each of the four mice (SB025, SB026, SB028, SB030) as the test set***\n\n| Model | Metric | Test: SB025 | Test: SB026 | Test: SB028 | Test: SB030 |\n| :--- | :--- | :---: | :---: | :---: | :---: |\n| LOLCAT | Out-of-domain (Cross-Animal) | 0.391 | 0.523 | 0.417 | 0.477 |\n| | Out-of-domain (+Adversarial) | 0.642 | 0.751 | 0.679 | 0.704 |\n| NeuPRINT | Out-of-domain (Cross-Animal) | 0.513 | 0.649 | 0.577 | 0.589 |\n| | Out-of-domain (+Adversarial) | 0.691 | 0.788 | 0.724 | 0.753 |\n| VAE | Out-of-domain (Cross-Animal) | 0.492 | 0.611 | 0.537 | 0.588 |\n| | Out-of-domain (+Adversarial) | 0.618 | 0.719 | 0.649 | 0.686 |\n\n***Data presentation in tables 1-3***\n\nWe have recalculated the percentages in the third column \"cross-attribute + adversarial training\" to show the decrease relative to the first column's \"In-domain\" value. Thank you for helping us make our work more clear.\n\n\n\nWe thank the reviewer once again for their constructive feedback, which has helped us significantly improve the clarity and impact of our manuscript. We are confident that the revised paper now more robustly supports our contributions and provides a solid foundation for future work on generalizable single-neuron representation learning. In light of these revisions, which we believe address the initial concerns, we would be grateful if you would reconsider your assessment of our work."}, {"review_id": "X7ov14TNOX", "reviewer": "Reviewer_Awe4", "summary": "The paper is interested in the training of an embedding model of single-neuron categorization from functional recording. The authors provide a simple adversarial method to make the model generalize better to new stimulus conditions (or new animals). The method could be applied to enhance generalization across stimulus conditions. animal identity or others.\n\nThe key idea is to train the embedding model against a classifier in an adversarial fashion. While the classifier is optimized to classify stimulus conditions (or animals) from the embedding, the model is constantly optimized to make it harder for the classifier. In this way, the trained embedding holds no \"information\" about the stimulus condition (or animal identity). In practice, this appears to enhance generalization across animals. This technique is related to Domain adversarial neural networks outside of neuroscience, or gradient reversal training (cited by the authors).\n\nThe method is tested on three different embedding models of single neuron data (LOLCAT, NeuPRINT, and VAE). All three models use a different loss to construct an embedding of the neural activity. From the embedding, the performance is reported as the accuracy of a downstream cell type classifier from the embedding representation. It first confirmed that when one stimulus condition (or animal) is left out of training, the decoded performance of a trained cell-type classifier is lower when tested on the left-out stimulus (or animal). This defect is indeed reduced with the proposed method: during the training of the embedding, the drop in accuracy when tested on a new stimulus condition or animal (out of the training set) is reduced with the described method.", "questions": "- Is the enhanced generalization also measurable for the VAE validation metrics ?", "limitations": "The impact of the paper is limited by the writing style, which is not of the best quality.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The method is remarkably simple and could turn out to be very useful to reinforce the generalization capability of a model.\n\nStrengths:\n- The method is simple and seems easy to try in practice. It is appropriate to use that for neuroscience in this context.\n- The method can be applied broadly to pretty much any deep learning model, so the scope of applicability is large\n- The method appears to generalize to new animals or conditions, even with a fairly low number of animals in the training set\n\nWeaknesses:\n- The model is demonstrated on cell type decoding, generalization across animals, and brain area decoding. Yet the method is very general, so the benefits could have been demonstrated on more quantitative metrics. For instance, using the trained model at hand (VAE, etc), one could test if the validation metrics of these models generalize across datasets. \n- Similarly, I see no reason why focusing on single-cell functional activity. The method is likely to be usable as well with many cells or other contexts. It is not clear to me why single-cell data is specifically interesting in this context.\n- Unfortunately, the paper is somewhat hard to understand because of the writing. The English is grammatically correct, but the paragraph structure and choice of words are sometimes hard to follow when reading from top to bottom. In terms of structure, the different datasets are presented one after the other. Then, the experimental settings for all experiments are described. Then the results for all three experiments are presented. It means one needs to constantly switch between the experimental context, which is hard to follow. Regarding the wording, the concepts that are described as \"batch\", \"invariant\", and \"out-of-domain\" are often assumed to be understood by the reader without prior definition or motivation. Even if these terms are common, one needs more explanation to understand what is meant in the context of the paper.", "was_revised": true, "final_justification": "I am increasing my grade because the authors are willing to address my issues regarding the paper's readability.", "rebuttal": "Dear Reviewer,\n\nThank you for your thorough review and the constructive feedback on our manuscript. We appreciate the time and effort you dedicated to our work. We agree that clarity is paramount and have undertaken a significant revision to enhance the manuscript's readability and logical flow, making our core contributions even more accessible.\n\nWe found your specific suggestions invaluable. In response, we have made the following improvements:\n\n* **Clarified Key Terminology**: We have revised the Abstract and Introduction to provide explicit, context-specific definitions for crucial concepts such as \"batch effects,\" \"invariant representations,\" and \"out-of-domain generalization.\"\n\n* **Restructured for Readability**: Following your excellent advice, we have merged the \"Dataset\" and \"Experiment Setup\" sections into a unified \"Benchmark\" section. Each experiment is now presented as a self-contained unit, creating a clearer, more logical narrative for the reader.\n\nWe would also like to offer additional context on two other points you raised: our choice of evaluation metrics and our focus on single-neuron functional activity. We hope this clarification will better highlight the motivation and significance of our work.\n\n***On the Contribution and Focus on Single-Neuron Data:***\nIn computational neuroscience, many models have recently emerged to learn neuron \"identity representations\" from in-vivo, single-neuron activity data. These representations are highly valuable for downstream tasks such as predicting a neuron's cell type or its anatomical location. However, while this direction is promising, a critical challenge often overlooked is that these models exhibit poor generalization across different experimental batches (e.g., different subjects or stimulus sets). Our work is the first to systematically identify, benchmark, and provide an effective solution for this crucial generalization gap. This specific focus is the core of our contribution.\n\n***On the Framework and Evaluation Metrics:***\nTo address this challenge, we proposed a simple, model-agnostic adversarial training strategy. We believe a key strength of our approach is its flexibility; it is an intuitive framework that can be easily incorporated into a wide variety of existing and future model architectures.\n\nGiven this practical goal, we chose downstream classification performance as our primary evaluation metric. This is the established standard for gauging the utility of representations in our field because it directly measures what neuroscientists need: the ability to predict meaningful biological labels. Our extensive experiments, conducted across two different datasets and four distinct model architectures, convincingly demonstrate that our method achieves significant gains in out-of-distribution generalization. This robust validation directly supports our central claim that our framework produces more useful and reliable representations for scientific discovery.\n\nIn summary, our manuscript:\n\n* Identifies and provides a solution for a critical generalization problem within the emerging field of single-neuron representation learning.\n\n* Introduces a simple, effective, and model-agnostic framework with broad applicability.\n\n* Demonstrates robust improvements in out-of-distribution performance across multiple datasets and model architectures, confirming the practical utility of our approach.\n\nWe have substantially revised the manuscript to improve its clarity based on your feedback. We hope that with this additional context, the value of our contributions is clear. We believe our work offers the field a practical and powerful tool for building more generalizable models, and we would be grateful if you would reconsider it."}, {"review_id": "FW5mIq6eg9", "reviewer": "Reviewer_nitk", "summary": "The authors point out that many computational neuroscience models have the problem of poor generalizability across batches and propose a model-agnostic method to solve this problem. The method involves adversarial training, where a representation encoder and a batch discriminator are trained jointly so that batch information is removed from the representation encoder. Then, the authors proceed to evaluate their method with 2 different datasets and 4 different types of representation encoders (including transformer-based models, contrastive learning models, VAE-based models, and supervised models). The proposed adversarial training method can improve cross-stimulus and cross-animal generalizability.", "questions": "1. I am wondering how the batch labels are encoded during training. The paper says the batch label encodes \"experimental metadata (e.g., animal ID, stimulus condition, recording session)\". Is it done via a cross-entropy loss on a classification objective?\n2. Could you provide more details on how you chose the hyperparameters via cross-validation?\n3. Adversarial training is difficult to converge. Do you observe that some representation encoders are easier to work with than others in adversarial training?\n4. Regarding the discriminator, have you tried different sizes/depth/architecture, and do those choices lead to better/worse/similar performance?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n* The paper points out an important problem in computational neuroscience and proposes a simple yet effective method to solve the problem\n* The paper is well-motivated and easy to follow\n* The proposed approach is model-agnostic and seems easy to incorporate into other model training as well\n\nWeaknesses:\n* The paper has limited information on the details of adversarial training", "was_revised": true, "final_justification": "The author proposed a generalizable method for a well-known and challenging problem in neuroscience research. With the additional experiments and clarification of methodology, I believe the paper has become stronger and contributes to the research community.", "rebuttal": "Dear Reviewer,\n\nWe sincerely thank you for your valuable time and insightful feedback on our manuscript. We particularly appreciate the acknowledgment of the paper's significance and originality in addressing the critical problem of generalizability in computational neuroscience.\n\nBelow, we address your specific questions.\n\n**1. On the Encoding of Batch Labels**\n\nWe treat the task of identifying the batch as a multi-class classification problem. The batch discriminator, $D_{\\phi}$, is trained to predict the batch label (e.g., Animal ID or Stimulus ID) from the neuron's embedding $z_i$. The loss function for this task is the standard **cross-entropy loss**. We clarify this mechanism further in the main text of the revised manuscript to ensure this key detail is more accessible.\n\n**2. On Hyperparameter Selection via Cross-Validation**\n\nYou requested more details on the cross-validation process for choosing hyperparameters.\n\nOur goal was to select the optimal adversarial weight, $\\lambda$[cite: 204, 628], to maximize the model's ability to generalize across different experimental batches. This entire process was performed using **only the training dataset**, ensuring the final test set remained completely unseen.\n\nTo accomplish this, we employed a standard **k-fold cross-validation** procedure within the training data. The process is as follows:\n* **Partitioning the Training Set**: If our training set consisted of data from *k* distinct batches (for instance, in the cross-animal experiment, we train on 3 animals, so k=3), we perform a k-fold cross-validation.\n* **Iterating Through Folds**: For each potential value of $\\lambda$ in our grid search, we iterate through *k* folds. In each fold, we train the model on data from *k-1* batches and validate its performance on the single, held-out batch. The performance metric is the accuracy on the primary downstream task (e.g., cell type classification).\n* **Averaging Performance**: After completing all *k* folds, we calculate the **average validation accuracy** for that specific value of $\\lambda$.\n* **Selecting the Best Hyperparameter**: We repeat this for every value of $\\lambda$. The value that yields the **highest average cross-validation accuracy** is chosen as the optimal hyperparameter.\n\nWe update the methods section of our manuscript to reflect this clear and standard procedure.\n\n**3. On the Convergence of Adversarial Training**\n\nYou asked if we observed differences in training stability or convergence difficulty among the different representation encoders.\n\nThis is an excellent and practical question, as training stability can indeed differ across model architectures, and our approach was adapted accordingly.\n\nFor most of the models we tested, including the end-to-end supervised **LOLCAT** and the **VAE-based framework**, convergence with the adversarial loss was generally stable. We were able to train these models end-to-end with both the primary and adversarial losses from the start.\n\nHowever, the **NeuPRINT** model, which learns an implicit representation, required a more tailored approach. We observed that jointly optimizing its delicate primary objective alongside the adversarial objective from scratch was challenging for convergence. To address this, we adopted a **two-stage training strategy** specifically for NeuPRINT:\n1.  **Stage 1: Pre-training.** We first trained the NeuPRINT model solely on its self-supervised objective until it achieved a stable, converged state.\n2.  **Stage 2: Adversarial Fine-tuning.** After the model had converged, we introduced the adversarial loss and fine-tuned the entire network to learn batch-invariant representations.\n\nWe will add these crucial details about our training methodology to the revised manuscript.\n\n**4. On the Discriminator Architecture**\n\nWe thank the reviewer for this excellent suggestion. Prompted by your feedback, we conducted a new set of ablation studies to systematically investigate the impact of the discriminator's architecture on model performance.\n\nWe first increased the depth of the discriminator to three and four hidden layers. We found that this additional complexity had a negligible impact on the final generalization accuracy across our models.\n\nWe then replaced the MLP with a much simpler linear model (a logistic regression classifier). In this configuration, we observed a consistent and notable decrease in performance. This suggests that a non-linear classifier is necessary to effectively capture the complex, subtle artifacts that constitute batch effects.\n\nAdding further complexity yields no additional benefit, while a simpler linear model is insufficient. This analysis validates our architecture as a robust and efficient choice for this task. We add a summary of this new ablation study to the appendix of our revised manuscript.\n\nOnce again, we thank you for your constructive comments. We are confident that by incorporating these clarifications, the final version of our paper will be stronger and more complete."}]

Author rebuttal:

Meta-review: This paper tackles generalization issues in current models of single-neuron activity based on representation learning. This is done by adopting an adversarial training strategy designed to counter 'batch effects' that can cause said methods to overfit. The proposed method is tested on two public datasets.

The reviewers appreciated the flexibility, simplicity and wide applicability of the method, which is shown to be convincingly effective. Some of the weaknesses identified were addressed by changes in the text (notably, claims that the method could address cross-lab systematic variability in the data), and some with additional analysis (scaling with the number of available batch-level attributes in the data).

Overall, the reviews indicate a paper that while not particularly innovative from a technical standpoint manages to identify and make progress on a meaningful problem in the field, and will therefore be of interest to the NeurIPS community.
Decision: Accept (poster)