Title: TRACE: Contrastive learning for multi-trial time series data in neuroscience
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neural cell types

Dimensionality reduction

Contrastive learning

Two-photon recordings

Time series data
Abstract: Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing applications for neural time series rely on generic data augmentations and do not exploit the multi-trial data structure inherent in many neural datasets. Here we present TRACE, a new contrastive learning framework that averages across different subsets of trials to generate positive pairs. TRACE allows to directly learn a two-dimensional embedding, combining ideas from contrastive learning and neighbor embeddings. We show that TRACE outperforms other methods, resolving fine response differences in simulated data. Further, using in vivo recordings, we show that the representations learned by TRACE capture both biologically relevant continuous variation, cell-type-related cluster structure, and can assist data quality control.


Peer reviews:
comments: SUMMARY:
The authors propose a novel method, TRACE, for projecting neurons in a 2D space based on their multi-trial activity using contrastive learning. Using cauchy kernels they force the model to project to a 2D space and not in a hyper-sphere as it is usually done. As every contractive method positive and negative pairs are needed. The positive pairs are the same neuron averaged across different trials, while the negative pairs are different neurons across different trials. The method is evaluated on simulated data and 2photon imaging neural data from mice. In their results section, they show that their TRACE is able to cluster neurons based on their activity patterns better than other methods such as CEED, TS2VEC, tSNE, and UMAP.

STRENGTHS AND WEAKNESSES:
Strengths:
- The method addresses a real problem in neuroscience, which is the need to visualize and interpret high-dimensional neural data.
- If training the MLP model and choosing the right hyper-parameters is straightforward, the method could be widely applicable to many datasets.
- Using this method as an outlier / artifact detection could be potential useful.

Weaknesses:
- A potential limitation is that neurons with highly variable or even contradictory responses across trials (e.g., responding positively in some trials and negatively in others) could result in positive pairs that are poorly correlated or even misleading. This raises the question of whether the subset-averaging strategy could inadvertently wash out meaningful variability or create noisy positive samples.

QUESTIONS:
Questions: 
- I found subsection 3.2 a bit unclear. Specifically, how many positive pairs are created per neuron during training? Are the subsets fixed or dynamically resampled?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-25

comments: SUMMARY:
The paper introduces TRACE, a contrastive learning framework for visualizing dependencies among multi-variate (e.g., neurons) time-series data over multiple-trials. TRACE generates positive pairs by averaging disjoint subsets of trials and directly learns a 2D embedding using a t-SNE-inspired Cauchy kernel.

The authors compare TRACE to baselines on both simulated and large-scale in vivo neural datasets. For a two-photon imaging dataset of retinal ganglion cell axon endings, TRACE's embeddings capture biologically relevant continuous variations, cell-type-related cluster structures, and can assist in identifying recording artifacts.

STRENGTHS AND WEAKNESSES:
The problem setting, method and experiments of the submitted manuscript are well written and very clear. 
The authors provide code and detailed results for the considered baselines in the appendix.

Weaknesses:

 - Quality: the authors limit their analysis to a single neuroimaging dataset despite a plethora of public multi-trail, multi-variate neuroimaging datasets.
 - Originality: the authors develop TRACE around the time-tested idea of averaging responses across multiple repetitions to improve the SNR, which is a standard technique in neuroscience. They then use boosting to sample positive pairs and standard ML techniques like a contrastive loss with a Cauchy kernel.
 - Significance: TRACE requires multiple repetitions and the assumption of independent, additive noise (to improve the SNR via averaging repetitions). This limits TRACE to specific applications like neuro imaging with likely no impact on other ML application domains.

QUESTIONS:
None

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new model to project neural activity into low(2)-dimensional space for visualization. The proposed method extends on existing latent space models by exploiting contrastive learning across trial repeats in time series data to capture neural functional similarity. The authors tested their method in a retinal ganglion cell dataset and simulated data. The results and comparisons to alternative existing method showed that the method can recover known cell types.\n\nSTRENGTHS AND WEAKNESSES:\nThe work is clearly presented and provides qualitative evidence that the proposed method outperforms commonly used non-linear latent space methods on a calcium imaging dataset. The authors further experiment to understand the model limitations in trial repeats. \n\nWhile the main application of the method is visualization, presenting a more quantitative description of the model performance is needed. Moreover, the method is tested in a single neural dataset where neural types are known and have been previously identified. It would be relevant to extend the experiments to other and more complex datasets to evaluate the relevance and generalization of the proposed method, given that alternative methods can also discriminate the studied cell types.\n\nQUESTIONS:\nHow does the model perform when using different loss functions? Can the model generalize to time-series data without trial structure? Would the method extend to spiking neural data? How?\n\nLIMITATIONS:\nThe authors list the method limitations in terms of increase computational demands and requirement for trial repeats. Still, a larger limitation is that the method assumes known and single stimulus presentation, when in practice neuroscience experiments present multiple stimuli or external and internal conditions at once. Additionally, the dimensionality of the latent space seems to be limited to a 2D space, regardless of the inherent variability in the data.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}