Self supervised learning for in vivo localization of microelectrode arrays using raw local field potential
Authors: Tianxiao He
Malhar Patel
Chenyi Li
Anna Maslarova
Mihály Vöröslakos
Nalini Ramanathan
Wei-Lun Hung
Gyorgy Buzsaki
Erdem Varol
Venue: NeurIPS 2025 poster ()
Keywords: neural decoding
electrophysiology
contrastive learning
neuroscience
Primary area: neuroscience_and_cognitive_science

Abstract: Recent advances in large-scale neural recordings have enabled accurate decoding of behavior and cognitive states, yet decoding anatomical regions remains underexplored, despite being crucial for consistent targeting in multiday recordings and effective deep brain stimulation. Current approaches typically rely on external anatomical information, from atlas-based planning to post hoc histology, which are limited in precision, longitudinal applicability, and real-time feedback. In this work, we develop a self-supervised learning framework, Lfp2vec, to infer anatomical regions directly from the neural signal in vivo. We adapt an audio-pretrained transformer model by continuing self-supervised training on a large corpus of unlabeled local-field-potential (LFP) data, then fine-tuning for anatomical region decoding. Ablations show that combining out-of-domain initialization with in-domain self-supervision outperforms training from scratch. We demonstrate that our method  achieves strong zero-shot generalization across different labs and probe geometries, and outperforming state-of-the-art self-supervised models on electrophysiology data. The learned embeddings form anatomically coherent clusters and transfer effectively to downstream tasks like disease classification with minimal fine-tuning. Altogether, our approach enables zero-shot prediction of brain regions in novel subjects, demonstrates that LFP signals encode rich anatomical information, and establishes self-supervised learning on raw LFP as a foundation to learn representations that can be tuned for diverse neural decoding tasks. Code to reproduce our results is found in the github repository at https://github.com/tianxiao18/lfp2vec.

Self supervised learning for in vivo localization of microelectrode arrays using raw local field potential
Tianxiao He
Malhar Patel
Chenyi Li' Wei-Lun Hung!
Anna Maslarova
Mihály Vöröslakos? Erdem Varol
Nalini Ramanathan
György Buzsáki²
Department of Computer Science, New York University Neuroscience Institute, Grossman School of Medicine, New York University
Abstract
Recent advances in large-scale neural recordings have enabled accurate decoding of behavior and cogmitive states, yet decoding anatomical regions remains underex- plored, despite being crucial for consistent targeting multiday recordings and effective deep brain stimulation. Current approaches typically rely on external anatomical information, from atlas-based planning to post hoc histology, which are limited in precision, longitudinal applicability, and real-time feedback. In this work, we develop self-supervised learning framework. Lfp2vec, infer anatomical regions directly from the neural signal in vivo. We adapt audio- pretrained transformer model by continuing self-supervised training on large corpus of unlabeled bocal-fieldpooentte
(LFP) data, then fine-tuning for anatomi- cal region decoding. Ablations show that combining out-of-domain initialization with in-domain self-supervision outperforms training from scratch. We demon- strate that our method achieves strong zero-shot generalization across different labs and probe geometries, and outperforms state-oftthe-ar self-supervised models on electrophysiology data. The learned embeddings form anatomically coherent clusters and transfer effectively to downstream tasks like disease classification with minimal fine-tuning. Altogether, our approach enables zero-shot prediction of brain regions in novel subjects, demonstrates that LFP signals encode rich anatomical information, and establishes self-supervised learning on raw LFP as foundation to learn representations that can be tuned for diverse neural decod- ing tasks. Code to reproduce our results is found in the github repository at pettps//githubb www.coo///oo... anxia anxxa018/ff22eee
1 Introduction
The ability to precisely localize high-density multielectrode recording sites in-vivo is a critical first step for systems and cognitive neuroscience research. Without accurate knowledge of which brain layer or sub-region each channel captures, subsequent analysis of cell-type specificity [1], circuit motifs [2], or disease biomarkers 3 become unreliable. In clinical settings such as deep brain stimulation at focal sites, the precision of electrode placement can significantly impact therapeutic outcomes and cognitive changes post-operation
4 Today, most laboratories plan probe trajectories pre-operatively using brain atlas coordinates [5] or MRI scans (6), and validate them post-operatively by follow-up scans or ex-vivo histology 77]. However, these approaches lack in vivo knowledge of probe locations, and they labor intensive, arrorpronee (limited by imaging resolution) and unsuitable for chronic recordings, flexible probes or clinical settings where tissue cannot be sacrificed.
denotes equal contributions.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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As multishank arrays scale to over thousand channels (8) both the data collection throughput and the need for accurate localization performance increases.
Recent work has attempted to address this using spike-based localization with action potential
waveforms [ or firing rates T0 [T]. However, these approaches require potentially inefficient and
error-prone spike sorting [2] and ignore the rich structure in the local field potential (LFP) band [I3]
14]. In parallel, self-supervised learning has been applied to unlabeled time series and various
downstream tasks in audio [1]] and neural signals (9] 16 I7 Most existing work decodes behavioral states [8]. discrete events [9), or speech (20) from LFP, rather than anatomical mapping. General purpose time series SSL models are typically designed with temporal prediction objectives [21], but they ignore anatomical priors and laminar continuity that characterize linear probes, which limits their use for brain region localization [22). To our knowledge, no existing study has applied self- sufe-supere learning to raw LFP signals, nor tested whether such models can generalize across species, probe types, and labs for scalable and reproducible brain region localization.
To this end, we introduce Líp2vec, sule-vuper framework for anatomical localization from raw LFP recordings. Our approach adapts from wav2vec 2.0 [21], a foundation model pre-trained on audio signals, to the domain of electrophysiology. Using contrastive learning with masked prediction, we train on raw voltage traces without labels or probe geometry. A key aspect of our approach is transfer learning across modalities: instead of training from scratch, we initialize the model using wav2vec 2.0 and continue self supervueee training on large-scale LFP data. We hypothesize that representations learned from general purpose time series data are partially transferable the neural domain. As our ablation studies will show (Fig. [ this cross-domain initialization provides substantial performance boost over random initialization, highlighting the value of out-of-domain foundation models for biomedical applications.
We then evaluate the output embeddings on recordings from four labs: Allen Institute (23) Interna- tional Brain Lab (24), Neuronexus SiNAPs (8), and Neuropixel-NHP with macaque reaching dataset. We find that the embeddings cluster by anatomical subregion across sessions and animals, and can be decoded with high accuracy using multilayer perceptron. Beyond localization, these embeddings are also transferable to downstream tasks such as Alzheimer's disease classification from hippocampal dynamics. Together, our results show that neural population activity encodes brain region identity, and highlight the potential of self-supervised model handle noisy and non-stationary neural time series data.
The contributions of this work include:
A novel application of self-supervised learning to LFP signals, extracting embeddings for multiple downstream tasks.
Empirical evidence that raw LFP signals encode rich anatomical and functional information, with SSL extracted features outperforming handcrafted features.
A robust automatic probe localizer that achieves high-accuracy zero-shot prediction on held-out subjects and sessions, and generalizes across probe geometries, labs, and species (rodent to non-human primate).
2 Related Work
2.1 Self Supervised Learning in Extracellular Recordings
Self-supervised learning (SSL) has become a powerful tool in both machine learning and neural data science, enabling generalization across a wide range of downstream tasks. Models such as wav2vec 2.0 21 timesFM [15] learn latent structure from time series via masked prediction, but they are not tailored to extracellular recordings and overlook domain-specinf challenges such as biological noise, sonsstation,rity and diverse sampling rates. In neural data science, SSL has shown promise primarily in the spike domain. Methods such as POYO (9. NEMO [II]. NDT2 [17]. and NEDS [16] use contrastive or masked objectives to learn embeddings from spiking activity. However, these methods typically rely on firing rates, spike times, or waveform features that are sensitive to spike sorting quality (25), and not extend to local field potentials which are more broadly accessible. Models like BrainBERT 20) Brant [26) and related intracranial models [27] use broadband signals to decode

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speech and cognitive states, but are not designed for anatomical localization. While effective, they do not incorporate anatomical priors or evaluate spatial generalization.
2.2 Brain Region Decoding
Traditional brain region decoding often relies
clectrophysiological features biomarkers. For example, sharp wave ripples help identify CA1 sublayers in the hippocampus
28). gamma coherence distinguishes hippocampal subregions (29), and firing statisties (e.g., bursti- ness, firing rates) differentiate cortical layers in V1 (3). However, these approaches are typically heuristic, region-specific, and not learned from data. More recent methods apply deep learning firing rates and spike timing to infer anatomical identity [31], but still rely on manual feature design and dense region labels, limiting scalability. Self-supervised models like NEMO [I]] learn neuron embeddings for region decoding at single-unit level, yet they ignore population-leve dynamics that captures brain region identity. Furthermore, lab and do not evaluate generalization across labs and species, despite known variability in surgical procedures, probe designs, and behavioral paradigms [32 [24]. To address these limitations, we apply self-supervised learning to raw, multi-channel LFP recordings to learn anatomically meaningful representations that generalize across labs and recording setups.
models are trained and evaluated within a single
Datasets
For brain region decoding, we apply two publicly available datasets and two private datasets across different species, recording probes, and experimental setup.
IBL Reproducible Electrophysiology Dataset. This dataset consists of Neuropixels recordings from mice performing visual decision-making task (24), collected by International Brain Lab (IBL.). Ground-trus anatomical labels were obtained through post hoc histology by registering fluorescent probe tracks to the Allen Common Coordinate Framework. Labeled brain regions include secondary visual cortex (VISa), hippocampal subregions (CA1, CA3, dentate gyrus), and thalamic nuclei (LP, PO).
Allen Visual Coding Dataset. This dataset consists of Neuropixels recordings from mice perform- ing a visual stimulus presentation task [23]. Recordings were sampled at 1250 Hz and span regions across the visual cortex, hippocampus (CA1, CA2, CA3, dentate gyrus), and thalamus. Ground-truth labels were obtained via post hoc histological analysis.
Neuronexus Multi-Shank Dataset. This dataset consists of 1024-channel Multi-shank Simultane- ous Neural Active Pixel Sensor (SiNAPS) probe recordings from mice during spontaneous activity 8. It includes five transgenic mice modeling Alzheimer's s Disease (APP/PSEN1) and two normal controls. Recordings target hippocampal subregions including CA1, CA2, CA3, dentate gyrus, and surrounding cortex. The SiNAPs probe has 8 shanks with 128 channels per shank, enabling denser spatial coverage of the hippocampus than standard Neuropixels probes. Anatomical labels were assigned by expert annotation using electrophysiological landmarks and manual registration to brain atlas.
Macaque Sequential Reaching Dataset. This dataset consists of Neuropixels-NHE recordings from macaque monkey performing center-out and sequential reaching tasks. Recordings target motor-related regions including the basal ganglia, supplementary motor area (SMA), and primary motor cortex (M1). Ground-truth region labels were obtained by registering a 3D-printed electrode grid to MRI data and the recording chamber.
4 Methods
4.1
Preprocessing
To reduce non-physiological artifacts, we preprocess raw LFP recordings using the International Brain Lab (IBL) destriping pipeline [33]. Signals are zero-phase high-pass filtered at 2 Hz to remove
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Figure 1: Overview of the Lfp2vec pipeline. a) Example multi-species mocal-fill-pootntte (LFP) segments: SiNAPs (mouse), Neuropixels-IBI. (mouse), Neuropixels-Allen (mouse), and Neuropixels- NHP (Macaque), segmented into short spatiotemporal windows. b) Each window is encoded using 1-D convolutional encoder and a Transformer, trained with musked-predicttonn contrastive objective to learn spatially coherent representations. The pretrained model is then fine-tuned for downstream tasks. <) Learned embeddings after fine-tuning: rodent hippocampus (top), macaque motor regions (middle), and healthy or experimental labels. d) Embeddings drive lightweight decoders for downstream tasks: brain- region localization (top: rodent, middle: macaque) and dession-leve disease classification (bottom; Alzheimer's model vs. healthy). Each dot shows predicted region probabilities pie charts per trial, Columns correspond trials, channels, and colors represent brain regions disease labels in c).
lisease-mode recordings (bottom). Colors denote ground-truth anatomical
slow drifts, bad channels are detected using amplitude and spectral criteria and interpolated, and common-mode noise is removed by subtracting the per-sample median across valid channels. The cleaned recordings are segmented into 3-second trials and normalized (Fig. [Tb,, which standardizes inputs across devices (c.g., Neuropixels vs. Neuronexus) and species (mouse vs. macaque). In practice, model performance is sensitive to preprocessing and signal quality; insufficient artifact removal or quality control can substantially degrade representation quality and downstream accuracy.
4.2 Model Architecture
We adapt the wav2vec 2.0 framework 21 to LFP recordings, shown in Fig. Dp. Rather than handcrafted spectral features (e.g., band power or spectrograms), we learn tokens directly from raw LFP by segmenting the continuous signal and encoding each window with a 7-layer 1D CNN for each channel and trial. The resulting tokens are masked and passed to a 12-layer Transformer context network, which predicts the correct target among distractors at masked time steps using surrounding context. For downstream decoding, we mean-pool Transformer outputs across time and apply two-layer MLP classifier to predict brain-region discase labels (Fig. [d).

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4.3 Objectives
We train Lfp2vec in two stages, adapting from a pretrained self supervised model to LFP recordings, shown in F Tb. The objective closely follows the wav2vec 2.0 contrastive learning framework.
Self-supervised pretraining. Given an LFP segment x E IR""" we compute tokens 2 = E(x) and discretize them via Gumbel-softmax quantization q ((2) with multiple codebooks [2]] We
randomly mask time steps M in 2 feed the masked latents & into the Transformer, and obtain contextual embeddings c (()). For each masked step m E M,, apply a contrastive objective that pulls the context token Cm (Transformer output at m) toward the target token 9m (quantized
vector m), while pushing it away from distractors {qk} kemm sampled from other time steps in the same batch. With temperature T, the loss is:
log exp(sim((mm qm TT)
LSST. = E
_kek(m) exp(sim sim gb)/T)
MEM
where X(() denotes the set of distractors unioned with the true quantized vector gm. To encourage diverse codebook usage, we additionally apply a diversity loss Laiv weighted by X. The overall pretraining objective is LSSL ALaiv 21
Supervised fine-tuning. We attach a classification head h(-) to the pooled context outputs č POOL(c), and fine-tune all parameters end-to-end on anatomical region labels {1: Denoting model outputs as i = h(C,), we minimize cross-entropy.Corr = - -; V/ log Dt. allowing the network to refine its representations for accurate region decoding.
4.4 Post processing
To improve decoding performance, we incorporate temporal and spatial priors via lightweight post-processing pipeline (d), We first smooth model's per-timepoint predictions by averaging class probabilities over temporal window W; with class prior and assign the final label as arg maxe [Wa Erew, Te-, which suppresses transient fluctuations and abrupt label changes.
We then enforce spatial continuity by assigning each channel the majority label of its 5 nearest neighbors, smoothing out isolated misclassifications. This step leverages the strong anatomical prior that adjacent channels on a linear probe are highly likely to reside in the same or contiguous brain regions. As shown in Figure 2d, these two steps improve channel-wise location prediction without retraining or requiring anatomical atlases, and run in milliseconds per session.
Experiments
5.1 Baselines
For baseline comparisons, we implemented classical spectrogram model and two self-supervised methods, each paired with two-layer MIPP decoder (128 hidden units, ReLU) trained using Adam optimizers. Spectrograms were computed using Short Time Fourier Transform, producing 500 frequency bins x 16 time bins per trial. For SimCLR (34 CNN encoder with MLP projectii head was trained on 3-second raw LFP segments (1.25 kHz) using InfoNCE loss (temperature = 0.606) with temporal masking. BrainBERT (35) uses transformer encoder pretrained on spectrograms via masked token prediction. After pretraining, we fine-tune only the MLP head while freezing the encoder. All models share the same decoding architecture, isolating differences in encoder representation quality rather than decoder design.
5.2 Evaluation
To avoid implicitly exploiting spatial or temporal correlations between training and test data, we adopt an across-session and lcross-lab evaluation protocol.
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Across-session evaluation. We split each lab's recordings by session, reserving 15% sessions for testing, 30% for validation. Models are trained on all chamnels and trials independently in remaining sessions, and evaluated on the held-out session. We report both balanced accuracy and macro F1 to account for class imbalance across five brain regions, and assess embedding quality via PCA of encoder representation.
Across-lab evaluation. To test cross-lab generalization, we train on all sessions from one lab and evaluate zero-shot on another lab with different probes and tasks. We also include one-shot transfer condition by adding single session from the target lab training. This setup tests each model's ability to extract anatomy-relevant features without relying on probe geometry or task structure.
Results
6.1 Brain Region Decoding Across Sessions
We evaluate all models on cross-session region decoding in three large-scale LFP datasets (Neu- ronexus, Allen, and IBL). As shown in Figure Lfp2vec achieves the highest balanced accuracy and macro FI, outperforming both the spectrogram baseline and SSL methods (SimCLR, BrainBERT), with the largest improvements on Allen and Neuronexus. Fewer performance gains are observed on IBL, likely due to its simpler classification task (fewer region classes). On the Allen dataset, confusion matrices (Figure 2b) show that L.p2vec achieves the fewest errors across all classes, including in underrepresented subregions such as CA2 and CA3. All models perform best on CAI and cortex, likely due to their distinctive signatures and class prevalence.
PCA projections of latent embeddings (Figure 2p) suggest that Lfp2vee produces more compact and separable clusters by region. To test whether this structure reflects neuroanatomy rather than probe- identity or session-specific artifacts, we quantitatively evaluated cluster quality using the Silhouette score (1 = perfect separation, 0= no structure) and linear probing accuracy, as shown in Fig. 2h, right. Lfp2vec achieved markedly higher Silhouette score for region clustering (0.576#0.00 026) than the spectrogram (-0.054+0.015)) SimCLR 1-0.062+0.050) and BrainBERT (0. (0.14640.025 0266 baselines, while showing no meaningful clustering by session (-0.053+0.009) or probe identity (-0.266+0.000 006). Linear probing results mirrored this trend: although embeddings encode minor session and probe information, anatomical region dominates, with Lfp2vec enabling far more accurate region decoding +..2010.004) than baselines. Together, these findings demonstrate that Lfp2vec learns more anatomically aligned representations.
Figure 2 shows the channel-wise prediction map compared to the ground truth. Although all models capture the coarse anatomical layout, Lfp2vec's 's predictions more closely adhere to the known laminar organization of the hippocampus. For instance, in the example shown, it clearly delineates the CAI 1- DG boundary, distinction that is less precise in the baseline models. A zoomed view (Figure Pi right) highlights how our post-processing steps further refine these boundaries to improve localization accuracy.
6.2 Brain Region Decoding Across Labs
In addition to within-lab evaluation, we assess the robustness of Lfp2vec under cross-lab transfer. We initialize Lfp2vec with pretrained weights, fine tune the model one lab, and evaluate its zero-shot prediction accuracy on another lab with different tasks and probe geometries (Figure 2b.). Diagonal entries represent within-lab performance, with chance-level accuracy shown in parentheses. We observe strong zero-shot transfer between the IBL. and Allen datasets, likely due to their shared use of Neuropixels probes and similar visual task structure (Figure 2). left). In contrast, transfer from the Neuronexus dataset to others is weaker, possibly due to its distinct probe design and spontancous recordings. When a single session from the target lab included (one-shot transfer), accuracy improves substantially, often matching within-lab performance (Figure 2b, right), suggesting that minimal data can suffice to adapt Lfp2vec new labs.
6.3 Brain Region Decoding Across Species
To test cross-species generalization, we apply L.f22vee to the macaque Neuropixels-NHP dataset (Figure 3R) using the same across-session protocol. As shown in Figure Tp. Lfp2vec outperforms

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Model performance comparison in region decoding across rodent datasets. a) Balanced test accuracy and macro-F1 (left) for brain-region decoding on three mouse LFP datasets (Neuronexus, Allen, IBL). Silhouette score and linear probing accuracy (right) quantifies how well embeddings cluster by brain region, session, and probe identity in Allen sessions. b) Confusion matrices showing the brain region classification performance across all models in Allen sessions. c) PCA projections of channel embeddings for Allen sessions, colored by distinct brain regions, showing clusters by brain regions. d) Channel-wise predicted regions Neuronexus probe compared to ground truth. Each dot represents region probability pie chart (right top), with temporal smoothing (right middle) and spatial smoothing (right bottom) improving prediction. e) Cross lab generalization matrix for zero-shot (middle) and one-shot (right) performance across three mice datasets (left), here high off- diagonal values indicate good generalization performance from one lab another.
Figure 2:

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all baselines (spectrogram, SimCLR, BrainBERT), achieving the highest test accuracy and macro Fl without any species-specing adaptation. The confusion matrices (Figure Be) reveal that Lip2vec has the fewest misclassificationn especially between closely related regions such as SMA and M1. Latent space projections (FigureB3) 3)) further show that Lfp2vec produces more compact and separable region-specific clusters, suggesting that its representations preserve fine anatomical distinctions. These results highlight Lfp2vec's 's ability to extract generalizable, anatomically meaningful features from LFP signals across species and experimental settings.
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Figure 3: Lfp2vec representations transfer across laboratories, probe geometries, and species. a) Cross-species generalization: Lfp2vec outperforms spectrograms, SimCLR, and BrainBERT in balanced accuracy and macro-F1 for classifying SMA, M1, and BG. b) Confusion matrices show Lfp2vec achieves the highest accuracy and clearest separation across regions (BG, SMA, M1). c) PCA plots reveal Lfp2vee embeddings form distinct, region- speciffc clusters, generalizing beyond rodent data.
6.4 Disease Prediction
Besides the brain region decoding task, evaluate Lfp2vee downstream task of Alzheimer's disease (AD) classification using LFP recordings from App x Psen1 transgenic mice []_ A lightweight classifier fine-tuned on top of pretrained Lfp2vec embeddings to distinguish AD mice from healthy controls. As shown in Figure 4h. Lfp2vec outperforms SimCLR and BrainBERT in both accuracy and Fl score. PCA projections of Líp2vec embeddings (Figure Ap) show clear separation between diseased and healthy samples, suggesting the model captures disease-relevan neural features. To further localize abnormalities and analyze sources of error, we compute channel-level disease probabilities across trials (visualized as pie charts in Figure 4b-d), d), and aggregate them into region- level abnormality scores shown in the bar plots below. AD mice exhibit lower abnormality level in regions such as CA3 and DG, while healthy controls show consistently low abnormality scores across all regions. These findings highlight L.p2vec's 's ability to detect and localize pathological neural signatures without supervision.
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Figure 4: Disease Prediction and Abnormality Study by Brain Regions. a) Classification per- formance (accuracy and Fl score) on distinguishing Alzheimer's s disease (AD) model mice (App Psenl) from healthy controls using different sule-supe models. Lfp2vee consistently outper- forms SimCLR and Br.. b) PCA projection learned Lfp2vec embeddings shows distinct clustering between diseased and healthy animals. c-d) Channel-wis predictions and region-level abnormality scores for AD model mice (c) and healthy controls (d). Each dot represents a channel's prediction across trials. Bar plots below summarize region-wise abnormality scores, showing which anatomical regions have higher deviation from normal activity. CA3 and DG show the least abnormal signals in AD mice, while abnormality scores in healthy controls remain low across all regions.
6.5
Ablation Study
To isolate the benefits of our learning strat- egy, we conducted an ablation study (Fig. 5) evaluating two key factors: the amount self-supervised pre-training on LFP data, and the choice of model initialization. We compared models initialized with random weights against models initialized with weights from the audio-pretrained wav2vec 2.0 model. Across all datasets, continued self-supervised pre-training on LFP data consistently improves downstream decod- ing performance, with accuracy increasing with the number of unlabeled trials. More importantly, audio-initialized models sig- nificantly outperformed randomly initial- ized ones. For instance, on the Neuronexus dataset, the audio-intiaalzed model with only a small amount of LFP pre-training (6k trials) already matches the performance of
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Figure 5: Ablation Study. Brain region decoding accu- racy across three datasets (Neuronexus, Allen, and IBI.) using different number of unlabeled pretraining trials for random session. Solid lines show accuracy from models trained with self-supervised learning, initial- ized either randomly (gray) or with pretrained weights (blue). Pretraining consistently boosts the decoding per- formance and improves with more trials, demonstrating the benefit of combining SSL with pretrained models.
initialized model trained on over 400k trials. This demonstrates that features learned from audio provide strong inductive bias for modeling neural signals. The best per- formance is consistently achieved by combining both strategies: initializing from an audio foundation model and continuing self-supervised learning large corpus of in-domain LFP data.

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7 Discussion
In this work, we presented Lfp2vec, a self-supervised model that learns anatomy-aware represen- tations directly from raw local field potential (LFP) signals. Across three mouse datasets spanning probe geometries and experimental paradigms, Lfp2vec outperforms traditional features such as spectrograms and previous self-supervised models (SimCLR, BrainBERT) on hippocampal subregion decoding under across-session and across-lab evaluation, suggesting the representations capture fine- grained anatomical structure rather than session correlations. Lightweight post-processing via temporal aggregation and spatial smoothing further improves channel-level localization. We additionally observe strong cross-domain transfer: Lfp2vec generalizes across laboratories and probe types (Neuropixels to Neuronexus), and extends to non-rodent setting with improved decoding accuracy macaque motor cortex recordings without redesigning the architecture. Finally, demonstrate that Lfp2vec embeddings support a downstream Alzheimer's 's disease classification task, indicating that anatomically grounded representations may also capture functionally relevant biomarkers.
Despite these advances, several limitations remain. Our current study focuses on cortical and hippocampal areas due to data availability. Future work will incorporate datasets with well-labeled deep structures to test whether performance and embedding consistency persist in more heterogeneous regions. Performance is also sensitive to preprocessing (IBI. destriping), suggesting that end-to-end artifact suppression adaptive filtering could further improve robustness. Disease classification results, while promising, are based on limited mouse samples and require larger studies to assess clinical relevance in humans. Clinical translation would additionally require multi-site validation, cross-species studies, and comparison with established biomarkers. Finally, our evaluation assumes session-level splits; streaming or real-time use will likely require online adaptation.
Looking forward, we see several potential directions. Incorporating explicit spatial priors into the SSL loss could further regularize embeddings and improve cross-probe generalization. Multi-modal fusion with spiking data or imaging modalities may generate richer, interpretable representations for both neuroscience and clinical applications. From a practical standpoint, the ability to perform zero-shot localization in vivo opens the door to adaptive electrode placement and real-time feedback in closed-loop neurophysiology experiments. These methods could also be applied in chronic or clinical settings, such as locating physiological events preemptively (e.g. predict which electrodes the seizure/sharp wave ripple occur), can do early discharges during closed loop stimulation.
8 Broader Impact
Our framework for self-supervised LFP representation learning has the potential to reduce reliance on labor-intensive histology, enabling real-time electrode localization. It could support clinical applications such as adaptive deep brain stimulation and closed-loop brain-computer interfaces with targeted ultrasound neuromodulation. In safety-critical settings, incorrect localization could lead to inappropriate stimulation targets, so clinical deployment should include calibrated uncertainty estimates and human verification. For the ML community, Lfp2vee shows that transformer- based time series foundation models can be successfully adapted to nonstationary, noisy neural recordings, setting a new benchmark for self-supervised learning on biomedical signals. By releasing our code and pretrained weights, we aim to spur further work on domain adaptation, spatiotemporal regularization, and ethical deployment of ML methods for neural data.
Acknowledgements
We would like to thank Saurabh Vyas for generously making their Macaque Neuropixels-NHP recordings data available. We also acknowledge Corticale Srl. for SiNAPS probes and the Radiens software for data acquisition and processing that made this research possible. This work was supported by NIH grant IR00MN128772 and NIH grant IROINS113782-01A.
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Table 1: Author contributions.
TH
MP
CL
MV
AM
NR
WH
GB
EV
Conceptualization
Data Collection
Data analysis
Code Development
Writing
Editing
Funding acquisition
References
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Supplementary Material
A Brain Region Decoding Results
In addition to the Allen dataset discussed in the main text, we also present extended decoding results for the Neuronexus and IBL datasets (Figure 6). The confusion matrices show how each model performs across different brain regions. Lfp2vec consistently shows more focused diagonal patterns, indicating better reliably identified by all models, subfields such CA2 and CA3 remain harder distinguish. This is likely due to the sparsity of region labels and their lower biological separability. Notably, Lip2vec maintains strong performance across all datasets. In the IBL dataset, all models perform well, possibly because of sparser labeling and the absence of more challenging classes. These findings support Lfp2vec's ability generalize across recording platforms with varied probe geometries and signal characteristics.
accuracy across all datasets. While regions like the cortex and CA1
B Baseline Settings
SimCLR We implement SimCLR 334 as a baseline for contrastive self-supervised learning. The input to SimCLR is a 2D spectrogram derived from the raw LFP signals. For data augmentation, we apply temporal masking, by randomly masking out segments along the time axis to encourage temporal invariance. The encoder architecture is a convolutional neural network (CNN) followed by projection head composed of two linear layers with ReLU activation. During pretraining, we use the NT Xentent loss (normalized temperature-scaled cross-entropy loss) to maximize agreement
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Neuronoxus
3000
87
400
445
380
344
4006
3-
342
231
316
225
360
28
8
KEC
88
7P9
CAL
0G
CHL
de
DUCE
Allon
323
3- 127
23
8-
12
3-
13
32
23
23
1367
1154
14
712
170
19286
CAL
Cete
CAL
CA
ob
400
PMN
122
-1000
271
1130
304
-30000
8-
3-
8-
8-
23-
122%
240
0.00
4000
4111
Protitec
Figure 6: Decoding performance comparisons across models and datasets. Here we compare confusion matrix for anatomical region classification across datasets (Neuronexus, Allen, IBL) and models (spectrogram, SimCLR, BrainBERT, Lfp2vec). Each matrix shows predicted versus true region labels. Across all datasets, L.f22vec shows more concentrated diagonal patterns. The cortex and CA1 regions are consistently well-identified across models, while hippocampal subfields such as CA2 and CA3 remain more challenging. Lfp2vec generalizes better than baselines, maintaining stable performance across datasets with diverse probe geometries and recording conditions.
between positive pairs generated from the same signal. After pretraining, we discard the projection head and fine-tune an MLP classifier on top of the encoder for brain region decoding.
BrainBERT We adopt BrainBERT following the implementation in 33 which adapts transformer architecture to 2D spectrograms from electrophysiology signal. The model is trained from scratch on our dataset using masked token prediction as the pretraining task. After pretraining, the transformer encoder is frozen, and we train an MLP classification head for downstream decoding. The MLP consists of two fully connected layers with ReLU activation and dropout. We use Cross Entropy Loss for classification and perform Bayesian optimization over the learning rate, dropout rate, and hidden size of the MLP to select hyperparameters. This two-stage training mirrors the original BrainBERT protocol while allowing adaptation to our LFP data.
C Hyperparameters Settings
Training Schedule. We use separate training schedules for pretraining and fine-tuning in Lfp2vec. During self-supervised pretraining. the encoder is optimized using the AdamW optimizer with a learning rate of lc-5, batch size of 32, and trained for 50 epochs. For fine-tuning, we use learning rate of 3c-5, keep the batch size at 32, and train for 10 epochs. We apply gradient accumulation over 4 steps to simulate larger effective batch size, and use linear warmup schedule over 10% of the total training steps. This specific set of hyperparameters are suitable for Allen dataset, and may require further hyperparameter tuning for novel datasets.
Model Architecture. The model architecture consists of four main components: a convolutional encoder, a product quantizer, a Transformer module, and an MLP classifier. The encoder is a 7-layer ID convolutional stack with kernel sizes of (10, 3, 3, 3, 3, 2, 2) and strides of (5, 2, 2, 2, 2, 2, 2), each followed by GELU activation and LayerNorm.
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Table Hyperparameter Settings for Lfp2vec
Stage
Hyperparameter
Value(s)
Description
Pretraining
Learning Rate
x 10-5
Step size for encoder during self-supervised learning
Batch Size
32
Number of samples per training batch
Epochs
50
Number of training iterations
Optimizer
AdamW
Optimization algorithm
Fine-tuning
Learning Rate
x
10-5
Step size for encoder during fine tuning
Batch Size
32
Number of samples per training batch Number of fine-tuning iterations Optimization algorithm
Epochs
10
Optimizer
AdamW
Gradient Accumulation
Batches per optimizer step
Warmup Ratio
0.1
Fraction of steps for learning rate warmup
This produces 512-dimensional latent representation at each time step. The quantizer discretizes these features using Gumbel-softmax sampling. A diversity loss applied to encourage full usage of the codebooks. The Transformer module contains 12 layers with 12 attention heads, a hidden size of 768, feedforward size 3072, and a dropout rate of 0.1. Finally, the classifier a two-layer MLP with 256 hidden units and ReLU activation, mapping the output one of five brain region classes
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Table 3: Model Hyperparameters for Lfp2vec
Component
Parameter
Value
Encoder
# Layers
7
Kernel Sizes
3, 3, 3,
Strides
(5, 2, 2,
Activation
GELU
Normalization
LayerNorm per layer 512
Output Dim
Quantizer
Codebooks
x
Total Dimension
640
Sampling Method
Gumbel--oftmax
Auxiliary Losses
Diversity Loss
Transformer
# Layers Attention Heads
12
12
Hidden Size
768
Feedforward Size
3072
Dropout
0.1
Classifier
Hidden Units
256
Activation
ReLU
Output Classes
5 (brain regions)
D Post-processing
We post-process the model's per-timepoint predictions Pr.c p xz)) by averaging class probabilities across temporal window. This acts as lightweight denoising step that suppresses local fluctuations in predictions. For each channel we compute smoothed class score:
Σ pr,c NEW.
Pe
W
where W; denotes a symmetric window around t. This temporal smoothing preserves softmax semantics and can be interpreted local belief update under the assumption of short-term temporal consistency. And we optionally incorporate class prior Te to bias predictions toward frequent classes. He may be uniform or estimated empirically from the training distribution. The final prediction is obtained by taking the most probable class:
0 = arg max Pt.c-
This smoothing strategy increases temporal coherence without introducing model parameters or transition assumptions. For spatial smoothing, apply majority voting across neighboring channels within each timepoint. Specifically, each prediction Dt,c is replaced with the most frequent label among a fixed spatial neighborhood No around channel This encourages spatial contiguity and reduces anatomically implausible local fluctuations.
The effectiveness of these post-procees poot-processing strategies is summarized in Figure The temporal smoothing ensures that each channel receives single, consistent anatomical label across trials. And the spatial smoothing ensures that there are no discontinuities in channel prediction. As shown in panels b, and f, this significantly reduces noise-driven fluctuations the raw predictions and produces cleaner anatomical maps, particularly in deeper structures where variability across trials is most pronounced. The accuracy plots (a, c, e) confirm that this approach yields measurable improve- ments, especially for Lfp2vec, which produces rich but sometimes inconsistent predictions. These findings highlight that post-prot proccssoossinn does not merely boost metrics, but selectively compensates for dataset-specific and sppccifcc noise patterns inherent in neural data.
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The reviewers of your paper will be asked to use the checklist one of the factors in their evaluation. While "[Yes] " generally preferable to "[No] ", it perfectly acceptable to answer "[No] "rovided proper justification is given (e.g., "error bars are not reported because it would be too computationally Raw Output
a)
Accuracy processing steps
b)
Temporal
Spatial
Ground Truth
Neuronoxus
Channel
spectrogram
simdr
brainbert
fpovec
model
C
0.8
d)
0.6
Accurscy
Allen
0.4-
Channel
0.2
0.0
Mo2ves
Accuncyy
0.6
Channel
IBL
0.2
spectrogram
simdd
branneert
medel
Trial
Trial
Trial
Trial
Figure 7: Efffet post-processing on anatomical decoding across datasets and models.. a, c, Classification accuracy before and after post-processing (temporal smoothing, spatial smoothing) across datasets (Neuronexus, Allen, IBL) and models. Accuracy consistently improves or remains stable after post-processing, with the largest gains seen for Lfp2vec. b, d. f) Visualization of predicted anatomical labels across channels and trials for an example session, before and after post-processing, compared ground truth. Raw predictions exhibit spatial and temporal discontinuity, particularly in deeper regions. Temporal and spatial smoothing align predictions more closely with anatomical boundaries, reducing local inconsistencies and increasing interpretability.
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Question:
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly state our key contributions--elf supervised LFP representation learning, zero-shot anatomical decoding, cross-lab and cross-species generalization, and downstream task transfer, which are all substantiated by the empirical and methodological results presented the paper.
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Question:
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Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics htttpp/////surpppp..////////eaee
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Justification: All animal experiments were conducted under institutional LACUC or equiva- lent approval collaborating labs, and we adhere community guidelines on data handling and privacy for neural recordings.
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
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Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
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Answer: [No]
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The answer NA means that the paper does use existing assets.
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If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com /dtassts has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
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If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
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Answer: [Yes]
Justification: We commit to releasing the Lfp2vee code, pretrained model checkpoints, and preprocessing scripts, each accompanied by usage documentation in our GitHub repository.
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Justification: Our study involves only animal electrophysiology recordings and does not include human crowdsourced experiments.
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The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
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Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: We do not use large language models in our methodology; all components are self-supervised transformer-basee models applied electrophysiology data.
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Please refer to our LLM for what should or should not be described.
24


Reviews:
[{"review_id": "2WXe1hzbtK", "reviewer": "Reviewer_vF5b", "summary": "This paper introduces Lfp2Vec, a self-supervised \"foundation model\" that learns embeddings directly from raw local field potential (LFP) traces recorded by high-density probes. Adapting the wav2vec 2.0 masked-prediction framework to electrophysiology, the model uses a 1D conv front-end plus a 12-layer Transformer to output 512D embeddings without any anatomical labels. The authors pre-train on three large mouse datasets collected in different laboratories (Allen Visual Coding, IBL Repro-Ephys, and 1024-channel Neuronexus SiNAPs recordings) and evaluate on four tasks. Across all datasets Lfp2Vec outperforms handcrafted spectrogram features, SimCLR on raw LFP, and the spectrogram-based BrainBERT, achieving accurate zero-shot region predictions and forming anatomically coherent embedding clusters. Minimal post-processing further boosts per-channel localization. Thus raw LFPs contain generalizable anatomical/functional cues, and Lfp2Vec could aid real-time probe placement and biomarker discovery.", "questions": "* You show a single zero-shot result on one macaque dataset. Could you (a) add a within-species baseline (macaque-only training) to quantify the transfer gap, and/or (b) report channel-wise confusion to reveal which regions fail?\n* Region clusters might reflect probe-type or amplifier signatures rather than neuroanatomy. Please test a domain-adversarial experiment (e.g., predict probe ID from embeddings) or provide a confusion table stratified by probe model after adversarial training\n* Can you visualize Transformer attention heads or perform a linear regression from embedding dimensions to canonical LFP bands (delta, theta, ripple)? Even qualitative insight would help users trust and adopt the model.", "limitations": "* The Alzheimer’s-model demo is preliminary. Clarify that the model is *not* validated for diagnostic use and discuss pathways (multi-site trials, regulatory approval) needed before clinical deployment.\n* The cross-species results are anecdotal; please state explicitly that performance on primate or human iEEG/LFP remains unverified and may require fine-tuning.\n* While rodent LFP poses minimal privacy risk, future human recordings could be re-identifiable. Discuss anonymization and consent practices that would be needed.", "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 paper that convincingly shows self-supervised masked prediction on raw LFP yields embeddings useful for anatomical localization and several downstream tasks, with promising cross-lab generalization. Originality is moderate but practical significance for neurophysiology is high.\n\nQUALITY\n\nStrengths:\n* The study combines 3 high-density mouse datasets from different labs\n* straightforward but effective adaptation of wav2vec-2.0 masking to 1D LFP, with ablations on spectogram input, SimCLR raw-waveform, and BrainBERT baselines\n* Rigorous benchmark evaluation (cross-lab transfer, downstream tasks, etc.)\n* Code link and dataset identifiers are provided\n\nWeaknesses:\n* Cross-species test is thin, with only one macaque dataset and a single motor task\n* Embedding dimensions and attention maps are not analyzed; biological insight is limited to UMAP plots\n* Probe-type or amplifier signatures could drive region clustering\n\nCLARITY\n\nStrengths:\n* Figures crisply show architecture, masking scheme, and localization heat-maps\n* Results tables separate in-lab, cross-lab, and cross-species settings\n* Writing is concise; methodological flow is easy to follow\n\nWeaknesses:\n* Key preprocessing steps (filter bands, re-referencing) are relegated to the supplement\n* Statistical significance notation is inconsistent across plots\n\nSIGNIFICANCE\n\nStrengths:\n* Accurate, label-free channel localization could aid real-time probe placement and large-scale annotation pipelines\n* Demonstrates that raw LFP encodes transferrable anatomical/functional signatures, a useful message for systems and clinical neurophysiology communities\n\nWeaknesses:\n* Broader AI relevance is moderate: the technique is a straightforward domain transfer of wav2vec rather than a new representation-learning principle\n\nORIGINALITY\n\nStrengths:\n* First work (to my knowledge) to apply masked predictive coding à la wav2vec to high-density LFP\n* Cross-lab foundation-model framing is novel within electrophysiology\n\nWeaknesses:\n* Conceptually similar to prior BrainBERT/EEG-BERT work; innovation lies mainly in domain shift rather than algorithmic novelty\n* No new masking strategy or architecture variant specific to bio-signals is proposed", "was_revised": true, "final_justification": "The rebuttal has alleviated (minor) concerns about cross-species evaluation, probe-type bias in clustering, and biological insight. I will maintain my positive score", "rebuttal": "We thank the reviewer for the thoughtful feedback and recognition of the practical impact and comprehensive evaluation across datasets and settings. The reviewer raised three main concerns: robustness of cross-species testing, potential clustering confounds, and limited interpretability of attention heads. We address concerns by conducting new experiments to resolve clustering confounds. \n\n\n**Weaknesses**:\n\n- **Cross-species evaluation**: We agree that a single macaque dataset focused on a motor task is limited in task diversity, and we have clarified this in the manuscript. That said, the macaque dataset presents a meaningful domain shift, across species, regions (motor vs. hippocampal/visual), and tasks (goal-reaching vs. spontaneous/visual), making it a nontrivial test of generalization. Our results serve as proof of concept, and we plan to expand datasets and analyses in future work.\n\n- **Probe-type bias in clustering**: We thank the reviewer for raising probe-type and amplifier signatures as potential confounds. In cross-session evaluation, we held out entire sessions with different probes to avoid such biases. To assess their impact further, we ran two analyses: (1) linear probes predicting session ID, probe ID, or region; and (2) Silhouette scores for clustering by each factor. Both analyses showed that anatomical structure dominates the embeddings, with minimal influence from session or probe. See Question #2 for details.\n\n- **Interpretability and biological insight**: We agree that analyzing embedding dimensions and attention heads would provide deeper insight. We will include these visualizations in the revision.\n\n- **Preprocessing pipeline**: We thank the reviewer for pointing this out. We would like to clarify that the core preprocessing steps are described in the main text (Section 4.1, line 150-160). These steps follow the IBL LFP-band destriping pipeline, including high-pass filter, bad channel removal, rephrasing and noise reduction.\n\n- **Statistical significance**: We thank the reviewer for raising this point. We have removed Table 1 and replaced it with Supplementary Figure 6, which includes error bars to reflect variance in disease classification across folds of test sessions. We also added error bars to Figure 3 in the revised manuscript.\n\n- **Novelty and broader relevance**: While our method builds on wav2vec 2.0, it goes beyond simple domain transfer. Unlike BrainBERT (spectrogram-based) and EEG-BERT (focused on behavioral or physiological state decoding), we introduce a new task: anatomical localization from raw LFP signals at the channel level. To our knowledge, this is the first SSL approach for this problem. For a broader AI audience, our results show that general-purpose SSL models can effectively transfer to noisy, high-dimensional LFP recordings, an important step toward foundation models for neural time series.\n\n**Questions**:\n\n“Could you (a) add a within-species baseline…, and/or (b) report channel-wise confusion…?”\n\n- Thank you for the suggestion. We report channel-wise confusion in Supplementary Figure 5 and will move it to the main paper. Our macaque results are within-species: trained on macaque sessions and tested on held-out macaque sessions. This shows that our audio-pretrained model can be fine-tuned for anatomical inference in a different species. True cross-species evaluation is limited by label mismatch: mouse data focus on hippocampal/visual regions, while macaque targets motor areas. Still, we are excited to explore cross-species generalization using macaque datasets with shared labels from the Allen Institute.\n\n“Region clusters might reflect probe-type…rather than…neuroanatomy. Please test a domain-adversarial experiment … or provide a confusion table stratified by a probe model…”\n\n- We thank the reviewer for raising this important point. To minimize the risk of probe-specific signatures driving clustering, our evaluation protocol uses a strict leave-session-out strategy. This ensures that each test session comes from a different animal and probe insertion than those seen during training, reducing the chance that the model memorizes probe-related artifacts.\n\n- To directly test whether probes are encoded in the learned representations, we conducted two more analyses: (1) linear prober to predict region/session ID/probe ID from embeddings, (2) label embeddings by probe ID or session ID to assess whether they cluster by probes, session, or anatomical region\n\n\nLinear Probing Accuracy\n\n|               | Spectrogram       | SimCLR            | BrainBERT         | lfp2vec           |\n|---------------|-------------------|-------------------|-------------------|-------------------|\n| **Region**    | 0.441±0.010       | 0.683±0.053       | 0.851±0.022       | 0.921±0.004       |\n| **Session**   | 0.229±0.007       | 0.232±0.009       | 0.271±0.023       | 0.515±0.035       |\n| **Probe**     | 0.050±0.004       | 0.054±0.007       | 0.076±0.012       | 0.273±0.019       |\n\n\n\nSilhouette Score for Clustering\n\n|               | Spectrogram       | SimCLR            | BrainBERT         | lfp2vec           |\n|---------------|-------------------|-------------------|-------------------|-------------------|\n| **Region**    | -0.054±0.015      | -0.062±0.050      | 0.146±0.026       | 0.576±0.026       |\n| **Session**   | -0.077±0.022      | -0.052±0.043      | -0.024±0.006      | -0.053±0.009      |\n| **Probe**     | -0.284±0.029      | -0.121±0.093      | -0.112±0.042      | -0.266±0.006      |\n\n\n- Linear probing results show that while our model embeddings encode some session and probe identity, the dominant signal is anatomical (region) information. This suggests the representations are primarily anatomy-aware, not probe- or session-biased. Also, Lfp2vec encodes significantly more region-specific information than baseline methods. \n\n- Silhouette scores show that Lfp2vec strongly clusters by brain region, but does not cluster by probe or session, suggesting that the model learns biologically meaningful structure rather than technical artifacts. Also, Lfp2vec shows more well-separated region clusters compared to baselines.\n\n- We will add this table and visualization of embeddings colored by probe and sessions for comparison to the supplementary. We appreciate the reviewer for the excellent ideas. \n\n“Can you visualize Transformer attention heads…or perform a linear regression from embedding dimensions to canonical LFP bands?”\n\n- We agree that this is a valuable approach for interpreting model internals and have made visualization of attention heads of the last layer. We will include these visualizations in the revised manuscript. \n\n\n**Limitations**:\n\n- More detailed Alzheimer's results are shown in Supplement Figure 6. We acknowledge that the demo is preliminary in mice and not validated for clinical or diagnostic use in humans. To deploy this clinically, we would need large scale, multiple site studies, cross-species validation, comparison with clinical biomarkers, FDA approval, and human trials with reproducibility studies. We appreciate the reviewer’s concern, and will revise the broader impacts to reflect careful consideration of clinical implication of the model performance.\n- Cross-species results are shown in Supplement Figure 5. We acknowledge that this is only within species study on maceque, and performance on human subjects remains unverified and may require further changes. \n- We appreciate the reviewer for bringing up the concern regarding privacy. Though our research does not involve human recording experiments now, we recognize that privacy becomes a critical issue when scaling such approaches to human data. For future research, we could remove all direct identifiers, mask metadata, and only release pretrained models or de-identified features instead of raw LFP traces. \n\nFormatting:\nSiNAPs probe stands for Simultaneous Neural Active Pixel Sensor probe"}, {"review_id": "7BLTgO6ts7", "reviewer": "Reviewer_4cyY", "summary": "•\tThis paper presents a method for predicting the location, i.e., the region where an electrode is implanted, based on the LFP signal recorded by that electrode. The method is based on wav2vec. Using self-supervision, a model is pre-trained to learn a contrastive objective. The authors then demonstrate that information about electrode location can be decoded from the resulting representations. They also show that the representations are useful for other downstream tasks.", "questions": "•\tLine 191: What is $\\mathcal{K}$? Is it the set of distractors unioned with the true codebook word? \n•\tSection 4.3: Earlier, on line 171, commitment and diversity losses were mentioned. Are they simply added to the loss on line 191?\n•\tFigure 2d: What does “temporal smoothing” refer to? I can’t find it in the text.\n•\tFigure 2d: The caption says “Channel-wise localization accuracy before and after…post-processing”. This makes sense for the right side of Fig 2d. But for the left-hand side: are the probes shown before or after post-processing?  What does “rescues baseline models” mean in the context of this figure?\n\nSmall things\n•\tFigure 1 Task 1: This is a little hard for me to interpret on the first reading. What do the columns correspond with? Are they probes? What are the colors? Are they regions?\n•\tSection 4.1: There are many abbreviations here: “LFP”, “IBL”, “DC”, “ADC”. I think “ADC” and “IBL” should be spelled out at least once. \n•\tSection 4.3: The description of the method is sparse. I assume this is because most of it is similar to the objective in wav2vec2.0. It could be worth mentioning that. \n•\tFigure 3b: The bar chart label says “wav2vec”", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Pros\n•\tThe task of anatomical source localization seems like a novel application for a self-supervised approach\n•\tThe post-processing seems novel and highlights the usefulness of having priors from neuroscience, i.e., knowing the relative size of regions. I imagine the post-processing could be made even more opinionated by injecting knowledge about which regions are proximal to which other regions.\n•\tPresentation of methods is well-written and clear.\n•\tResults are shown for ambitious cross-lab and cross-species settings\nCons\n•\tThe wav2vec2.0 architecture and approach itself are not novel. And other approaches have used similar wav2vec2.0 based approaches for LFP, e.g., BENDR https://arxiv.org/abs/2101.12037\n•\tThe paper proposes using a deep-learning approach to predict the anatomical location of electrodes. I have two doubts about the utility of such an approach. First, predicting a region label does not give the same information as co-registration with a pre-op MRI. The region label is much coarser. I do grant that this won’t matter for all analyses. Second, this method has much better performance in the cross-session and one-shot settings, as opposed to the cross-lab and zero-shot settings (Fig 1a and Fig 3). This is a little disappointing because, in the cross-session or one-shot setting, ground-truth labels for the location are already available, and prediction isn’t needed. Or am I misunderstanding something?", "was_revised": true, "final_justification": "The thorough response from the authors and their answers to the questions I raised have convinced me that this manuscript is suitable for publication.", "rebuttal": "We thank the reviewer for their positive feedback on our novel application of self-supervised learning to source localization, and effective usage of postprocessing as a neuroscience prior . The reviewer raised three main concerns: model novelty, the value of anatomical decoding, and unclear cross-lab evaluation. We address these by clarifying differences from prior methods like BENDR, highlighting the role of anatomical decoding when MRI is unavailable, and clarifying the cross-lab evaluation setup.\n\n\n**Weakness**:\n\n“Other approaches have used similar wav2vec2.0 based approaches for LFP, e.g., BENDR”\n- We thank the reviewer for highlighting relevant prior work such as BENDR. In the related work section, we have added the following: “Other models like BENDR [1], BrainBERT [2], EEG2REP [3], EEG-BERT [4] and related intracranial models operate on broadband signals to decode speech, behavioral, or cognitive states. While effective, these models are not designed for anatomical localization and do not incorporate anatomical priors or address cross-lab/species generalization in invasive electrophysiology. In contrast, our model operates directly on raw LFP signals and targets in vivo anatomical decoding, bridging neuroanatomy and self-supervised learning.” \n\n[1] Kostas et al., Frontier in Human Neuroscience, 2021\n[2] Wang et al., ICLR, 2023\n[3] Foumani et al., KDD, 2024 \n[4] Wang et al., biorxiv, 2024\n\n“Predicting a region label does not give the same information as co-registration with a pre-op MRI”\n- We thank the reviewer for raising this concern. We have revised the introduction to clarify that, unlike raw LFP traces, pre-op/post-op MRI is not always available in chronic studies or during surgery. Even when available, MRI lacks the spatial resolution needed to distinguish fine subregion structures such as CA1, CA2, CA3, DG in the hippocampus. \n\n“better performance in cross-session and one-shot …[than] the cross-lab and zero-shot. [But] in cross-session and one-shot, ground-truth labels … are already available”\n- We thank the reviewer for the comment and have clarified this in the revised manuscript. All evaluations are zero-shot on new insertions or new subjects, with no ground truth labels at inference time. Cross-lab zero-shot involves no examples from the new lab and is the most challenging task. One shot uses a single labeled session but is still evaluated on unseen insertions or subjects in the new lab. What our results show is that, (1) if a lab uses similar probes and task setup as Allen/IBL, this model generalizes well in zero-shot; (2) if the task/probe is new, one labeled session is enough to enable generalization to the future subjects using the same task/probe. With more diverse lab data in the future, the next step is good zero-shot transfer to new labs.\n\n\n**Questions**\n\n- Yes, we revised line 191 to clarify this. Specifically, we now add: “where K(m) denotes the set of distractors unioned with the true quantized vector q_m”\n- Yes, we clarified this in line 171 with the following addition: “This diversity loss is added to contrastive loss, weighted by a hyperparameter lambda that controls its strength”.\n- “Temporal Smoothing” in Figure 2d refers to the method described in Section 4.4. It involves computing a weighted average of label probabilities over a time window.\n- We revised the caption in Figure 2d to clarify it: Figure 2d (left) channel-wise prediction before post processing, Figure 2d (right) post processing applied on lfp2vec outputs. \n- We added more labels and legends in Figure 1 Task 1 and clarified this in the caption. “Dot plot shows predicted region probabilities per trial. Columns correspond to trials, rows to channels, and colors represent brain regions (matching the legend in middle panel). \n- We revised the text to define all abbreviations at first mention: IBL is the dataset collected by International Brain Lab, LFP is Local - - Field Potential signal, DC is direct current, ADC is Analog-to-Digital Converter in Neuropixel probes. \n- Thank you for pointing this out. We now include in Section 4.3, “The objective closely follows the wav2vec 2.0 contrastive learning framework.”\n- Apologies for the labeling error, we corrected it to “lfp2vec” in the revised Figure 3b."}, {"review_id": "BMw6fjr6fU", "reviewer": "Reviewer_jdpB", "summary": "This work presents an SSL architecture and training strategy heavily inspired by wav2vec. It employs 1D convolution encoding layers, a discrete codebook, and Transformer for masked reconstruction training. Embeddings are straightforwardly used with finetuned classification layers, whether for region or disease decoding. Model performance compared to existing baselines appear competitive, with potential for across-lab and across-species generalization.", "questions": "1. how large is within-session variability of embeddings, and therefore, the decoded brain region? \n2. Similarly, how does representation change according to task?\n3. line 166: please clarify how a 50Hz sequence post-convolution preserves fast oscillatory dynamics, which the various forms of gamma in the hippocampus can have frequencies much higher than the Nyquist rate of 25Hz here.\n4. I’m confused how the codebook dimensions add up to 512? Or is it 4 different codebooks of various sizes, all with a code dimensionality of 512?\n5. I think a linear probing experiment would be informative to assess the learned representations, instead of having to finetune an MLP, Transformer, and the code quantizer.\n6. Have the authors tried other masking strategies, aside from full time index masking, assuming that means masking out tokens at individual timepoints in their entirety? What about random masking over time and dimensions? \n7. Please clarify how the context and distractor tokens are chosen, based on temporal distance?\n8. Is the 5-NN majority vote procedure iterative, or simply done in one sweep based on the original model prediction?\n9. For how long is it safe to assume that channels sit in fixed anatomical locations (and layers) across all trials (windows) of one recording? Does one observe consistent prediction shifts over time for a long recording? (referencing line 199)\n10. Are the post-processing steps also applied to the other baseline models in Figure 2a & b? It is implied in the caption for d, but it’s unclear whether that applies to the previous results in the figure.\n11. Is there a quantification / ablation study of the impact of postprocessing?\n12. Apologies if I missed this, but how exactly is the unlabeled data for SSL and labeled data for fine-tuning split? Line 225 states zero-shot prediction is tested on test sessions. So are pretraining and finetuning data the same trials in the training set, just used without and then with labels?\n13. Just to clarify: the model operates entirely on 1D time series, on a basis of 3s snippets of individual channels? This is how it generalizes to other recording setups without reconfiguring the convolution encoders?\n14. why did one-shot finetuned performance decrease, e.g., for Allen to IBL transfer?\n15. It’s difficult to evaluate the consistency of higher performance in Figure 3 and Table 1 without error bars, though the absolute performance is nevertheless impressive.", "limitations": "Reasonable discussions of limitations in the proposed method given space constraint.", "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- tackles an important problem of using LFPs, which is much more readily available and easier to standardize, for anatomical localization. This is a very feasible use case so a compelling solution would be an important contribution.\n- really well written manuscript and I enjoyed reading it; the text is informative but concise, and adequately covers literature in both neuroscience and ML.\n- architecture and training strategy mirrors wav2vec2.0, is simply, elegant, and sensible\n- the post processing strategy is also interesting, and appears to boost performance\n- evaluation experiments appear to be rigorously setup (e.g., across-session splits), and the results presented seem convincing and is well-visualized\n- code is already available!\n\nWeaknesses\n- the model boasts prediction of detailed anatomical information, but all quantitative performance shown were for multi-class region classification. It should be straight-forward to extend to regression onto a continuous measure? It would be very interesting to apply the proposed method for really fine-graned depth localization use cases.\n- while the evaluation results are interesting and impressive in most cases, there remains some concerns regarding the setup and lack of variance reporting.\n- given that LFP signals change drastically as a function of task and behavioral state, I find it surprising that there is no evaluation of embedding or prediction consistency as a function of time / state\n- there are some overlap in text in section 5.2\n- the visualization in Fig 3a is a bit confusing, as one expects it to be a confusion matrix with low off-diagonal values, but it’s rather cross-dataset performance, such that off-diagonal values are ideally high.", "was_revised": true, "final_justification": "Technically sound paper, reasonable evaluations, and an adequate ML solution to an important and practical neuroscience problem.", "rebuttal": "We thank the reviewer for their positive feedback on the robustness of our method and its practical significance to neurophysiology. The reviewer raised three main concerns: the temporal stability of predictions and embeddings, the absence of variance reporting, and the limited explanation of design choices. We address these concerns by conducting additional experiments to analyze prediction and embedding consistency, updating our results to include error bars, and providing further clarification on our design choices.\n\n**Weaknesses**:\n\n“[Can we extend]...region classification…to a continuous measure? “\n\n- We can extend this approach to the Allen dataset, which includes continuous anatomical labels from the Allen Brain Atlas Common Coordinate Framework (CCF), providing x, y, z coordinates for each channel. By replacing the classification objective with a regression objective (e.g., MSE loss), the model can be fine-tuned to predict spatial coordinates directly. However, the IBL and Neuronexus datasets do not provide such continuous labels, so we focused on region classification in this study.\n\n“concerns regarding the setup and lack of variance reporting”\n\n- We thank the reviewer for raising this point. We have removed Table 1 and replaced it with Supplementary Figure 6, which includes error bars to reflect variance in disease classification across folds of test sessions. We also added error bars to Figure 3 in the revised manuscript.\n\n“no evaluation of embedding or prediction consistency as a function of time / state”\n\n- We thank the reviewer for raising this point. To address this, we added additional analysis of prediction consistency over time/trials. Here the first row is the mean variance of prediction probabilities across trials, averaged across sessions, second row is the mean cosine similarity for embeddings across trials, averaged across sessions. \n\n|                         | **Spectrogram**     | **SimCLR**          | **BrainBERT**       | **lfp2vec**         |\n|-------------------------|---------------------|---------------------|---------------------|---------------------|\n| **Prediction Variance** | 0.004 ± 0.002       | 0.012 ± 0.007       | 0.018 ± 0.005       | 0.018 ± 0.007       |\n| **Embedding Cosine Similarity** | 0.897 ± 0.151 | 0.551 ± 0.212       | 0.863 ± 0.051       | 0.864 ± 0.036       |\n\n\n- The small prediction variance indicates that our model, along with other baselines, produces consistent predictions across trials. Embedding similarity, measured as the average cosine similarity between normalized embedding vectors across trials, is also high in our model, suggesting that the learned representations are stable over time.\n\n\n“visualization in Fig 3a is confusing as one expects confusion matrix…overlap in text in Section 5.2”\n\n- We agree that current Fig 3a may resemble confusion matrices and lead to misunderstanding. To clarify, we added the following text in Fig 3a caption: “Cross lab generalization matrix for zero-shot and one-shot performance, here high off-diagonal values indicate good generalization performance from one lab to another”. We also cut Section 5.2 to avoid redundancy. \n\n\n**Questions**:\n\n- To evaluate within session variability, we compute embedding and prediction variance  across trials, as shown in Weakness # 3. Results showed that our model produces stable representation and consistent prediction with low variability across trials. Also, our model does not explicitly leverage task structure, and achieves accurate region decoding in a task agnostic manner. \n- We agree that signals above the Nyquist frequency cannot be faithfully reconstructed after downsampling. But empirically, we observe that the embeddings retain some high-frequency information, suggesting that the convolutional layers do not entirely filter out high-frequency components. One possible explanation is that high-frequency bursts may leave detectable traces in the lower-frequency structure, which the model may still exploit. To further investigate this, we plan to visualize the learned convolutional filters to understand which frequency components they emphasize or suppress. \n- We apologize for the oversight. This was due to changes across multiple configurations during tuning. The final model uses two notebooks, each with a size of 320, resulting in a total dimension of 640. \n- We agree that linear probing would offer a clearer view of the representations' quality and will include it in future analysis. \n- We thank the reviewer for this insightful point. Our model currently uses random time masking within each trial. Since inputs are 1D signals and each trial-channel pair is treated independently, no inter-trial structure is explicitly modeled. \n- In response to the suggestion, we clarified this in Section 4.3: “Context tokens are the transformer outputs at masked time steps; target tokens are quantized vectors at the same time step; distractors are quantized vectors sampled from all other time steps in the same batch.”\n- 5-NN majority vote is applied in a single pass. While an iterative approach could be explored, it may cause over-smoothing. In practice, one iteration is usually sufficient to correct spontaneous errors. \n- To ensure recording stability, we rely on high-quality data from collaborators and preprocessing tools such as DREDGE [1] to correct for motion artifacts. As an additional consistency check, we analyzed prediction variance across time in Weakness #3 and confirmed our model's robustness. \n\t[1] DREDGE, Windolf et al. , Nature Methods 2025\n- We apologize for the confusion in Figure 2d and have revised its caption as follows:: Figure 2d (left) channel-wise prediction before post processing, Figure 2d (right) example of post processing applied on lfp2vec outputs.  \n- To quantify the impact of post-processing, we added Supplementary Figure 7. Post-processing improves performance across all models, with the largest gain observed for LFP2vec.\n- We clarified Section 5.2 to specify: “We use the same unlabeled data for SSL training and the same data with labels for supervised fine-tuning.”\n- If the sampling frequency (1250 Hz) and trial length (3 s) are kept the same, the model should generalize across recording setups without reconfiguration.\n- When transferring from Allen to IBL, the model is pretrained on a larger, diverse dataset. In comparison, one labeled IBL session has fewer channels and less anatomical diversity. If we fine tune on the small, biased sample, it may skew the decision boundary and bias the model away from the more generalizable Allen representations.\n- To address the consistency issue, we removed Table 1 and replaced it with Supplement Figure 6 with error bars. We also added error bars for Figure 3, and the confusion matrix can be found in Supplement Figure 5."}, {"review_id": "MVowoL0NQR", "reviewer": "Reviewer_KDGD", "summary": "The paper introduces Lfp2Vec, a contrastive, masked SSL transformer method that utilizes Wav2vec-like training to learn neural embeddings from LFP data. \n\nThe embeddings exhibited high performance on a variety of different downstream settings, including within-session, cross-lab, and even cross-species, as well as on classifying alzheimer's disease.", "questions": "- Where do you foresee these methods being applied?\n- How do you think the performance would be affected if more areas were imaged?\n- is there any analysis on the dynamics of the vector quantizer? Do the learnt representations also make sense? Would you expect them to?", "limitations": "- Although method has shown superior performance on a good amount of tasks already, it could be tried on more extensive spans of areas (deeper cells) to see if this performance chasm remains.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n- Improved results across the board.\n- Variety of tasks and settings.\n- Tested on both cortex and hippocampus regions.\n- Method is generalizable, and makes intuitive sense. Has been used in other neural tasks (CEBRA, Neuroformer, etc).\n- Informative figures, paper is in general well-presented and written.\n\n**Weaknesses**\n- Similar methods have already been attempted.\n- Sparse reporting. Exact accuracies for the tasks are missing, including confidence intervals and significance testing.\n- It would be desirable to also show results on deeper areas if possible.\n- The codebook dynamics for the learnt vector quantizer were left unexplored. This would be an interesting area of exploration.", "was_revised": true, "final_justification": "The paper is well presented, impactful, and is broadly applicable to important neuroscientific research.", "rebuttal": "We thank the reviewer for their positive feedback on the robustness and generalizability of our model across tasks. The reviewer raised three main concerns: model novelty, incomplete reporting, and lack of codebook interpretation. We address these concerns by clarifying the model's novelty, adding error bars to the results, and providing visual analysis of the codebook vectors.\n\n**Weaknesses**:\n- While prior work exists, our approach is the first to apply SSL directly on raw LFPs for anatomical decoding across species and labs.\n- To address the sparse reporting, we replaced Table 1 with Supplement Figure 6, and added Figure 3 with Supplement Figure 5. These additions made sure that all accuracy values are reported with error bars. \n- We agreed that deeper region results are valuable. This is our important next step to extend this method to thalamus, and whole brain regions.\n- We thank the reviewer for this suggestion. We applied t-SNE to analyze the quantized vectors in the codebook and included it in the revised manuscript.\n\n**Questions**:\n- Following this suggestion, we expanded the broader impact section: “these methods could be applied in physiological experiments to provide real time feedback, or in chronic or clinical settings, or locating physiological events preemptively (e.g. predict which electrodes the seizure/sharp wave ripple occur), so we can do early discharges during closed loop stimulation. “\n- With broader spatial coverage, we expect performance to vary by region. Potential factors affecting the performance are signal quality, number of labels (sparse labels often give poor performance), and if there exist signature biomarkers for sublayers. \n- We visualized the codebook and will include them in the revised manuscript. \n\n\n**Limitations**:\n- We agree that testing in deeper brain regions (e.g.,thalamus, basal ganglia) is important for assessing generalizability. Our current study focused on cortical and hippocampus areas due to data availability. As a next step, we plan to incorporate datasets with well-labeled deep structures to evaluate whether performance and embedding consistency persist in more heterogeneous regions. \n\n**Formatting**:\n- We removed Table 1 and replaced it with Supplement Figure 6"}]

Author rebuttal:

Meta-review: This paper adopts a wav2vec2.0 style modeling approach for local field potentials (LFP), evaluated on data recorded by the IBL, the Allen Visual Coding datasets, and Neuronexus datasets as well as macqque sequential reaching.

Reviewers are unamously positive about this paper. The reviewers highlighted the simplicity of transferring the wav2vec2.0 approach adapted to Lfp signals, and the the evaluation paradigm involving datasets across three labs. However, the reviewers critized the limited conceptual advance in terms of modeling. The approach is largely adopted from wav2vec 2.0, without an in-depth study if parts of the approach could be better fine-tuned to the application approach at hand. In particular, *vF5b* quotes that the broader AI relevance is moderate as the technique is a "straightforward" extension of wav2vec rather than conceptually novel (and, as my addition: the evaluation of the technique is too shallow to generate domain-specific insights beyond what the authors report in the paper). *4cyY* and *KDGD* agree regarding this. *KDGD* also criticies sparse reporting, which could partially be resolved during the rebuttal phase. Deeper analysis could have further strenghtened the manuscript.

Finally, in the results section, a lot of imprecise statements leave the discussion at a qualitative level, for instance, "sharper diagonal dominance", "Channel-wise localization accuracy" (which is not quantified in the figure), "While embeddings from SimCLR and BrainBERT show only partial clustering by region, Lfp2vec produces well-separated, compact clusters that align closely with ground-truth labels.", "especially in difficult CA2–CA3 distinctions", etc.

Considering all reviewer feedback and following the further discussion, I overall **recommend this paper for acceptance, but would ask the authors to perform the following *minor revisions* for the camera ready**:

- Work in all results promised during the rebutal phase, particular with respect to confidence intervals, and pulling results from appendix to the main paper.
- Carefully check statements in the abstract, introduction, and the results section again. In the results section, back up qualitative statements with clear summary statistics derived from the reported results

I recommend acceptance as a poster presentation. For a spotlight/oral, I agree with the reviewer's sentiment that conceptual novelty is too limited, while at the same time, a deeper empirical investigation or application to other tasks (e.g. coordinate localization, further ablations, etc.) is missing.
Decision: Accept (poster)