Title: Self supervised learning for in vivo localization of microelectrode arrays using raw local field potential
Venue: NeurIPS 2025
Accepted: yes
Keywords: neural decoding

electrophysiology

contrastive learning

neuroscience
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

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 audiopretrained 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 outperforms 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 .

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 [7]. However, these approaches lack in vivo knowledge of probe locations, and they are labor intensive, error-prone (limited by imaging resolution) and unsuitable for chronic recordings, flexible probes or clinical settings where tissue cannot be sacrificed.

As multishank arrays scale to over a 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 [9] or firing rates [10, 11]. However, these approaches require potentially inefficient and error-prone spike sorting [12] and ignore the rich structure in the local field potential (LFP) band [13, 14]. In parallel, self-supervised learning has been applied to unlabeled time series and various downstream tasks in audio [15] and neural signals [9, 16, 17]. Most existing work decodes behavioral states [18], discrete events [19], 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-supervised 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 Lfp2vec , a self-supervised 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-supervised training on large-scale LFP data. We hypothesize that representations learned from general purpose time series data are partially transferable to the neural domain. As our ablation studies will show (Fig. 5), this cross-domain initialization provides a 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], International 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 to 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-specific challenges such as biological noise, non-stationarity, and diverse sampling rates. In neural data science, SSL has shown promise primarily in the spike domain. Methods such as POYO [9], NEMO [11], 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 do 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

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 on hand-crafted electrophysiological features or biomarkers. For example, sharp wave ripples help identify CA1 sublayers in the hippocampus [28], gamma coherence distinguishes hippocampal subregions [29], and firing statistics (e.g., burstiness, firing rates) differentiate cortical layers in V1 [30]. However, these approaches are typically heuristic, region-specific, and not learned from data. More recent methods apply deep learning on 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 [11] learn neuron embeddings for region decoding at single-unit level, yet they ignore population-level dynamics that captures brain region identity. Furthermore, most models are trained and evaluated within a single 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.

3 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-truth 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 performing 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 Simultaneous Neural Active Pixel Sensor (SiNAPS) probe recordings from mice during spontaneous activity [8]. It includes five transgenic mice modeling Alzheimer'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 a brain atlas.

Macaque Sequential Reaching Dataset. This dataset consists of Neuropixels-NHP recordings from a 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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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. 1a), which standardizes inputs across devices (e.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, as shown in Fig. 1b. 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 a two-layer MLP classifier to predict brain-region or disease labels (Fig. 1d).

4.3 Objectives

We train Lfp2vec in two stages, adapting from a pretrained self supervised model to LFP recordings, as shown in Figure 1 b. The objective closely follows the wav2vec 2.0 contrastive learning framework.

̸

Self-supervised pretraining. Given an LFP segment x ∈ R T , we compute tokens z = E ( x ) and discretize them via Gumbel-softmax quantization q = Q ( z ) with multiple codebooks [21]. We randomly mask time steps M in z , feed the masked latents ˜ z into the Transformer, and obtain contextual embeddings c = C (˜ z ) . For each masked step m ∈ M , we apply a contrastive objective that pulls the context token c m (Transformer output at m ) toward the target token q m (quantized vector at m ), while pushing it away from distractors { q k } k = m sampled from other time steps in the same batch. With temperature τ , the loss is:

where K ( m ) denotes the set of distractors unioned with the true quantized vector q m . To encourage diverse codebook usage, we additionally apply a diversity loss L div weighted by λ . The overall pretraining objective is L SSL + λ L div [21].

Supervised fine-tuning. We attach a classification head h ( · ) to the pooled context outputs ¯ c = POOL( c ) , and fine-tune all parameters end-to-end on anatomical region labels { y i } . Denoting model outputs as ˆ y i = h (¯ c i ) , we minimize cross-entropy: L CE = -∑ i y ⊤ i log ˆ y i , 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 a lightweight post-processing pipeline (Figure 1 d). We first smooth model's per-timepoint predictions by averaging class probabilities over a temporal window W t with class prior π c , and assign the final label as ˆ y t = arg max c 1 |W t | ∑ t ′ ∈W t p t ′ ,c π c . , 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.

5 Experiments

5.1 Baselines

For baseline comparisons, we implemented a classical spectrogram model and two self-supervised methods, each paired with a two-layer MLP decoder (128 hidden units, ReLU) trained using Adam optimizers. Spectrograms were computed using Short Time Fourier Transform, producing 500 frequency bins × 16 time bins per trial. For SimCLR [34], a CNN encoder with an MLP projection head was trained on 3-second raw LFP segments (1.25 kHz) using InfoNCE loss (temperature = 0.606) with temporal masking. BrainBERT [35] uses a 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 across-lab evaluation protocol.

Across-session evaluation. We split each lab's recordings by session, reserving 15% sessions for testing, 30% for validation. Models are trained on all channels 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 a single session from the target lab to training. This setup tests each model's ability to extract anatomy-relevant features without relying on probe geometry or task structure.

6 Results

6.1 Brain Region Decoding Across Sessions

We evaluate all models on cross-session region decoding in three large-scale LFP datasets (Neuronexus, Allen, and IBL). As shown in Figure 2a, Lfp2vec achieves the highest balanced accuracy and macro F1, 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 Lfp2vec achieves the fewest errors across all classes, including in underrepresented subregions such as CA2 and CA3. All models perform best on CA1 and cortex, likely due to their distinctive signatures and class prevalence.

PCA projections of latent embeddings (Figure 2c) suggest that Lfp2vec produces more compact and separable clusters by region. To test whether this structure reflects neuroanatomy rather than probeidentity 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. 2a, right. Lfp2vec achieved a markedly higher Silhouette score for region clustering (0.576±0.026) than the spectrogram (-0.054±0.015), SimCLR (-0.062±0.050), and BrainBERT (0.146±0.026) baselines, while showing no meaningful clustering by session (-0.053±0.009) or probe identity (-0.266±0.006). 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 (0.921±0.004) than baselines. Together, these findings demonstrate that Lfp2vec learns more anatomically aligned representations.

Figure 2d shows the channel-wise prediction map compared to the ground truth. Although all models capture the coarse anatomical layout, Lfp2vec's predictions more closely adhere to the known laminar organization of the hippocampus. For instance, in the example shown, it clearly delineates the CA1DG boundary, a distinction that is less precise in the baseline models. A zoomed view (Figure 2d, 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 on one lab, and evaluate its zero-shot prediction accuracy on another lab with different tasks and probe geometries (Figure 2e). 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 2e, left). In contrast, transfer from the Neuronexus dataset to others is weaker, possibly due to its distinct probe design and spontaneous recordings. When a single session from the target lab is included (one-shot transfer), accuracy improves substantially, often matching within-lab performance (Figure 2e, right), suggesting that minimal data can suffice to adapt Lfp2vec to new labs.

6.3 Brain Region Decoding Across Species

To test cross-species generalization, we apply Lfp2vec to the macaque Neuropixels-NHP dataset (Figure 3a) using the same across-session protocol. As shown in Figure 3b, Lfp2vec outperforms

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all baselines (spectrogram, SimCLR, BrainBERT), achieving the highest test accuracy and macro F1 without any species-specific adaptation. The confusion matrices (Figure 3c) reveal that Lfp2vec has the fewest misclassifications, especially between closely related regions such as SMA and M1. Latent space projections (Figure 3d) 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 ability to extract generalizable, anatomically meaningful features from LFP signals across species and experimental settings.

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6.4 Disease Prediction

Besides the brain region decoding task, we evaluate Lfp2vec on a downstream task of Alzheimer's disease (AD) classification using LFP recordings from App x Psen1 transgenic mice [8]. A lightweight classifier is fine-tuned on top of pretrained Lfp2vec embeddings to distinguish AD mice from healthy controls. As shown in Figure 4a, Lfp2vec outperforms SimCLR and BrainBERT in both accuracy and F1 score. PCA projections of Lfp2vec embeddings (Figure 4b) show clear separation between diseased and healthy samples, suggesting the model captures disease-relevant 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 4c-d), and aggregate them into regionlevel 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 Lfp2vec's ability to detect and localize pathological neural signatures without supervision.

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6.5 Ablation Study

To isolate the benefits of our learning strategy, we conducted an ablation study (Fig. 5) evaluating two key factors: the amount of 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 decoding performance, with accuracy increasing with the number of unlabeled trials. More importantly, audio-initialized models significantly outperformed randomly initialized ones. For instance, on the Neuronexus dataset, the audio-initialized model with only a small amount of LFP pre-training (6k trials) already matches the performance of a randomly-initialized model trained on over 400k trials. This demonstrates that

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features learned from audio provide a strong inductive bias for modeling neural signals. The best performance is consistently achieved by combining both strategies: initializing from an audio foundation model and continuing self-supervised learning on a large corpus of in-domain LFP data.

7 Discussion

In this work, we presented Lfp2vec , a self-supervised model that learns anatomy-aware representations 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 finegrained anatomical structure rather than session-specific 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 a non-rodent setting with improved decoding accuracy on macaque motor cortex recordings without redesigning the architecture. Finally, we demonstrate that Lfp2vec embeddings support a downstream Alzheimer'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 (IBL destriping), suggesting that end-to-end artifact suppression or 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), so we 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, Lfp2vec 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.

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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 region-specific accuracy across all datasets. While regions like the cortex and CA1 are reliably identified by all models, subfields such as CA2 and CA3 remain harder to distinguish. This is likely due to the sparsity of region labels and their lower biological separability. Notably, Lfp2vec 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 to generalize across recording platforms with varied probe geometries and signal characteristics.

B Baseline Settings

SimCLR We implement SimCLR [34] 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 a projection head composed of two linear layers with ReLU activation. During pretraining, we use the NT-Xent loss (normalized temperature-scaled cross-entropy loss) to maximize agreement

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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 [35], which adapts a 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 1e-5, a batch size of 32, and trained for 50 epochs. For fine-tuning, we use a learning rate of 3e-5, keep the batch size at 32, and train for 10 epochs. We apply gradient accumulation over 4 steps to simulate a larger effective batch size, and use a 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 1D 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.

This produces a 512-dimensional latent representation at each time step. The quantizer discretizes these features using Gumbel-softmax sampling. A diversity loss is applied to encourage full usage of the codebooks. The Transformer module contains 12 layers with 12 attention heads, a hidden size of 768, a feedforward size of 3072, and a dropout rate of 0.1. Finally, the classifier is a two-layer MLP with 256 hidden units and ReLU activation, mapping the output to one of five brain region classes.

D Post-processing

We post-process the model's per-timepoint predictions p t,c = p ( y = c | x t ) by averaging class probabilities across a temporal window. This acts as a lightweight denoising step that suppresses local fluctuations in predictions. For each channel c , we compute a smoothed class score:

where W t denotes a symmetric window around t . This temporal smoothing preserves softmax semantics and can be interpreted as a local belief update under the assumption of short-term temporal consistency. And we optionally incorporate a class prior π c to bias predictions toward frequent classes. π c may be uniform or estimated empirically from the training distribution. The final prediction is obtained by taking the most probable class:

This smoothing strategy increases temporal coherence without introducing model parameters or transition assumptions. For spatial smoothing, we apply majority voting across neighboring channels within each timepoint. Specifically, each prediction ˆ y t,c is replaced with the most frequent label among a fixed spatial neighborhood N c around channel c . This encourages spatial contiguity and reduces anatomically implausible local fluctuations.

The effectiveness of these post-processing strategies is summarized in Figure 7. The temporal smoothing ensures that each channel receives a single, consistent anatomical label across trials. And the spatial smoothing ensures that there are no discontinuities in channel prediction. As shown in panels b, d, and f, this significantly reduces noise-driven fluctuations in 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 improvements, especially for Lfp2vec, which produces rich but sometimes inconsistent predictions. These findings highlight that post-processing does not merely boost metrics, but selectively compensates for dataset-specific and region-specific noise patterns inherent in neural data.

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NeurIPS Paper Checklist

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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-selfsupervised 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 in the paper.

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Justification:Sections 4.1-4.4 and 5.2 provide comprehensive details on data splits (train/val/test), hyperparameters (learning rates, batch sizes, dropout), optimizer settings (AdamW), and early-stopping criteria, with full configuration available in the Appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: We include error bars (standard deviations) on all quantitative plots (e.g., Figures 2, 3, 4), computed via repeated train/validation splits and model initializations, and describe in the text that these capture variability across sessions and random seeds.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [No]

Justification: While we detail model architectures, data splits, and training schedules, we do not specify the GPU types, memory requirements, or total training time; this information will be added in the supplementary material.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: All animal experiments were conducted under institutional IACUC or equivalent approval at collaborating labs, and we adhere to community guidelines on data handling and privacy for neural recordings.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We include a dedicated 'Broader Impact' paragraph outlining benefits for real-time electrode localization and closed-loop neurostimulation, as well as neuroprivacy risks and proposed mitigation strategies.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [Yes]

Justification: We advocate for controlled sharing of pretrained weights under ethical licenses, rigorous consent protocols, and guidelines to prevent misuse in surveillance or coercive applications.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: 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?

Answer: [No]

Justification: While we cite all public datasets and software packages, we have not yet specified the exact licenses; these will be clarified in the final manuscript and supplementary.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We commit to releasing the Lfp2vec code, pretrained model checkpoints, and preprocessing scripts, each accompanied by usage documentation in our GitHub repository.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: Our study involves only animal electrophysiology recordings and does not include human or crowdsourced experiments.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: No human subjects are involved; all animal work was conducted under appropriate institutional animal care and use approvals.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

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For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: 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, or originality of the research, declaration is not required.

Answer: [NA]

Justification: We do not use large language models in our methodology; all components are self-supervised or transformer-based models applied to electrophysiology data.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
comments: SUMMARY:
The paper introduces Lfp2Vec, a contrastive, masked SSL transformer method that utilizes Wav2vec-like training to learn neural embeddings from LFP data. 

The 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.

STRENGTHS AND WEAKNESSES:
**Strengths**
- Improved results across the board.
- Variety of tasks and settings.
- Tested on both cortex and hippocampus regions.
- Method is generalizable, and makes intuitive sense. Has been used in other neural tasks (CEBRA, Neuroformer, etc).
- Informative figures, paper is in general well-presented and written.

**Weaknesses**
- Similar methods have already been attempted.
- Sparse reporting. Exact accuracies for the tasks are missing, including confidence intervals and significance testing.
- It would be desirable to also show results on deeper areas if possible.
- The codebook dynamics for the learnt vector quantizer were left unexplored. This would be an interesting area of exploration.

QUESTIONS:
- Where do you foresee these methods being applied?
- How do you think the performance would be affected if more areas were imaged?
- 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.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-24

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nReasonable discussions of limitations in the proposed method given space constraint.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n•\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.\n\nSTRENGTHS AND WEAKNESSES:\nPros\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?\n\nQUESTIONS:\n•\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”\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThis 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\n\nQUESTIONS:\n* 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.\n\nLIMITATIONS:\n* 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}