Title: SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spike Sorting

Neuroscience
Abstract: Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling researchers to study how specific neurons communicate and process information. Although there exist a number of spike sorting methods which have contributed to significant neuroscientific breakthroughs, many are heuristically designed, making it challenging to verify their correctness due to the difficulty of obtaining ground truth labels from real-world neural recordings. In this work, we explore a data-driven, deep learning-based approach. We begin by creating a large-scale dataset through electrophysiology simulations using biologically realistic computational models. We then present SimSort, a pretraining framework for spike sorting. Trained solely on simulated data, SimSort demonstrates zero-shot generalizability to real-world spike sorting tasks, yielding consistent improvements over existing methods across multiple benchmarks. These results highlight the potential of simulation-driven pretraining to enhance the robustness and scalability of spike sorting in experimental neuroscience.

SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation

Abstract

Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling researchers to study how specific neurons communicate and process information. Although there exist a number of spike sorting methods which have contributed to significant neuroscientific breakthroughs, many are heuristically designed, making it challenging to verify their correctness due to the difficulty of obtaining ground truth labels from real-world neural recordings. In this work, we explore a data-driven, deep learning-based approach. We begin by creating a largescale dataset through electrophysiology simulations using biologically realistic computational models. We then present SimSort , a pretraining framework for spike sorting. Trained solely on simulated data, SimSort demonstrates zero-shot generalizability to real-world spike sorting tasks, yielding consistent improvements over existing methods across multiple benchmarks. These results highlight the potential of simulation-driven pretraining to enhance the robustness and scalability of spike sorting in experimental neuroscience.

1 Introduction

Understanding the complex computations performed by the brain requires insight into the activity of individual neurons [1, 2], which is crucial for exploring how information is encoded, processed, and transmitted within neural circuits, as well as decoding the brain's functional dynamics [3, 4, 5]. Recent advances in neural recording technologies have enabled capturing the activity of neurons across multiple regions of the brain with high spatial and temporal precision [6, 7, 8, 9]. Notably, extracting meaningful information from recordings relies on a critical step known as spike sorting .

Spike sorting is the process of extracting and identifying neural activity from extracellular recordings. It involves two main steps (Fig. 1): spike detection , which extracts spike events from background noise, and spike identification , which assigns these detected spikes to individual neurons [10]. Spike sorting is indispensable for transforming raw electrical signals into interpretable data that reveal the firing patterns of individual neurons. Accurate spike sorting is essential for linking neural activity to behavior, understanding the functional organization of neural circuits [11], and uncovering mechanisms underlying various sensory and cognitive processes [12]. Furthermore, its role extends to translational applications, such as improving neural prosthetics and developing closed-loop BCIs, where spike sorting is critical for achieving precise neural decoding and control [13, 14].

Threshold-based

method

SimSort

Threshold

Detector

× channels

pre-setting

voltage threshold

Detector

detected spikes

detected spikes

# channels

Non-learning method

SimSort

# waveforms

PC 1

PC 2

putative

neurons

embedding space

putative

neurons

Detected Spikes

# channels

# channels

For a long time, spike sorting predominantly relied on heuristic statistics and machine learning approaches [1, 15, 16, 17, 6, 18, 19]. While these methods have promoted neuroscience research, they exhibit several key limitations. First, their sorting results are sensitive to parameter settings and post-processing, which depend on the experimenter's expertise and must be customized for each dataset. Moreover, these approaches lack a data-driven foundation, limiting their scalability and adaptability across diverse experimental settings, particularly in low signal-to-noise ratio (SNR) and high-variability scenarios, where their performance declines significantly. Recently, deep learningbased methods, such as YASS [20] and CEED [21], have attempted to improve spike sorting by adopting data-driven approaches to enhance detection and clustering accuracy. However, their generalizability and practical use are hindered by the limited training datasets.

To address these limitations, we highlight the importance of using massive training data with groundtruth annotations to achieve more reliable and robust spike sorting. Furthermore, we aim to design a deep learning framework optimized for spike sorting and pretrain models for practical usage.

Therefore, in this work, we first generated a large-scale labeled dataset to address the scarcity of ground-truth data for spike sorting. Building on this, we present SimSort , a framework that utilizes data-driven approaches to enable fully automated spike sorting. By pretraining a spike detection model on the large-scale dataset, SimSort achieved notable improvements in detection accuracy and adaptability compared to commonly used threshold-based methods. Additionally, SimSort incorporated contrastive learning to enhance waveform feature representations, achieving better robustness against noise. We evaluated SimSort in zero-shot settings on publicly available datasets without fine-tuning, showing its effectiveness in spike sorting tasks compared to existing methods.

The key contributions of the SimSort framework include:

A publicly available, large-scale labeled dataset to help address the scarcity of ground-truth data, supporting the development and evaluation of learning-based spike sorting methods.

A pretraining paradigm for spike sorting with a large-scale simulation dataset. The results, for the first time, demonstrate successful zero-shot transfer from simulated to real-world spike sorting tasks.

An off-the-shelf pretrained model for neuroscientists to use * , enabling fully automated spike sorting on Tetrode recordings without dependence on hand-tuned parameters.

2 Preliminaries

The goal of spike sorting (Figure 1) is to extract single-neuron spiking activity from extracellular recordings, where the activities of an unknown number of neurons are mixed together. Mathematically,

spike sorting is a blind source separation problem [22] and can be formally defined as follows: Let V ∈ R T × C represent the extracellular recording (voltage) across C electrode channels over T time points. The goal is to infer a set of spike times { t k } K k =1 and corresponding neuron labels { y k } K k =1 , where y k ∈ { 1 , . . . , N } represents the neuron identity, and N is the total number of detected neurons.

3 Related Work

Early spike sorting pipelines typically used thresholds to detect spike events, followed by dimensionality reduction (e.g., PCA or t -SNE) and clustering [23, 24, 25]. Although this pipeline was straightforward, it often suffered from noise susceptibility, inaccurate detection of low SNR events, and reliance on manual parameter tuning.

Deep learning approaches have been adopted to replace or enhance certain steps in traditional pipelines, aiming to improve robustness, automation, and adaptability to diverse experimental conditions. Autoencoders have been explored as a method for dimensionality reduction [26, 27, 28]. YASS [20] employed a convolutional neural network for spike detection and waveform cleaning, thereby mitigating clustering errors caused by distorted waveforms. CEED [21] applied a contrastive learning framework to enforce invariances to amplitude fluctuations, noise, and channel subset changes in the extracted embeddings. However, these methods were limited by their reliance on restricted training datasets, potentially reducing their generalization across diverse experimental conditions.

In particular, YASS depended on high-quality prior training data and assumed a consistent experimental setup with validated sorting results, limiting its utility in scenarios where training data were scarce or recording conditions varied significantly.

Similarly, CEED had its limitations: (1) its invariance assumptions may not generalize to datasets with unaccounted variability; (2) it relied on KiloSort2.5-processed data, potentially inheriting inaccuracies from these analyses; (3) its training and testing datasets were narrowly scoped, originating from similar experimental conditions, which may constrain broader applicability; and (4) it did not optimize spike detection or provide a complete spike sorting pipeline.

To overcome these limitations, we propose a data-driven approach to develop a fully automated spike sorting pipeline that does not rely on manually defined parameters, demonstrating improved generalization across diverse datasets and recording conditions.

4 Methods

4.1 Dataset Creation

To address the scarcity of labeled spike data, we generated a large-scale synthetic dataset using biophysically detailed neuron models from the Blue Brain Project (BBP) [29, 30, 31], covering 206 neuron models from layers 1-6 of the juvenile rat somatosensory cortex. These models capture diverse morphologies and electrophysiological dynamics across 30 neuronal types.

Electrophysiology Simulation Based on these models, we first simulated intracellular activity by injecting noise currents into multi-compartment neurons using the NEURON simulator [32], producing realistic spiking responses. Then, extracellular potentials were computed via volume conductor theory, summing transmembrane currents across compartments to obtain virtual recordings from tetrode electrodes randomly placed near the neurons. Each simulation trial included 5 neurons of varying types, placed in a 100 × 100 × 100 µ m 3 volume, with randomized positions for both cells and electrodes. (Figure 2; full technical details in Appendix A).

Dataset Preparation We generated a large-scale dataset consisting of 8192 simulated recording trials, representing continuous neuronal activities from more than 40,000 individual neurons. Intracellular spike timestamps were recorded as ground-truth for extracellular spikes, providing precise annotations for model training and evaluation.

The dataset was structured into two primary subsets:

Continuous Signal Dataset : This subset comprised full-length extracellular recordings across all electrode channels. It was designed for training spike detection models and evaluating the overall performance of spike sorting pipelines.

Input

V intra

V extra

Pipette

50 mV

200 μV

1 ms

Noise

(a) Multi-compartment neuron model

(b) Simulation

compartment

compartmental model

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soma

branch

R i

/2

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/2

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ext, i

r

l, i

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k, i

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E k, i

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(c) Simulated Extracellular Recording

A large-scale simulated

extracellular dataset

Electrode

YASS

CEED

2 trials,

400 neurons

1 trial,

unkown neurons

SimSort

8192 trials,

over 40,000 neurons

Train dataset size

Spike Waveform Dataset : This subset contained spike waveforms extracted from each ground-truth units. It was used for training and evaluating the spike identification model.

We used signals simulated from BBP layers 1-5 neuron models for training and validation, reserving signals from BBP layer 6 neuron models exclusively for evaluation, ensured that the test set comprises previously unseen neuron types and configurations, allowing us to effectively assess the model's generalization capabilities.

4.2 Spike Detection

We formulate the spike detection task as identifying the temporal segments { t k } corresponding to putative neural spikes from the raw input V ∈ R T × C , where T represents the number of time points and C denotes the number of electrode channels. The raw signal V first underwent two preprocessing steps: bandpass filtering and spatial whitening. Then, the processed signal was subsequently provided as input to the spike detection model.

We utilized a Transformer-based architecture [33] for spike detection, trained on the simulated continuous signal dataset detailed in Sec. 4.1. For robustness and generalization, data augmentation was integrated into the training process, referring to Fig. 3. We employed a binary cross-entropy loss for the spike detection model:

output

Ground-truth spikes

Spike Detection

Detector

Random

Augmentation

Transformer

Embeddings

FC layer

PE

Probabilities

Sigmoid

where the binary label y t denoted spike events, ˆ y t was the predicted probability, T was trial length, and w p was a hyperparameter to reweigh the positive predictions given the general scarcity of spikes.

Data Augmentation: To improve robustness and generalization in spike detection, data augmentation was applied to each input signal segment V ′ ∈ R T × C (Fig. 3). A subset of channels C ′ ⊆ { 1 , 2 , . . . , C } was randomly selected with a probability p to apply augmentation. Various augmentations, such as adding noise, amplitude scaling, and temporal jitter, were applied to enhance the diversity of training data. The detailed implementation of these augmentations is provided in Appendix A.

4.3 Spike Identification

We formulate spike identification as embedding each identified spike waveform X i ∈ R L × C , the voltage trace (waveform) of spike i , into a latent space to learn robust representations using contrastive learning. Then the learned embeddings are clustered into several groups, indicating putative neurons.

The spike identification model was trained on the simulated spike waveform dataset (Sec. 4.1). The model consisted of several key components, as illustrated in Fig. 4 and described as follows.

Contrastive Learning: We leveraged contrastive learning to learn representations that capture the relationships between spike waveforms (Fig. 4). Contrastive learning sought to map neural data into an embedding space where related examples were positioned nearby, while unrelated examples were placed further apart [34, 35]. For each waveform X i , we constructed a triplet by selecting X + i with

Spike Identification

Random

Augmentation

Encoder

Contrastive Learning

A P

N

Clustering

Waveform

Rep

.

Denoise

the same label and X -i with a different label. The objective was [36]:

where f ( X i ) denoted the embedding of X i , and α was a margin enforcing separation between positivenegative pairs. Additionally, we uniformly applied data augmentation to each triplet waveforms. The augmentation methods followed those in Sec. 4.2, with adjusted parameters (Appendix A) for better waveform representation, ensuring robustness to noise, amplitude variations, and temporal jitter.

Denoising: The augmented waveform X ′ i ∈ R L × C then underwent a denoising step using Singular Value Decomposition (SVD) [37, 38]. The waveform was first reshaped and decomposed as X ′ i = U Σ V ⊤ , where U , Σ , and V represented the singular vectors and singular values. The first k components were retained to reconstruct the waveform.

Encoder: The denoised waveform X ′ i, recon was fed into a Gated Recurrent Unit (GRU) encoder [39] (Fig. 4), which processed the input sequentially. The final hidden state h T served as the learned representation f ( X i ) , encapsulating the spatiotemporal features of the waveform for subsequent clustering tasks.

Clustering: After obtaining the waveform representation, we applied dimensionality reduction using UMAP [40] before clustering (Fig. 4). We tested two clustering approaches: a parametric algorithm, Gaussian Mixture Model (GMM) [41], and a non-parametric one, Mean Shift (MS) [42].

4.4 Overall Spike Sorting Pipeline

We established a cascaded spike sorting pipeline (Fig. 1). During inference, raw multi-channel signals were processed through spike detection, waveform extraction, embedding generation, and clustering, yielding distinct neuronal units and their spike timestamps.

5 Results

With the large-scale dataset and a pretraining framework, we try to answer the following research questions (RQ).

RQ1: Can the model trained on our simulated dataset generalize to real-world spike sorting tasks?

RQ2: If the model performs well, what are the crucial underlying components?

RQ3: Does data scale hold the key of effective spike sorting?

5.1 Benchmarks

Since the models of SimSort has been trained on our simulated dataset, readers must be curious about how well it can generalize to real-world spike sorting tasks. To empirically investigate this problem, we respectively evaluate SimSort in the following benchmarks:

Simulated test dataset (with ground truth): BBP L6 dataset from our simulation (using different neuronal models from the training dataset).

Real-world data-based synthetic datasets (with label): Hybrid [43], WaveClus [44], IBL Neuropixels [21]. We evaluate both zero-shot (using only our simulated dataset) and fine-tuned (using also the dataset) performance of SimSort.

Real-world neural recording (w/o ground truth, indirect validation): extracellular recordings collected from the mouse primary visual cortex during drifting grating stimulation.

The detailed information of benchmark datasets, hyper-parameters of models, and formulation of the evaluation metrics can be found in Appendix A.

Evaluation Metrics . To evaluate the performance of the overall spike sorting and spike detection tasks, we used Accuracy, Recall, and Precision as the evaluation metrics. Note that these metrics are specifically defined for the spike sorting context, where they measure the alignment between detected spikes and ground truth spike events, rather than their conventional definitions in classification tasks. For the spike identification task, we used the Adjusted Rand Index (ARI) [45] to measure clustering accuracy relative to the ground truth.

5.2 Spike Sorting Results on simulated dataset

We first examined Simsort on a simulated test set (BBP L6). In Table 1, we evaluated SimSort alongside widely used algorithms, including KiloSort [17], KiloSort2, MountainSort4 [6], and MountainSort5, implemented via the SpikeInterface pipeline [46], on the simulated BBP L6 dataset. SimSort achieved the best performance across all metrics, demonstrating its robustness and strong zero-shot generalization ability under idealized, simulated conditions.

5.3 Spiking Sorting results on real-world data-based synthetic datasets

zero-shot generalization Given SimSort's strong performance on simulated data, we extended our evaluation to real-world data-based datasets to further test its zero-shot generalizability and robustness (RQ1). On the Hybrid dataset, we compared SimSort against several algorithms via SpikeForest, including HerdingSpikes2 [18], IronClust, JRClust, KiloSort [17], KiloSort2, MountainSort4 [6], SpykingCircus, and Tridesclous. The results are summarized in Table 2 and Fig. S1. On this dataset, SimSort demonstrated the highest Accuracy and Precision in static recordings and achieved the best Accuracy and Recall under drift conditions, although it slightly trailed KiloSort in Precision.

For WaveClus dataset, we used SpikeInterface pipeline to run KiloSort, KiloSort2, MountainSort4, and MountainSort5. Results are provided in Table 3 and Fig. S1. SimSort performed reliably across both easy and difficult subsets, showing a pronounced advantage on the challenging 'difficult' set. This performance was likely due to its ability to handle the unique challenges of this subset, such as lower SNRs and reduced inter-class waveform variability, which resulted in significant overlaps between units. These results underscored SimSort's effectiveness in noisy and complex scenarios.

Fine-tuning Beyond demonstrating SimSort's zero-shot transfer ability on unseen datasets, we also investigate its performance after fine-tuning on a small amount of data from the target domain (Table 4). While the overall improvements in sorting metrics are modest, we observe notable changes in spike detection performance, particularly in the balance between Precision and Recall. These changes suggest that fine-tuning helps the model adapt to dataset-specific waveform and noise characteristics, enhancing its practicality with minimal labeled data.

Table 4: Performance metrics of SimSort after fine-tuning on different datasets.The hybrid-static and hybrid-drift datasets were fine-tuned on recordings 1-3 and tested on recordings 4-9 (train/test set split). The waveclus -easy and -difficult datasets were fine-tuned on subsets easy1/diff1 and tested on easy2/diff2, respectively. The IBL Neuropixels dataset was fine-tuned on 100 neurons and evaluated on 50 test sets, each with 10 randomly selected neurons.

5.4 Real-World Neural Recording

Beyond quantitative benchmarks, we further assessed SimSort in a real experimental setting. Specifically, we applied it to extracellular recordings (collected in our wet lab, see Appendix A.1.2) from the mouse primary visual cortex obtained using tetrodes during presentation of full-field drifting gratings in 8 orientations (Fig. 5a). From a representative recording session, SimSort identified six putative single units (Fig. 5b). These units exhibited distinct spike waveforms and their autocorrelograms showed clear refractory period structure(Fig. 5c), suggesting good isolation. We further analyzed the orientation tuning of these units. Several exhibited selective responses, with global orientation selectivity index (gOSI) values exceeding 0.2 (Fig. 5d; Appendix A for definition). Across 10 tetrode recordings, SimSort isolated a total of 40 units, with a mean gOSI of 0.27 ± 0.14 (mean ± S.D., Fig. S17). These results demonstrate the practical applicability of SimSort to real neural recordings.

5.5 Detailed Analysis

To explain why our framework achieved higher overall spike sorting accuracy (RQ2), we dissected SimSort and analyzed each component, i.e., spike detection and spike identification models. Furthermore, we presented some case studies of the latent embedding of spikes from multiple neurons.

(a)

(b)

(c)

Unit 1

Unit 2

Acquisition

System

Spike count

Drifting gratings

.2

20

0.04 0.02 0.00 0.02 0.04

0.04 0.02 0.00 0.02 0.04

UMAP 2

0

Spike count

2 90

40

Unit 4

10

0.04 0.02 0.00 0.02 0.04

0

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Spike count

60

8 orientations.

0.04 cpd, 2 Hz

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UMAP 1

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gOSI: 0.25

270°

gOSI: 0.14

270°

gOSI: 0.40

270°

gOSI: 0.18

270°

gOSI: 0.37

270°

Spike Detection We first compared our detection model to a commonly used threshold-based detection method (see Appendix A for details). The results are shown in Table 5a. SimSort's spike detector consistently outperformed the threshold-based method across all metrics on the Hybrid dataset. In the static condition, SimSort improved accuracy by ∼ 18 % , recall by ∼ 18 % , and precision by ∼ 1 % . Under the drift condition, the improvements were ∼ 13 % in accuracy, ∼ 17 % in recall, and ∼ 1 % in precision. It is important to note that the performance of the Threshold detector was highly sensitive to the choice of threshold, with results varying significantly across different settings. The results shown here were obtained under the best-performing thresholds. In contrast, SimSort achieved its superior performance without manual tuning, demonstrating its robustness and adaptability to varying recording conditions. These results indicated the effectiveness of our detection model, providing a solid foundation for the overall performance of SimSort in spike sorting tasks.

(a) Spike detection results. The threshold detector used the best-performing threshold by grid search.

(b) Spike identification results. All results averaged across 20 repeats of GMM.

Spike Identification After detecting spikes, our spike identification model extracted waveform representations in the latent space, enabling subsequent clustering. To effectively demonstrate the superiority of our identification model, we assembled a waveform dataset by gathering waveform data from each neuron in every hybrid recording. Using this dataset, we compared the 2D UMAP embedding of our model's inferred representations with features extracted directly from waveforms using widely adopted dimensionality reduction techniques, including PCA [23], t -SNE [24], and UMAP [40]. For evaluation, we applied a parametric clustering method, GMM [41], to compute the Adjusted Rand Index (ARI), providing a quantitative measure of the reliability of the representations.

As shown in Table 5b, SimSort substantially outperformed these conventional methods under both static and drift conditions, achieving the highest average ARI values of 0.91 and 0.89 , respectively. These results revealed the robustness and effectiveness of our approach in deriving meaningful and reliable representations from extracellular data.

Additionally, we compared SimSort with CEED [21], a recent framework for representation learning on extracellular data, using the author-provided model. Note that CEED is trained on datasets derived from IBL DanLab DY016 and DY009 Neuropixels recordings, which are more closely related to our test sets. We evaluated both SimSort and CEED on fifty test sets from the IBL Neuropixels dataset (specifically derived from the IBL CortexLab KS046 recording). As presented in Table S4, Fig. S2, S3, our method, without training on the dataset, achieved performance comparable to CEED.

Case Studies: In Fig. 6, we visualized the results of spike identification on an example recording in the Hybrid static subset. SimSort's embeddings (first column) demonstrated superior clustering performance, with a GMM clustering ARI score of 0.90, closely aligning with true labels. In contrast, UMAP, t -SNE, and PCA embeddings (second to fourth columns) yielded lower clustering scores of 0.80, 0.78, and 0.67, respectively. SimSort embeddings showed clear separation between neuron clusters, while other methods exhibited significant overlap, highlighting the advantages of SimSort in learning robust and discriminative features.

Impact of Dimensionality Reduction and Clustering Methods: To evaluate the adaptability of SimSort, we conducted a study to assess the impact of different dimensionality reduction techniques (PCA, t -SNE, UMAP, and None) and clustering algorithms (GMM, KMeans [47], Mean Shift [42], and Spectral Clustering [48]). Dimensionality reduction was applied to latent features extracted from waveforms, with an additional setup where clustering was performed directly on the raw latent features without dimensionality reduction (None). As illustrated in Fig. S4, the combination of UMAP and Mean Shift achieved the highest accuracy on both hybrid static and drift subsets. Across many other combinations, SimSort also maintained competitive performance, demonstrating its flexibility and robustness to various configurations.

SimSort embedding (UMAP),

Raw feature (UMAP),

Raw feature (TSNE),

Raw feature (PCA),

GMM Pred Labels 0.90

GMM Pred Labels 0.80

GMM Pred Labels 0.78

GMM Pred Labels 0.67

20

·1

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apeglly

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40

5.6 Scaling Law of Data Size

As we emphasized the importance of a large-scale dataset throughout this work, it was interesting to evaluate how SimSort's performance scaled with the size of the training dataset (RQ3). Specifically, we evaluated performance across sorting accuracy, detection accuracy, and identification ARI score, capturing how each metric varied with the growing size of the training dataset, which ranged from 2 8 to 2 13 trials. As Fig. 7 shows, larger datasets led to almost consistent performance improvements on both hybrid static and drift subsets, reflecting that the large-scale dataset holds the key of SimSort's effectiveness.

2 8

2 9

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2 11

2 12

2 13

Data Size

0.40

0.45

0.50

0.55

0.60

0.65

0.70

Sorting Acc - static

Sorting Acc - drift

Detection Acc - static

Detection Acc - drift

Identification ARI - static

Identification ARI - drift

0.65

0.70

0.75

0.80

0.85

0.90

ARI

6 Conclusion

In this paper, we presented SimSort, a data-driven framework for automated spike sorting, built upon a biologically realistic large-scale extracellular dataset. SimSort achieves competitive or superior performance compared to commonly used spike sorting algorithms on both simulated and real-world benchmarks, demonstrating its robustness and scalability. Evaluations further highlighted its zero-shot transfer capability.

SimSort has limitations. The models were trained on 4-channel tetrode data. While SimSort shows promising performance on tasks with high-density probes (IBL Neuropixels), future work should extend the training with diverse electrode geometries.

Acknowledgments and Disclosure of Funding

This work was supported by AI for Science Foundation of Fudan University (FudanX24AI046 to Y.G.) and Microsoft Research.

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Broader Impacts

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A Implementation Details of SimSort

A.1 Data Preparation

A.1.1 Train datasets

In Sec. 4.1, we introduced the preparation of our simulated training dataset, divided into the continuous signal dataset and the spike waveform dataset.

The continuous signal dataset was constructed by concatenating multiple simulated trials (e.g., 8192 trials), each containing 6,000,000 timesteps (dt = 0.1 ms) of extracellular data. Spike labels were derived by identifying peak times of intracellular action potentials and assigning binary labels to the corresponding region. This dataset, therefore, provided labeled, continuous extracellular signals for training the detection model and evaluating the overall spike sorting performance of SimSort.

The spike waveform dataset was constructed by extracting waveform segments centered on labeled spike events. For each spike, a 60-timestep waveform was segmented from the extracellular signals, and up to 400 such waveforms were collected per unit. Each waveform was then paired with its corresponding unit label, creating a dataset dedicated to training and evaluating the identification model.

A.1.2 Test datasets

BBP L6 dataset (simulation) . This dataset comprised 20 simulated trials, generated as stated in Sec. 4.1, using BBP layer 6 neuron models. It provided 5 ground-truth units per recording for evaluating spike sorting performance.

Hybrid dataset . This dataset was composed of recordings provided by Spikeforest [43]. These recordings were generated by Kilosort, with waveform templates recorded at a 5 µm electrode spacing and 1/f background noise. It consisted of two subsets: 9 static tetrode recordings and 9 drift tetrode recordings that simulated sinusoidal probe movements. For each recording, we selected units with an SNR greater than 3 (unit IDs meeting this criterion were provided by SpikeForest to ensure a fair comparison), comprising 10-15 ground-truth units per recording. The dataset we prepared supported two modes of use. To evaluate overall spike sorting and detection performance, we used the raw data as input. For assessing identification performance, we constructed a waveform dataset by extracting 500 waveforms (60-timesteps length) per unit.

WaveClus dataset . This dataset consisted of four subsets (8 recordings in Easy1, 4 recordings in Easy2, 4 recordings in Difficult1, and 4 recordings in Difficult2). Each recording was generated by combining spike waveform templates derived from experimental recordings with background noise, with noise levels ranging from 0.05 to 0.4, and contained 3 ground-truth units. To increase the overall difficulty and adapt the original single-channel recording for tetrode analysis, we preprocessed the signals by duplicating them and modifying them with small random noise (standard deviation 0.01), amplitude scaling (ranging from 1 to 0.5), and random temporal shifts (-5 to +5 samples).

IBL Neuropixels dataset . This dataset was derived from a real-world extracellular recording, CortexLab KS046, published by the International Brain Laboratory (IBL). This recording was captured with a Neuropixels 1.0 probe across multiple brain regions from a mouse performing a decision-making behavior task. To compare spike identification performance with CEED, we prepared this dataset following the method described in the CEED paper (CEED trained and evaluated datasets derived from DanLab DY016 and DY009 Neuropixels recordings in the IBL database). We extracted waveforms from 400 units detected by Kilosort 2.5 and created fifty test sets for comparison between SimSort and CEED. Each test set contained 10 random neurons and 100 spikes per neuron. Note that CEED inputs 11-channel data into the model, while SimSort processes data from the center 4 channels.

Real-world Neural Recording . These recordings were collected by our wet lab (approved by research ethics committee). A 16-channel tetrode was implanted unilaterally in the primary visual cortex (V1) of mice. During the recording sessions, the mice were head-fixed, and the contralateral eye was stimulated with drifting grating stimuli of 8 different orientations. The stimuli were presented in a randomized sequence over 8 repeated trials. Each grating had a spatial frequency of 0.04 cycles per degree (cpd) and a temporal frequency of 2 Hz, lasting for 1.5 seconds per trial.

To quantify the orientation tuning of individual neurons, we computed the global orientation selectivity index (gOSI), as defined in the equation below:

where θ is the direction of the moving grating and R ( θ ) is the neuronal response to direction θ . The imaginary unit is denoted as i whose square is -1 . The gOSI ranges from 0 to 1, with 0 indicating no orientation selectivity (equal response to all directions), and 1 indicating maximal selectivity (response only to a single orientation).

A.2 Technical Details of Simulation

Intracellular Simulation . Intracellular electrophysiological activity was simulated using the NEURON package [32, 49]. Pink noise, scaled to the rheobase (the minimum current needed to elicit an action potential in a neuron), was injected into each neuron's soma to evoke stochastic action potentials, simulating biologically realistic background noise. The temporal resolution was set to 0.1 ms, and simulations spanned a total duration of 600 seconds. Neurons were initialized with a resting membrane potential of -70 mV and maintained at a physiological temperature of 34°C. Synaptic mechanisms and biophysical properties were dynamically compiled and loaded to ensure compatibility with the NEURON simulation environment.

For each neuron, a multi-compartmental model [29, 30, 50, 51] was used to calculate transmembrane currents [52, 53, 54, 55]. The neuron was divided into compartments, each described as an equivalent electrical circuit. The dynamics of the membrane potential in each compartment n were governed by Kirchhoff's current law [56], where the sum of all currents entering or leaving a node must equal zero. Considering that extracellular potential changed much slower than ion channel dynamics, we assumed it constant. The temporal evolution of the membrane potential V n in compartment n was given by g n,n +1 ( V n +1 -V n ) -g n -1 ,n ( V n -V n -1 ) = C n dV n dt + ∑ j I ( j ) n , where g n,n +1 and g n -1 ,n represented the conductances between neighboring compartments, C n was the membrane capacitance, and ∑ j I ( j ) n accounted for ionic currents and any externally applied currents.

These transmembrane currents served as the source for extracellular potential modeling in the next simulation stage.

Extracellular Simulation . Extracellular potentials were modeled using the volume conductor theory, which links transmembrane currents to extracellular potentials recorded at electrode sites [57, 58, 59]. In each simulation trial, five neurons were randomly selected from the available models and positioned within a 100 × 100 × 100 µ m 3 cubic space. These neurons consisted of different types to represent the diversity of cortical microcircuits. A virtual tetrode electrode with four recording sites was placed randomly within the same space to capture extracellular potentials.

The extracellular potential at a given electrode site r e was computed as the sum of contributions from all neuronal segments within the simulation space: ϕ ( r e , t ) = 1 4 πσ ∑ N n =1 ∑ M n m =1 I n,m ( t ) | r e -r n,m | , where

I n,m ( t ) was the transmembrane current of the m -th compartment of the n -th neuron, r n,m was its position, σ is the extracellular medium's conductivity, and | r e -r n,m | was the Euclidean distance between the segment and the electrode. Here, N was the total number of neurons, and M n was the number of compartments in the n -th neuron. A reference electrode far from the source was assumed, setting ϕ = 0 at infinity.

A.3 Spike Detection

Data Augmentation For each input segment X i ∈ R T × C , where T represents the number of time points and C denotes the number of electrode channels, a subset of channels C ⊆ { 1 , 2 , . . . , C } was randomly selected. A probability p ∈ [0 , 1] determined whether the following augmentations were independently applied to each channel k ∈ C :

where ϵ ∼ N (0 , σ 2 ) represents Gaussian noise, β ∼ U (1 -s, 1 + s ) is a random amplitude scaling factor, and ϵ t ∼ U ( -δ, δ ) introduces small temporal shifts. The parameter n is an up-sampling factor enabling finer time resolution.

Model-Based Detector In Sec. 4.2, we introduced a model-based detector to predict spike events. These predictions were processed through the following steps:

Detection with Model: The model took as input a multi-channel signal V ∈ R t × c , where t was the number of time samples and c was the number of channels. This input was processed simultaneously across all channels by the detection model, which predicted a sequence of binary labels ˆ y i ( t ) for each time point:

where f model represented the detection model. The output ˆ y i ( t ) indicated the presence or absence of spike events at each time point.

Adjacent Peak Merging: The predicted labels ˆ y i ( t ) were refined by merging adjacent peaks to reduce false positives and consolidate overlapping detections. To identify the most relevant peaks, the channel with the maximum summed absolute signal across all time points was selected:

where V ( t, c ) denoted the signal value at time t for channel c . Using this peak channel, adjacent peaks were grouped within a window size ∆ t , and only the time point with the maximum absolute signal was retained within each group:

where [ t s , t e ] denoted the boundaries of a group of adjacent peaks.

Spike Peak Extraction: After merging, the final spike peak positions { t k } were determined as the set of time points where ˆ y ′ i ( t ) = 1 :

Threshold-Based Detector In Sec. 5.5, we compared our model-based detector with a thresholdbased detector. To ensure a fair comparison, the processing steps for the threshold-based detector were designed to align closely with those of the model-based detector. The threshold-based method processed the signal V ∈ R t × c , where t was the number of time samples and c was the number of channels. Each channel was processed independently, as follows:

Local Maxima Detection : For each channel c , the signal V ( t, c ) was scanned to identify local maxima that satisfied the following conditions:

where Th single_ch was the amplitude threshold, and r ∈ { 1 , . . . , loc_range } . These conditions ensured that the detected peaks were above the threshold and were local maxima within the range r . Peaks identified by this criterion were further validated within a broader range long_range to ensure they represented significant spike events.

Adjacent Peak Merging : For each channel c , adjacent peaks were grouped within a window size ∆ t , and only the time point with the maximum absolute signal was retained within each group:

where [ t s , t e ] defined the boundaries of a cluster of adjacent peaks. This step ensured that closely spaced peaks were consolidated into a single spike.

Spike Peak Extraction : After processing all channels independently, the final detected spike positions for channel c were determined as:

A.4 Spike Identification

Waveform Snippet Extraction . For each detected spike at position t k , a waveform snippet W k ∈ R L × c was extracted from the multi-channel continuous signal V ( t, c ) , where L was the snippet length (number of time samples) and c was the number of channels. The snippet was centered around t k and spanned a time window determined by the pre- and post-padding values:

where V ( t, c ) represented the input signal value at time t for channel c , and p pre and p post were the padding values (in time samples) before and after the spike time t k , respectively.

A.5 Definitions of Metrics

To evaluate the performance of spike sorting algorithms, we adopted standard metrics as defined in the context of spike sorting and ground-truth comparisons [43, 46]. These metrics included accuracy , precision , and recall , which were derived based on the matching between the sorted spike train and the ground-truth spike train.

Matching Spike Events: For a given sorted spike train k and ground-truth spike train l , the first step was to calculate the number of matching events ( n match l,k ). A sorted spike event s ( k ) j was considered a match to a ground-truth event t ( l ) i if the absolute time difference was less than or equal to the predefined matching window ∆ , which was typically set to 1 millisecond:

Missed Events and False Positives: Using the number of matching events, we computed the number of missed events ( n miss l,k ) and false positives ( n fp l,k ) as follows:

where N l was the total number of ground-truth events in l , and M k was the total number of sorted events in k .

Accuracy: Accuracy ( a l,k ) balanced the contributions of both missed events and false positives and was defined as:

Precision and Recall: The precision ( p l ) and recall ( r l ) were calculated based on the best matching sorted unit ˆ k l for each ground-truth unit l . The best match was determined as the sorted unit k with the highest accuracy:

To identify the best matching sorted unit ˆ k l for each ground-truth unit l , the Hungarian Algorithm was applied to establish the best one-to-one correspondence between sorted and ground-truth units. Using this best match, precision and recall were defined as:

A.6 Hyperparameter Search

We performed a grid search to optimize the hyperparameters for the SimSort detection and identification models. The hyperparameter ranges included the number of layers, hidden size, learning rate, number of attention heads, dropout rate, and augmentation settings. The final selected values are presented in Tables S2 and S3.

A.7 Infrastructures

Our simulation dataset are generated using CPU clusters for approximately 5000 CPU hours, which can be parallelized. Our models are trained on NVIDIA A100/V100 GPU, approximately 20 GPU hours for each time.

B Supplementary Figures

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C More Showcases

In this section, we present additional visualizations from various datasets to provide a more comprehensive evaluation of SimSort's performance. These visualizations include examples of spike detection, identification, and sorting tasks. The supplementary showcases illustrate key aspects such as the alignment between detected and ground-truth spikes and the clustering of spike waveforms.

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UMAP 1

0

-1.5 1.0 0.5 0.0 0.5 1.01.5

BBP_L6-7

BBP_L6-8

BBP_L6-9

1.5

1.0

-1.0

1.5

2.0

1.5 1.0 0.5 0.0  0.5 1.0 1.5 2.0

BBP_L6-10

I evwr

UMAP 1

BBP_L6-11

BBP_L6-12

.8

a

1.5

1.5

UMAP 1

BBPL6-13

BBP_L6-14

BBP_L6-15

1.5

2

1.5

1.0

1.0

0.5

0.5

0.5

0.0

0.0

my

0.0

0.5

1.0

1.5

1.5

2.0 1.5 1.0 0.5 0.00.51.01.5

IRAP 1

UMAP 1

BBP_L6-16

BBP_L6-17

BBP_L6-18

L.5

1.0

0.5

0.0

0.5

1.5 1.0 0.5 0.0 0.5

1.01.52.0

UMAP 1

BBP_L6-19

BBP_L6-20

1.0

0.5

1.0

-1.5

2.0

1.0

1.5

1.0

-0.50.00.5

1.01.5

UMAP 1

2.0

UMAP 1

Unit 2

05: 0.28

05: 0.4)

1.0

1.5

1.0 0.5 0.0 0.5 1.01.5 2.0 2.5

IMAP 1

Unit 2

90*

JMAP 1

Unit 3

905: 0.19

E'0 I506

-2.0

1.0

2.5

1.0 0.5 0.0 0.5 1.0 1.5 2.0

UMAP 1

nit 2

Unit 3

g05: 0.10

909: 6.33

1.0

1.5

1.00.5

0.00.5

UMAP 1

Jnit 1

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose to pretrain a neural network for spike detection on a large simulated dataset, before using a contrastive learning approach to cluster spikes. They show improvements upon existing spike sorters on hybrid datasets, and show the results of SimSort on a real tetrode dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: Novel use of pretraining for spike sorting, novel combination of methods for spike sorting (clustering method is inspired from CEED, which has not been used for spike sorting so far), good evaluation on hybrid datasets, zero-shot vs fine tuning analysis + Scaling Law of Data Size are interesting analysis. \n\nWeaknesses: Lack of comparisons to existing spike sorters on real-world data, only low-dimensional real world dataset (tetrode) is considered, generalization capabilities are not clearly demonstrated (hybrid datasets are not necessarily \"out of distribution\" + real world dataset is fairly simple), limitations are not properly discussed (only one limitation is highlighted).\n\nQUESTIONS:\nOne question concerns the evaluation on real data. \n\nI am surprised it does not catch more low amplitude spikes (figure 5). It's surprising that even the lower amplitude unit does not violate the ISI. these spikes are very hard to accurately cluster, but can still be very informative (Multi Unit Activity). Have you tried to quantify how many spikes it is missing during detection? \n\nThose questions could be adressed by comparing to an existing sorter on the real world data. If SimSort finds as many spikes as Kilosort, then I would find it convincing. You could, for every unit, produce a venn plot + ISI + waveform plots comparing the output of SimSort and KS. Spike Interface provides a way to compare two spike sorters. More generally, I think having a comparison to KS on real data is necessary and I would greatly improve the quality of the paper and I would be willing to improve my score if authors show this during the rebuttal period.  \n\nHave you checked whether or not there is drift in your recording? it is possible maybe that units 2 and 6 are the same unit separated by drift? I think running DREDGE (Windolf et al., 2025) to preprocess the data, or just to quantify the amount of drift would greatly improve your preprocessing / subsequent analysis, especially as no augmentation in your contrastive approach accounts for drift. \n\nI am generally against whitening the recording - as the noise is rarely gaussian and removing dependencies between channels can hurt the performance downstream. Have you tried other preprocessing pipelines? And quantified their effects? \n\nWhy not have tried the full spike sorting pipeline on IBL Neuropixels dataset (rather than just selecting spikes from good units and clustering them with the contrastive approach)? If you have the data + pipeline, can't you run it on the IBL dataset and compare to KS (which results are available on the IBL datasets)? I would suggest adding localization features to the contrastive learning feature to perform clustering.\n\nLIMITATIONS:\nI believe that the limitations are not properly adressed by authors. The only limitation mentioned is that SimSort has only been trained and tested on tetrode data. I agree that this is a major limitation, but have other concerns. \n\nIt is not clear whether or not SimSort is able to generalize outside of the training data. More precisely, it may capture only \"easy\" spikes. Figure S8 is misleading as it shows the \"missing GT\", but hybrid recordings have more spikes than the injected spikes. Not finding any additional spikes si concerning. Especially, looking at the raw traces, there's other events that could look like small spikes. \nAgain, here the evaluation of detection should be complemented by a comparison to other sorters. Only finding hybrid unitys would be indicative of bad performance, although the metrics would be good. Could the authors clarify whether or not the sorter learns additional units, and evaluate the quality of these units? \nThe training dataset also seems to have very high SNR (figure S10). Why not increase the noise here to match real recordings?\n\nAuthors claim to report experiment statistical significance (checklist) but this is not done in table S4. I think authors should indicate there if there's no statistically significant difference. Also, this table, in my opinion should be moved to the main text.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper describes a dataset generated with a biophysical simulation leveraging neuron models published by the BBP in 2011 to generate a synthetic spike-sorting dataset using the neuron simulator. The dataset simulates 6 cortical layers and a 4-channel tetrode inserted inside the cortex model. In the simulation for generating the data, the cells are not recurrently connected, and they are stimulated with independent noise currents (this is my understanding; it could be wrong because the information is not very clearly stated). I could not find details on the morphology and 3D placement (missing information from the main text). The resulting dataset consists of 8k trials of 10minutes and a total of 40k units.\n\nThe synthetic dataset is then used to pre-train the proposed SimSort algorithm, which is a deep learning spike sorting algorithm. It is composed of two parts, a transformer (encoder only) which takes extra-cellular recordings sampled at 0.1ms and determines the presence/absence of a spike at each time via binary classification. The second model is a GRU model, which takes the waveform as input and returns an embedding that is used to determine the spiking cell identity.\n\nThe SimSort algorithm is tested zero-shot (without fine-tuning) on other spike sorting datasets (these are also synthetic datasets, but created differently). Without fine-tuning, the model already outperforms other spike sorting models and algorithms on most metrics, which is remarkable. When fine-tuned, the model is a bit better. Unsupervised clustering of cell type in layer 6 appears possible even if these synthetic neuron models were not used identically in the training set.\n\nSTRENGTHS AND WEAKNESSES:\nMain comment:\nThe paper is excellent and is undoubtedly making some progress for deep spike sorting algorithms, but details and citations are missing from the main text about the synthetic dataset. It should be stated much more prominently that the model does not model recurrent interaction and that the neurons are driven by independent pink noises (no recurrent interaction is simulated; in the absence of details, I also assume that no morphology is simulated). Note that other recent papers (including from the BBP team and elsewhere have gone deeper to generate realistic data for spike sorting, and this should be acknowledged [1]. I believe being more precise and transparent about its dataset in the main text will not undermine the validity and the strength of the paper, which has already achieved a lot by combining a deep spike sorting algorithm with a reasonably simple synthetic dataset. The simplicity of the dataset is rather a strength in this case. \n\nStrengths:\n- This paper is timely and follows a current trend that is likely to change the field. Synthetic data for BCI will become very important.\n- The paper is very clear and well written, and the code appears to be detailed and exhaustive.\n- SimSort is outperforming all existing spike sorting algorithms (even without fine-tuning), and everything indicates that the algorithm is ready to be used for wet labs (demonstration on in-house data, although without groundtruth spike times, which is very very difficult with in vivo data). \n- The model training is explained efficiently but with great clarity, everything is understandable, and given access to the dataset, it appears to be reproducible.\n\nWeaknesses:\nA) A1. The paper is very clear and well written. Even the appendix is well written, for what I saw. Maybe the appendix sections could be more appropriately referenced in the main text in a relevant place to inform the reader of their existence.\nA2. The font size of the figures is often too small. It makes the Figure impossible to read depending on the support. This is easy to fix, and a pity given the high quality of the paper and the efforts invested in everything else.\n\nB)\nB1. The dataset itself is not the best of its sort since the BBP group recently [1] (important missing citation) published a more advanced model to study spike sorting, which includes, in particular, recurrent interactions and a careful 3D placement and connectivity that are not commented on in this model. I assume that the simulation does not even need to simulate all cells together, since there is strictly no interaction between cells. If so, it would be useful to clearly state this limitation, rather than letting the reader find out. To be fair, this is not even a simulation; the simplicity of independent neuron simulation is rather a strength than a limitation.\n\nB2. The 3D morphology and recurrent connections are important for accurate spike sorting because of spike synchrony and because the waveform is impacted by the spatial position of cells [1] (should be acknowledged as a limitation).\n\nB3. It would be useful for the reader to know that trials in the dataset are 10 minutes long (basically, Table S1 could be put in the main text, or at least give the trial duration and time step clearly in the main text and point to Table S1 and the appendix for details).\n\nB4. Missing information in the main text: I assume that the neurons are simulated completely independently, and the waveforms are added depending on the 3D placement of the neuron. This could be clarified in the main text. Also related: how many neurons are simulated \"in the same 3D volume\" around an electrode at the same time?\n\nB5. Is it possible/frequent that two neurons in the same time bin can emit a spike in the same time bin? How is this case handled?\n\nC) I really like the description of the spike sorting algorithm, but I missed a better summary of the related deep learning models for spike sorting. For instance, is it standard to do spike detection with binary classification (spike event)? Which papers have already used deep learning to classify waveforms? (Without supervision?)\n\n\n\n\n[1] Laquitaine et al. 2025\nhttps://www.biorxiv.org/content/10.1101/2024.12.04.626805v2\n\nQUESTIONS:\nScientific questions:\n- At first sight, the dataset appears extremely satisfactory, even if the morphology and connectivity are not simulated. What additional benefits or difficulties should we encounter with more complex and detailed models?\n- What are the failure modes expected when training with this dataset (interfering spike forms, distant neurons, which are causing problems that are specific to this synthetic dataset)?\n- What are the challenges for validating the model, and what type of data should be included to test the limitations of the model?\n\nIn coherence with the weaknesses above, I would suggest putting the missing information about the dataset in the main text:\n- 3D placement of the neuron\n- absence of morphology?\n- absence of recurrent connection? Is the pink noise the only drive?\n- how many neurons are potentially simulated around a single electrode?\n- The 10-minute trial duration should be stated in the main text.\n- Added reference to Laquitaine et al. 2025 would be quite important in my opinion to reflect the context of synthetic datasets from the BBP. It would also be relevant to cite more advanced modeling papers that have put a lot of effort into modeling realistic networks (this includes Markram 2015 [2] and work from the Allen Institute [3]; other relevant models are welcome, of course).\n- Adding a bit more on the related work on deep networks for waveform clustering, and deep networks for spike sorting would be useful.\n\n\n[2] Markam et al. 2015\nhttps://www.cell.com/cell/fulltext/S0092-8674(15)01191-5?innerTabauthor-interview_mmc9=\n\n[3] Billeh et al. 2019\nhttps://www.cell.com/neuron/fulltext/S0896-6273(20)30067-2?dgcid=raven_jbs_etoc_email\n\nLIMITATIONS:\nSee above, no potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces SimSort, a deep learning-based framework for spike sorting, which is the process of identifying and classifying electrical signals from individual neurons in extracellular brain recordings. The goal is to overcome limitations of traditional heuristic methods, such as sensitivity to noise and manual parameter tuning, by leveraging biologically realistic neuron models (206 neuron types from the rat somatosensory cortex) and large-scale electrophysiology simulations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Uses a Transformer-based model trained on simulated continuous signals as a novel method, outperforming threshold-based methods。\n2. Employs contrastive learning to generate robust waveform embeddings, followed by clustering (e.g., UMAP + GMM).\n3. Zero-Shot Generalization: Achieves state-of-the-art performance on real-world datasets without fine-tuning.\n\nWeaknesses:\n1. The generalization of model could be explored more thoroughly, such as on different dataset, recording technique et al. For instance, the neuropixel/tetrode recordings with more channels.\n2. There's no comparison with kilosort3.5 and kilosort4 in Table 1-3 as they are the SOTA spikesorting methods widely used.\n3. Ablation studies are needed, as it's hard to see whether the good performance is from the transformer-based spike detection model, the spike identification model with constrastive learning, or denosing operation. For example, how will the performance be changed if using the spike identification results with threshold detector?\n\nQUESTIONS:\n1. The generalization of model haven't been explored enough, such as on different dataset, recording technique et al.\n2. There's no comparison with kilosort3.5 and kilosort4 as they are the SOTA spikesorting methods widely used.\n3. Ablation studies are needed.\n4. How different data types (for example, spikes from different areas) influence the model performance?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses a fundamental and crucial challenge in the field of neuroscience: Spike Sorting. The authors utilize highly accurate computational models from biophysics to create a large-scale, high-fidelity simulated extracellular recording dataset and propose a data-driven pre-training framework called SimSort, which achieves zero-shot transfer.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.Significant and highly original: The large-scale, high-fidelity dataset created by the authors, which they plan to make open-source, is a major contribution to the entire neuroscience community.\n2.The model architecture is well-designed, with Transformer used for spike detection (a sequence labeling task) and GRU + contrastive learning for spike identification (waveform representation learning).\n3.The experimental evaluation is rigorous, with tests conducted on multiple benchmarks, including a self-built simulated test set, publicly available mixed datasets, and real experimental data.\nWeaknesses:\n1.Limited innovation in the deep learning architecture itself: Although applying these techniques to spike sorting is novel, from a pure machine learning perspective, the model components used in the paper (Transformer, GRU, Triplet Loss, etc.) are existing and well-established frameworks.\n2.Lack of evaluation on inference speed. Tools like KiloSort are widely adopted not only for their accuracy but also for their extremely high processing efficiency. An analysis of SimSort's inference time should be conducted.\n3.The discussion on data scale-up laws is not deep enough. Figure 7 shows the model's performance as the training data scale increases, but based on the trend in the figure, the performance growth of multiple metrics (such as identification ARI) has shown signs of saturation. Does this mean that simply increasing the same type of simulated data no longer brings significant improvements? The authors could delve deeper into whether more diversified simulation data (from different brain regions, different electrode types) or improvements in the model architecture itself are needed to further break through the performance bottleneck.\n\nQUESTIONS:\n1.Regarding the gap between simulation and reality, although zero-shot transfer performs well, the SimSort model is mainly trained on simulated tetrode data. Its generalization ability to other electrode types (such as high-density Neuropixels probes) with vastly different geometric structures and noise characteristics may still be limited. Could you discuss which factors in the simulation process are key to successfully achieving zero-shot transfer?\n2.Regarding the choice of model architecture, you used Transformer for spike detection and GRU for spike identification. What was the initial intention behind this design? Does GRU have unique advantages over Transformer in representation learning for individual waveforms (shorter sequences)?\n3.How does the computational speed of the SimSort model during inference compare to traditional efficient algorithms like KiloSort? For researchers who need to process massive amounts of data, is it a practical choice in terms of speed?\n\nLIMITATIONS:\nSee Weaknesses and rebuttal questions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}