SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Authors: Yimu Zhang
Dongqi Han
Yansen Wang
Zhenning Lv
Yu Gu
Dongsheng Li
Venue: NeurIPS 2025 poster ()
Keywords: Spike Sorting
Neuroscience
Primary area: neuroscience_and_cognitive_science

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
Yimu Zhang Dongqi Han Yansen Wang²
Zhenning Ly!
Yu Gu
Dongsheng Li
Institutes of Brain Science, Fudan University
Microsoff Research Asia
(yimuzhang21, znlv23}0n @m. fudan. edu. (guyu Heuudan edu, en, {dongqihan, yansenwang,
com
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 challenging verify their correctness due 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 large- scale dataset through electrophysiology simulations using biologically realistic computational models. We then present SimSort, pretraining framework for spike sorting. Trained solely on simulated data, SimSort demonstrates zero-shot generalizability real-world spike sorting tasks, yielding consistent improvements over existing methods across multiple benchmarks. These results highlight the potential of simulation-driver 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 21, which crucial for exploring how information encoded, processed, and transmitted within neural circuits, as well as decoding the brain's 's functional dynamics 3 4 5
Recent advances neural recording technologics have enabled capturing the activity of neurons across multiple regions of the brain with high spatial and temporal precision (6 extracting meaningful information from recordings relies on critical step known as spike sorting.
7 g 9.. Notably,
Spike sorting is the process of extracting and identifying neural activity from extracellular recordings. involves two main steps (Fig. [D: spike detection, which extracts spike events from background noise, and spike identification, which assigns these detected spikes to individual neurons [1O]. 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 [T], 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 [
'The work was conducted during internship Yimu Zhang at Microsoft Research Asia. Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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bhreshold-based
SimSort
Non-learning method
SimSort
method
Mamayp
volage throikass
pC
pasiiio
detected gikes
Input Signals
Detected Spikes
Identified Neurons
Spike Detection
Spike Identification
Figure 1: Pipeline of spike sorting, which consists of two main steps: spike detection and spike identification. For spike detection, typical spike sorting algorithms (left) utilize threshold-baid detector relies on fixed voltage thresholds on each channel, and use non-learning method like per- forming PCA-based clustering on concatenated waveforms for spike identification. Those approaches are sensitive to noise and require manual parameter tuning. In contrast, our proposed framework SimSort (right) use a neural network- detector replaces the threshold method for spike detec- tion, enhancing robustness and generalization. For spike identification, the feature embeddings of multi-channel waveforms learned from contrastive learning to improve clustering accuracy.
For long time, spike sorting predominantly relied on heuristic statistics and machine learning
6 18, 19]. While these methods have promoted neuroscience research, they exhibit several key limitations. First, their sorting results sensitive 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 learning- based methods, such as YASS [20 and CEED [21], have attempted to improve spike sorting by adopting data-driven approaches enhance detection and clustering accuracy. However, their generalizability and practical use are hindered by the limited training datasets.
approaches [
13
16
17
To address these limitations, we highlight the importance of using massive training data with ground- truth 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, present SimSort, a framework that utilizes data-driven approaches to enable fully automated spike sorting. By pretraining a spike detection model 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 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:
1) A publicly available, large-scale labeled dataset to help address the scareity of ground-truth data, supporting the development and evaluation of learning-based spike sorting methods.
2) 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.
3) An off-the-shelf pretrained model for neuroscientists to use" enabling fully automated spike sorting on Tetrode recordings without dependence on hand-tuned parameters.
Preliminaries
The goal of spike sorting (Figure is to extract single-neuron spiking activity from extracellular recordings, where the activities of an unknown number of neurons are mixed together. Mathematically,
"See httpss:///iiSSrttToolll github. 10 for the model, code and usage instructions.

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spike sorting is blind source separation problem [22 and can be formally defined as follows: Let V E RT represent the extracellular recording (voltage) across C' electrode channels over T time points. The goal is to infer set of spike times {4+] and corresponding neuron labels {yx {y+) ) where y E (1,...,N) 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 dimen- sionality reduction (c.g.. PCA 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 improve robustness, automation, and adaptability diverse experimental condi- tions. 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 [2I] applied a contrastive learning framework 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 experi- mental 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 1aa.Son2.5--prcessstt 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 applicabilityy and (4) did not optimize spike detection or provide complete spike sorting pipeline.
To overcome these limitations, we propose 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 100 x 100 x 100 μm3 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. Intra- cellular spike timestamps were recorded as ground-truth -ruth 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. was designed for training spike detection models and evaluating the overall performance of spike sorting pipelines.
3

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(a) Multi-compartmen neuron model
(c) Simulated Extracellular Recording
YASS
urkowa BeuEces
Electrod
CEED
$192 itale
112r 40,000
soina
Cxmpaattmenn
A large-schle simulated extraceluula dataset
(b) Simulation
Nose
CH
Pipett
Elecmode
Figure 2: Overview of the large-scale simulated extracellular dataset generation process. (a) Multi- compartment neuron models simulate detailed neuronal morphologies and electrophysiolgicall properties, incorporating realistic ion channel dynamics. (b) Noise generated by stochastic process injected into the somatic compartment to induce stochastic intracellular action potential firing, and extracellular signals are recorded using virtual electrodes placed near the neurons in simulated environment. (c) The resulting multi-channel extracellular recordings capture diverse and realistic neural activity.
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 neuron models exclusively for evaluation, ensured that the test set comprises previously unseen neuron types and configurations, allowing to effectively assess the model's generalization capabilities.
4.2 Spike Detection
We formulate the spike detection task as identifying the temporal segments {tx corresponding to putative neural spikes from the raw input V E R'x where T represents the number of time points and C' denotes the number of electrode channels. The raw signal 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 Transformer-based ar- chitecture (33] for spike detection, trained 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:
Spike Detection
Detector
Continuous Signals
Random Angmensation
Probabditier
Figure 3: Spike detection model in SimSort.
T LACR - Σ
-Gt)). in)).
Up Be log d: + (1 ye loolll1 -
where the binary label y/ denoted spike events, Hr was the predicted probability, T' was trial length, and Wp was hyperparameter reweigh the positive predictions given the general scarcity of spikes.
Data Augmentation: To improve robustness and generalization in spike detection, data augmen- tation was applied to each input signal segment V' E T (Fig. 3}. A subset of channels C" C 2....... C} was randomly selected with a probability p to apply augmentation. Various augmentations, such adding noise, amplitude scaling, and temporal jitter, were applied to enhance the diversity of training data. The detailed implementation of these augmentations provided in Appendix

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4.3 Spike Identification
We formulate spike identification embedding each identified spike waveform X; E RAxC, 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, illustrated in Fig. 4 and described follows.
Contrastive Learning: We leveraged contrastive learning to learn represen- tations that capture the relationships between spike waveforms (Fig. + Contrastive learning sought to map neural data into embedding space where related examples were posi- tioned nearby, while unrelated exam- ples were placed further apart [34 35 For each waveform X,, con- structed a triplet by selecting X, with the same label and X, with a different label. The objective was 36
Spike Identification
Spike Waveforms
Contrasiive Leornnng
Ramdom Augreentation
Denoise
Encoder
Waveform
Clustering
Rep
Figure 4: Spike identification model in SimSort.
Cuplle max (0, [l,(()) f(x;)
f(X; 112 +
(2)
where f(X;) denoted the embedding of X,, and a was margin enforcing separation between positive- negative pairs. Additionally, we uniformly applied data augmentation 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.
The augmented waveform X E RR then underwent a denoising step using Singular Value Decomposition (SVD) 37 38).
Denoising:
The waveform was first reshaped and decomposed as X UEVT. where U, Σ, and V represented the singular vectors and singular values. The first components were retained to reconstruct the waveform.
The denoised waveform X __ was fed into Gated Recurrent Unit (GRU) encoder (39 (Fig. 4. which processed the input sequentially. The final hidden state lot served as the learned representation f(X,), encapsulating the spatiotemporal features of the waveform for subsequent clustering tasks.
Encoder:
Clustering: After obtaining the waveform representation, we applied dimensionality reduction using UMAP 40 before clustering (Fig. We tested two clustering approaches: parametric algorithm, Gaussian Mixture Model (GMM) 41 and non- non-parametric one, Mean Shift (MS) 42
4.4 Overall Spike Sorting Pipeline
We established cascaded spike sorting pipeline (Fig. D. 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 pretraining framework, we try to answer the following research questions (RQ).
RQI: 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 simulated dataset, readers must be curious about how well it can generalize to real-world spike sorting tasks. empirically investigate this problem, respectively evaluate SimSort in the following benchmarks:
5

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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 record- ings collected from the mouse primary visual cortex during drifting grating stimulation.
The detailed information of benchmark datasets, hyper- peper-parameters of models, and formulation of the evaluation metrics can be found in Appendix
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 simulated dataset
Table 1: Spike sorting results on BBP L6 dataset. The results for other methods were obtained through Spikelnterface [46]. Values are mean = S.E.M.
We first examined Simsort on a simulated test set (BBP L6). In Table I we evalu- ated SimSort alongside widely used algo- rithms, including KiloSort [17], KiloSort2, MountainSort4 [6]. and MountainSort5, im- plemented via the SpikeInterface pipeline (46), on the simulated BBP L66 dataset. Sim- Sort achieved the best performance across all metrics, demonstrating robustness and strong zero-shot generalization ability under idealized, simulated conditions.
BBP (20 trials)
Methods
Accuracy
Recall
Precision
KiloSort KiloSort2
0.49 1: 0.05
0.51 0.05
0.53 0.05
0.51 + 0.06
0.53 = 0.06
0.53 +: 0.06
MountainSort4
0.84 + 0.03
0.84 0.03
0.93 0.03
MountainSorr5
0.66 + 0.06
0.68 0.06
0.79 0.07
SimSort
0.85 + 0.02
0.90 0.01
0.93 0.02
Table 2: Spike sorting results on Hybrid dataset. Data for other methods were sourced from
SpikeForest [43]. Values are mean = S.E.M. Best results are in bold. Note that SimSort did not use this dataset for training, indicating a zero-shot generalization.
Hybrid-static (SNR=3, 9 recordings)
Hybrid-drift (SNR>3, recordings)
Methods
Accuracy
Recall
Precision
Accuracy
Recall
Precision
HerdingSpikes2
0.35 + 0.01
0.44 0.02
0.53 0.01
0.29 + 0.01
0.37
0.02
0.48 0.02
IronClust
0.57 + 0.04
0.81 0.01
0.60 + 0.04
0.54 0.03
0.71 0.02
0.65 + 0.03
JRClust
0.47 + 0.04
0.63 = 0.02
0.59 1 0.03
0.35 0.03
0.48 0.03
0.57 1 0.02
KiloSort
0.60 + 0.02
0.65 = 0.02
0.72
0.02
0.51 0.02
0.62 = 0.01
0.72 - 0.03
KiloSon12
0.39 - 0.03
0.37 0.03
0.51 0.03 0.03
0.30
0.31 0.02
0.57 = 0.04
MountainSort4
0.59 0.59 -: 0.02
0,73 0.01
0.73
0.36
0.57 0.02
0.6 0.03
SpykingCireus
0.01
0.63 0.01
0.75
0.55
0.68 0.03
Tridesclous
0.54 - 0.03
0.66 = 0.02
0.59 0.04
0.37 0.02
0.52 0.03
0.55 = 0.04
SimSort
0.62 1 0.04
0.68 0.04
0.77 1 0.03
0.56 1 0.03
0.63 0.03
0.69 1 0.03
Table 3: Spike sorting results on WaveClus dataset. The results for other methods were obtained
through SpikeInterface [46). Values mean + S.E.M. Note that SimSort did not use this dataset for training, indicating zero-shot generalization.
Easy (12 recordings)
Difficult recordings) Recall
Methods
Accuracy
Recall
Precision
Accuracy
Precision
KiloSurt
0.54 0.12
0.46 1 0.15
0.62 0.13
0.17 0.07
0.23 1.10
0.18 0.07
KiloSor12
0.61 1 0.09
0.66
1 0.09
0.67 0.09
0.10 1 0.07
0.12
0.10 1 0.07
MountainSort4
0.73 + 0.08
0.79 + 0.07
0.79 + 0.08
0.48 0.16
0.49 0.16
0.52 1 0.17
MountainSort5
0.71 + 0.09
0.74 + 0.08
0.79 0.10
0.42 + 0.15
0.46 0.15
0.46 + 0.16
SimSort
0.75 + 0.06
0.84 + 0.04
0.85 + 0.05
0.71 + 0.10
0.81 + 0.07
0.81 + 0.09

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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 real-world data-based datasets to further test its zero-shot generalizability and robustmess (RQI), On the Hybrid dataset, we compared SimSort against several algorithms via SpikeForest, including HerdingSpikes2 [18], IronClust, JRClust, KiloSort [77, KiloSort2, MountainSort4 [6], SpykingCircus, and Tridesclous. The results are summarized in Table 2 and Fig. [S] 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 and Fig. [ST] 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 '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 (T 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 easy 1/diff' and tested on easy2/diff2, respectively. The IBL Neuropixels dataset fine-tuned on 100 neurons and evaluated on 50 test sets, each with 10 randomly selected neurons.
Datasel
Spike Delection (Acc/Rec/Pree)
Spike ldentification (ARI)
Spike Sorting (Acc/Rec/Prec)
Hybrid-static
SimSort
0.71=0.02 0.83x0002 / 0.83a00.2
0.9020.02
0.5920.03 0.6520.03 0,76600.02
SimSort-FT
0.75=0.03 0. 0.899009022 0.82240.02
0.92=0.02
0.5820.03 0.65+0.03 0.773+0.02
Hybrid-drin
SimSort
0.67=0.02 / 0. 0.80&0.00 +0.01
0,88:0.03
0.54:0.09 / 0.611+0.09 00677+0.07
SimSort-FT
0.73+0.03 / 0. 0.885+0003 0.83+0.01
0.88+0.02
0.56+0.04 0.63340003 /00.9+0.03
SimSort
0.59g0.02 0.99900.00 0.59920.02
0.9120.01
0.70s0.11 0.7910.007 /0.8420..00
Waveclus-easy
SimSor-FT
0.94=0.01 / 0,966t0.00 0.99800.01
0.91=0.01
0.73#0.08 0.79900005 /0.91
Waveclus-difficalt
SimSort
0.66#0.08 0.999+0.000 00 /0.67+0.00
0.88+0.01
0.76+0. 14 0. 0.8440009 0.884+0.
SimSort-FF FT
0.95=0.01 / 0.966+0.000 0,,99+0.01
0,87=0.03
0.77#0. 14 0..8200. 0.8600 13
IBL Nouropixels
SimSart
NVA
0.49:0.09
NBA
SimSort-FTT
NA
0,53:0.09
N/A
5.4 Real-Worle Neural Recording
Beyond quantitative benchmarks, we further assessed SimSort real experimental setting. Specifi- cally, we applied it to extracellular recordings (collected in our wet lab, see Appendix A.112)) from the mouse primary visual cortex obtained using tetrodes during presentation of full-field drifting gratings in 8 orientations (Fig. Frig55 55). From a representative recording session, SimSort identified six putative single units (Fig. 5p). These units exhibited distinct spike waveforms and their autocorrelograms showed clear refractory period structure(Fig. [b)), 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. 5id; Appendix A definition). Across 10 tetrode recordings, SimSort isolated a total of 40 units, with a mean gOSi of 0.27 + 0. 0.14 (mean + S.D., Fig. (S7)). These results demonstrate the practical applicability of SimSort to real neural recordings.
5.5 Detailed Analysis
To explain why framework achieved higher overall spike sorting accuracy (RQ2), we dissected SimSort and analyzed each component, i.c., spike detection and spike identification models. Further- more, we presented some case studies of the latent embedding of spikes from multiple neurons.
7

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(b)
Ontrggring
COTTL
Uni3 WV
(d)
200 01.03 AV
SOW ANP
1244 031
212
100.0.0
25P
Figure 5: Evaluation of SimSort on real experimental data (See Fig. S17 for additional recording results). (a) Experimental setup: extracellular recordings from the mouse primary visual cortex during drifting gratings eight directions. (b) Visualization of SimSort's ' sorting results. (c) Autocorrelograms and average waveform of each unit. (d) Orientation tuning curves and global orientation selectivity index (gOSI) for each unit.
Spike Detection We first compared detection model commonly used threshold-baid detection method (see Appendix for details). The results are shown in Table Sa SimSort's ' spike detector consistently outperformed the threshold-based method across all metrics the Hybrid dataset. In the static condition, SimSort improved accuracy by 188, recall by 18%, and precision ~33% in accuracy, ~17% in recall, and
by 1% Under the drift condition, the improvements 1%% in precision. It is important to note that the performance of the Threshold detector was highly sensitive 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 solid foundation for the overall performance of SimSort in spike sorting tasks.
Table 5: Spike detection and identification results the Hybrid dataset. Values are mean + S.E.M.
(a) Spike detection results. The threshold detector
(b) Spike identification results. All results averaged across 20 repeats of GMM.
used the best best-perforiing threshold by grid search.
Dataset
Method
Accuracy
Recall
Precision
PCA
Static
SimSort's detector
Threshold detector
061+0003
0.72:0003
0.71+00.22
0.84:0002
081++002
0.82:=0.02
Hybrid-stati 0.79 + B04
Hybrid-trift 0.74 + 0.04
0.8 + 0.02
0.88 + 0.2
D88 + 0.01
0.88 0.02
0.91 0.02
0,89 1.03
Threshold detector
0.6020003
0.70:0.03
0.80:0002
SimSont's detector
00,03
0.2220.02
0.81:00002
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, 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 [1 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 our approach deriving meaningful and reliable representations from extracellular data.

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Additionally, we compared SimSort with CEED 21 a recent framework for representation learning on extracellular data, using the author- protided model. Note that CEED is trained on datasets derived from IBI. Danl .ab 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 S3 our method, without training on the dataset, achieved performance comparable to CEED.
Fig. S2
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 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 study to assess the impact of different dimensionality reduction techniques (PCA, I-SNE, UMAP, and None) and clustering algorithms (GMM, KMeans 477, Mean Shift [42], and Spectral Clustering (88). 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.
UNE Find Labeb 9.90
codle thid Labeta 0 l05
USet Predi Lubel 2,28
CMD Fred Labsb D.y
Sindson embedding
Figure 6: Visualized results of spike identification on example recording in the Hybrid static subset. See Appendix C for visualization results for additional samples.
5.6 Scaling Law of Data Size
As we emphasized the importance of large-scale dataset throughout this work, it was interesting evaluate how SimSort's performance scaled with the size of the training dataset (RQ3). Specifi- cally, 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 28 to 2 trials. As Fig. 7 shows, larger datasets led to almost consistent perfor- mance improvements on both hybrid static and drift subsets, reflecting that the large-scale dataset holds the key of SimSort's 's effectiveness.
0.70
-0.00
0.65
Accuracy
0.60
0.80
0.55
00.7
0.50
Serting fcr static
Serting d
Deseccion Acc dade
Desection Acc 2itt
dettiior on state
dent on
0.65
2
2
2/
Data Size
Figure 7: Performance scaling with increasing training data size the Hybrid-drift and -static.

---

Conclusion
In this paper, we presented SimSort, a data-driven framework for automated spike sorting, built upon biologically realistic large-scale extracellular dataset. SimSort achieves competitive superior performance compared to commonly used spike sorting algorithms on both simulated and real- wollw 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 (IBI. Neuropixels), future work should extend the training with diverse electrode geometries.
Acknowledgments and Disclosure of Funding
This work was supported by Al for Science Foundation of Fudan University (FudanX24A10466 to Y.G.) and Microsoft Research.
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Justification: Our model is for spike sorting in neuroscience data analysis. We are not aware of any risk for misusage.
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Broader Impacts
Our work aims advance the methodology of data processing in neuroscience experiments. It does not directly lead to any negative societal impact.
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 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 neuron models. It provided ground-truth units per recording for evaluating spike sorting performance.
43 These recordings were generated by Kilosort, with waveform templates recorded at 5 μm electrode spacing and 1/f background noise. consisted of two subsets: 9 static tetrode recordings and 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 input. For assessing identification performance, constructed waveform dataset by extracting 500 waveforms (60-timesteps length) per unit.
Hybrid dataset. This dataset was composed of recordings provided by Spikeforest
This dataset consisted of four subsets (8 recordings in Easyl, 4 recordings in Easy2, 4 recordings in Difficultl, and 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 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 to 0.5), and random temporal shifts (-5 to +5 samples).
WaveClus dataset.
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 Neuropixels 1.0 probe across multiple brain regions from a mouse performing decision-making behavior task. To compare spike identification performance with CEED, we prepared this dataset following the method described 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 1-channel data into the model, while SimSort processes data from the center 4 channels.
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Real-world Neural Recording. These recordings were collected by our wet lab (approved by research ethics committee). 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 different orientations. The stimuli were presented in randomized sequence over 8 repeated trials. Each grating had spatial frequency of 0.04 cycles per degree (cpd) and 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:
Σ R(0))2201 R(0)
gOSt
where 0 is the direction of the moving grating and R(O) is the neuronal response to direction 0. The imaginary unit denoted as whose square is 1. The gOSI ranges from 0 to 1, with 0 indicating no orientation selectivity (equal response to all directions), and indicating maximal selectivity (response only to single orientation).
Table S1: Details information of test datasets used for evaluation.
Num. Recordings
Sample (Hx)
Num. True Units
Ground-Trui
Datasets
Channels
600 (soc per rec.)
(per rec.)
Determination
BBP L6 dataset
20
10000
5
Simulation
Hyhrid dataset
18
10-15
waveforms hackground noise
WaveClus dataset
20
24000
60
3
Real waveforms background noise
IBL Neuropixels dataset
/
30000
10
Serted with KiloSort 2.5
Real-world Neural recording
40000
Ground-rrahh
A.2 Technical Details of Simulation
Intracellular elcctrophysiological activity was simulated using the NEU- RON package 322 49 Pink noise, scaled the rheobase (the minimum current needed elicit an action potential in a neuron), was injected into each neuron's '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 -70 m and maintained at physiological temperature of C Synaptic mechanisms and biophysical properties were dynamically compiled and loaded to ensure compatibility with the NEURON simulation environment.
Intracellular Simulation.
For each neuron, a muuli-comparttmnttu model [29 30 50 51 was used to calculate transmembrane currents [52 153 54 (55) The neuron was divided into compartments, each described an equivalent electrical circuit. The dynamics of the membrane potential in each compartment n were governed by Kirchhoff's 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 constant. The temporal evolution of the membrane potential V,, in compartment was
given by 9n.n+1 (V - Vx) In- (V, - Va-1) = C, dV,, + Σj [ where In,n+1 and In-1.x di represented the conductances between neighboring compartments, On was the membrane capacitance, and Σj (i) 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 100 x 100 100 yum cubic space. These neurons consisted of different types 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 a given electrode site re was computed as the sum of contributions from all neuronal segments within the simulation space: ((r.. t) 4xo
1 ΣN MM, [n.((t) where Ire
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In.m(8 (t) was the transmembrane current of the m-th compartment of the n-th neuron, Fa,m was its position, the extracellular medium's conductivity, and r. Tn.m was the Euclidean distance between the segment and the electrode. Here, N was the total number of neurons, and Mn 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 points and C' denotes the number of electrode channels, a subset of channels C C (1,2,...,,C C} was randomly selected. A probability p E [0, 1] determined whether the following augmentations were independently applied to each channel k E C:
For each input segment X; RT'C' where T represents the number of time
Xi,k + B Kiiks Xik,ltata
AddWithNoise,
X
GandommAppliuudeer RandomTimeJitter
where E ~v NNO,0² represents Gaussian noise, 3 ~ U(1 s, 1 + s) is random amplitude scaling factor, and E, (1-6, 5) introduces small temporal shifts. The parameter n is an up-sampling factor enabling finer time resolution.
Model-Based Detector In Sec. 4.2 introduced model-based detector to predict spike events. These predictions were processed through the following steps:
1. Detection with Model: The model took as input multi-channel signal V IR where t was the number of time samples and e was the number of channels. This input was processed simultaneously across all channels by the detection model, which predicted a sequence of binary labels (:(t) for each time point:
(c) mmodel (V), where fmadel represented the detection model. The output d (t) indicated the presence or absence of spike events at each time point.
Adjacent Peak Merging: The predicted labels (() 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:
T
max oE (I.....C)
v(t,c)).
(5)
where (t,, e) denoted the signal value time t for channel c. Using this peak channel, adjacent peaks were grouped within a window size At, and only the time point with the maximum absolute signal was retained within each group:
max = arg max (ttttc")l =E(t..fe)
(6)
where [4a, (e] denoted the boundaries of a group of adjacent peaks.
Spike Peak Extraction: After merging, the final spike peak positions (tu} were determined as the set of time points where (t) = 1:
(t (t) 1}.
Threshold-Bare Detector In Sec. 5.5 we compared our model-based detector with threshold- based detector. To ensure fair comparison, the processing steps for the threshold- based detector were designed to align closely with those of the model-based detector. The threshold-basee method processed the signal V E fxee where i 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 (tt, c) was scanned to identify local maxima that satisfied the following conditions:
V(t, c) Ths
V(t,c) V(t v(t-r,c), V(t,c) +r,+r,c),
(8)
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where Thiiigg ch was the amplitude threshold, and E {I...., loc poo___aaae______ These conditions ensured that the detected peaks were above the threshold and were local maxima within the range Peaks identified by this criterion were further validated within broader range rangerange to ensure they represented significant spike events.
2. Adjacent Peak Merging: For each channel adjacent peaks were grouped within a window size At, and only the time point with the maximum absolute signal was retained within each group:
mmx
arg max (V,,,)), (E(ts.fe
where [6a, te] defined the boundaries of cluster of adjacent peaks. This step ensured that closely spaced peaks were consolidated into a single spike.
3. Spike Peak Extraction: After processing all channels independently, the final detected spike positions for channel e were determined as:
ts (t, = +mmax
(10)
A.4 Spike Identification
Waveform Snippet
Extraction. For each detected spike position tx, a waveform snippet W₃ RLxe was extracted from the multi-channel continuous signal ((t, where I, was the snippet length (number of time samples) and was the number of channels. The snippet was centered around t and spanned time window determined by the pre- and post-padding values:
W = V Vtt+ Pere t + Ppost:). :),
(11)
where (tt, c) represented the input signal value at time for channel c, and Pare and Ppest were the padding values (in time samples) before and after the spike time tg. respectively.
A.5 Definitions of Metries
To evaluate the performance of spike sorting algorithms, we adopted standard metrics 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.
1. Matching Spike Events: For given sorted spike train k and ground-truth spike train L, the first step was to calculate the number of matching events (nipach) sorted spike event a) was
(k)
considered a match to a ground-truth event ( if the absolute time difference was less than or equal to the predefined matching window A, which was typically set to I millisecond:
match # 0
1 < for some j}.
(12)
Using the number of matching events, we computed the number missed events (niiik) and false positives (n.,)) as follows:
Missed Events and False Positives:
match miss = N - ni,k Bi,k
fp TiA
M2
ni,k
match
(13)
where N, was the total number of ground-truth events in 1, and M₁. was the total number of sorted events in k.
Accuracy: Accuracy (ar,x) balanced the contributions of both missed events and false positives and was defined as:
match 7
01,k
(14)
match
miss 7i
+ 12,K
Precision and Recall: The precision (pr) and recall (r:) were calculated based on the best matching sorted unit h for each ground-truth unit The best match was determined as the sorted unit & with the highest accuracy:
k
arg max K...
(15)
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To identify the best matching sorted unit kj for each ground ground-truth truth unit I, the Hungarian Algorithm was applied establish the best one-to-one correspondence between sorted and ground-truth units. Using this best match, precision and recall were defined as:
match 2.
n
match ",k,
PL
match 1,8g
manch 1,kk
+ 11 miss 1,
(16)
1.
A.6 Hyperparameter Search
We performed a grid search optimize the hyperparameters for the SimSort detection and identifica- tion 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 presented in Tables S2 and S3
A.7 Infrastructures
Our simulation dataset generated using CPU clusters for approximately 5000 CPU hours, which can be parallelized. Our models are trained on NVIDIA A100/V 100 GPU, approximately 20 GPU hours for each time.
Table S2: Hyperparameters of SimSort detection model.
Hyperparameters
Description
Value
Num Layer
Number of Transformer encoder layers
4
Input Size
Dimensions of the input data
4
Batch Size
Number of items processed a single operation
Learning Rate
Learning rate of the model
5e-4
Hidden Size
Dimension of the Transformer encoder
256
nhcad
Number of Transformer attention heads
4
Dropout
Dropout probability Transformer encoder
0.2
Weight Decay
Regularization parameter
to prevent overfitting Steps to warm up the learning rate
le-5
Warmup Steps
4000
Num Epochs
Number of epochs to run Probability threshold for classifying spike events
20
Sigmoid Threshold
0.97
Noise Level
The level of noise applied to the input data
0.9
Maximum Time Jitter
Maximum variation timing (in terms upsampled sampling points)
Amplitude Scale Range
The range for scaling the amplitude of the input data The probability to perform augmentations input data
[0. 1]
Transform Probability
0.8
Max Channels
The maximum number of data channels augmentations applied
4
Table S3: Hyperparameters SimSort identification model.
Hyperparameters
Description
Value
Num Layer
Number of GRU layers
Input Size
Dimensions of the input data
Batch Size
Number of items processed in a single operation
128
Learning Rate
Learning rate of the model
lc-4
Hidden Size
Latent dimention size of GRU
512
Weight Decay
Regularization parameter used to prevent overfitting
le-5
Num Epochs
Number of epochs to run
10
components
Number of components to reconstruct waveform using SVD
Noise Level
The level of noise applied to the input data
4
Maximum Time Jitter
Maximum variation in timing (in terms of upsampled sampling points)
50
Amplitude Scale Range
The range for scaling the amplitude of the input data
[0.5,
Transform Probability
The probability to perform augmentations input data
0.5
Max Channels
The maximum number of data channels augmentations are applied to
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B Supplementary Figures
Static
Drit
Ewsy
Officul
0.8
0.6-
Accuracy
Accuracy
0.8
0.4
0.3-
ecceee
Aending foorii
coodure Peetece andentiio direertoorg
0.0
Figure S1: (a) Accuracy of spike sorting on the Hybrid dataset. SimSort significantly outperforms HerdingSpikes2 (p- 0.0000*), JRClust (p 0. 0.0000*), KiloSort2 (p 0.0000*), ), and Tridesclous (p = 0.0001") in static recordings, and also shows significant advantage over IronClust (p = 0.0296*). There is significant difference compared KiloSort
0.5023), MountainSort4 (p = 0.1799), and SpykingCireus (p 0.1356). In the drift condition, SimSort significantly outperforms HerdingSpikes2 (p 0.0000"), JRClust (p 0.0000*), KiloSort (p 0.0285") ), KiloSort2 (p 0.0000"), MountainSort4 (p = 0.0004*), SpykingCircus (p = 0.0111*), and Tridesclous (p 0.0007*), with no signifieant difference compared to IronClust (p 0.3398). (b) Accuracy of spike sorting on the WaveClus dataset. In the easy subset, SimSort shows no sigmificant improvement compared to KiloSort (p 0.0770), KiloSort2 (p 0.1569), MountainSort4 (p = 0.6137), and MountainSort5 (p 0. 0.4424). In the difficult subset, SimSort significantly outperforms KiloSort (p 0.0007*), KiloSort2 (p 0.0004"), and MountainSort5 (p 0.0449*), with no significant difference compared to MountainSort4 (p 0.0935). Statistical analysis was performed using a paired two-tailed t-test.
CEED
CEED
000000000000000
SimSort
00000 CHOOTO
Mean ARI Score
Figure S2:
Performance comparison on Fifty test of IBL Neuropixels dataset.
Table S4: Performance comparison of spike identification 50 test sets from IBI. Neuropixels dataset. ARI values are presented as mean + standard deviation. Statistical significance was assessed with paired t-tests: p 0.0012 for SimSort vs. CEED, 0.0554 for SimSort vs. CEED+UMAP.
Methods
Test sets (seed 0-49)
CEED
0.46 + 0.09
CEED+UMAP
0.47 + 0.10
Ours
0.49 - 0.09
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Sindfart embeddiing
CPEDD embedking
CEED embedding
Labels
U00P
USGAP'
CETOX
Example Test set1
(MAF)
Mo
-1.0
Sindlert embedding, Thai Labels
CPFD embottding Than Labels
611
+309
UHAP
01667
Sindort embadding,
CEED emkedding.
CEEO enkedding.
JMAPTS
Example Test set2
SimSort embedding,
CEED embooding,
CEED embadding.
UNAP22
Example Test set3
embretding,
embedding
Figure S3: Visualized comparison results of three example sets from IBL Neuropixels dataset. For each example, the first column presents GMM clustering results on UMAP-embedder SimSort representations, with predicted labels (top) and labels (bottom). The second column displays GMM clustering on UMAP-embedder clustering directly on CEED representations (as in the CEED paper).
CEED representations. The third column illustrates GMM
28

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Hybrid-static
Hybrid-drift
GMM
GMM
0.60
0.51
0.49
0.51
0.56
0.48
0.46
0.51
0.54
KMeans
KMeans
0.55
0.51
0.48
0.50
0.56
0.48
0.44
0.51
0.53
0.50
MS
0.40
0.48
0.59
0.62
MS
0.42
0.46
0.56
0.55
0.45
SC
0.58
0.45
0.50
0.50
SC
0.53
0.41
0.44
0.56
None
PCA
TSNE
UMAP
None
PCA
TSNE
UMAP
Figure S4: Impact dimensionality reduction and clustering algorithms on the hybrid dataset. MS denotes Mean Shift, SC denotes Spectral Clustering- Comparing different combinations of dimensionality reduction methods (None, PCA, t-SNE, UMAP) and clustering algorithms (GMM, KMeans, Mean Shift, and Spectral Clustering) on static (left) and drift (right) subset. Here. "None" indicates that raw features learned by SimSort were used directly without dimensionality reduction.
29

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C More Showcases
In this section, we present additional visualizations from various datasets to provide a more com- prehensive 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.
Channel
Charnel
-00
10003
20033 (ampiln
S0000
Charrad
39003
29005
30009
ADOC
300.0
6000
cumpiia
Channel
33023
23:05
300.3
Figure S5: Visualization of spike detection performance on an example recording in Hybrid dataset.
3008
Hewy /l/ h
Wh M //
Figure S6: Zoomed-in visualization of spike detection performance on an example recording in Hybrid dataset.
30

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Uhamadd
Charnel
3903
2900
AD0O
50000
60000
Channel
3302
2000
2000
Dhatral
33003
20008
20.00
30000
60005
Figure S7: Visualization of spike detection performance on an example recording in WaveClus dataset.
Channel
Thennel
Chatrel
180
Chatrel
The Protind Detccial
Mavee Croundl
3009
Figure S8: Zoomed-in visualization of spike detection performance on an example recording in WaveClus dataset.
31

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Charnel
L2000
4000
500
1300
Chemcel
12500
29005
Chatrel
12000
2000
100
LOK0
Charrel
Thas Footain Delucted
Cround
1000
2000
90030
4000
5001
Figure 89: Visualization of spike detection performance on an example recording in BBP L6 dataset.
Chemrdd
Channel 2
86
Chanrel
Cherrel
2003
Figure S10: Zoomed-in in visualization of spike detection performance on an example recording in BBP L6 dataset.
32

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Static-1
Static-6
SimSort emocdding.
embredding, GMH
0.92
LSL
Ai
USAP
Static-2
SimSort embedding.
Static-7
embedding. CVN Fred abely 0.91
Static-3
SimSort embedding. GMM Pred Labels 0.33
2005
2005
Static-8
embedding,
CSSH 0.91
Static-4
SindBrt ambeddiing GNM Fred Lobels 0.92
2004
US60
Static-9
SimSort embedding. GAM Predd Labels 0.30
IMAP
Static-5
Simsort embedding.
-10
UINAP
Figure S11: Visualization of spike identification performance on hybrid static subset.
33

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Drift-1
Sinrfost embeotinng GNM Pred Labels 0.89
Drift-6
Simforrt embedding CasM Pred Labels 0.92
IMMP
USAPF1
Drift-2
SimSort embedking. GMM fred Labels 0.35
Drift-7
Sindort embeediing. GMN Pred Labels 0.89
IOOL
UNAP
Drift-3
SimSSrt embedding.
GMM Pred Labes 0.93
Drift-8
SimSort embadding. GMM Prod Labels 0.51
HAPP
Drift-4
SimSort embedding,
0.89
Drift-9
Sindort embedddnng
Drift-5
SimSprt embedding, Predi Labes 0.90
LENGU
Figure S12: Visualization of spike identification performance on hybrid drift subset.
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Static-1 1
Static-2 2
Static-3
Static-6
Static-4
Static-5
11
Static-7
Static-8
Static-9
Figure S13: Visualization of spike sorting performance hybrid static subset.
35

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Drift-1
Drift-2
Drift-3
Drift-4
Drift-5
Drift-6
Drift7
Drift-8
Figure $14: Visualization of spike sorting performance hybrid drift subset.
36

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E
Easy1-2
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Figure S15: Visualization of spike sorting performance on WaveClus dataset.
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Figure S16: Visualization of spike sorting performance on BBP L6 dataset.
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Figure S17: Visualization of spike sorting performance on real recording data.
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Reviews:
[{"review_id": "3pymFi9hMw", "reviewer": "Reviewer_mc8c", "summary": "This 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.", "questions": "1.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?", "limitations": "See Weaknesses and rebuttal questions.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors failed to adequately address the limitations regarding the dataset, specifically:\n\nDespite claiming the use of a large-scale dataset, the actual training data was limited to four-channel tetrode recordings. This dataset limitation may restrict the model's generalizability to higher-density probes (e.g., Neuropixels).\nElectrode geometry diversity: The model was not trained on diverse electrode geometries, which may impair its performance across different experimental configurations.\nIn their Rebuttal, the authors acknowledged that simply increasing dataset scale cannot fully compensate for insufficient data diversity, yet failed to propose effective mitigation strategies. Moreover, the authors did not sufficiently acknowledge the dataset limitations concerning electrode geometries and higher-density probes.\n\nGiven these reasons, we maintain our original evaluation decision.", "rebuttal": "We appreciate your thoughtful comments and suggestions, which helped improve the clarity and quality of our work. Below, we address your concerns and questions in detail:\n\n### 1. Model design and architectural novelty (W1)\n\nThe main contribution of our study is to reveal the potential of simulation-driven pretraining to enhance the robustness and scalability of spike sorting in experimental neuroscience. Although our architecture is based on well-established components such as Transformers and GRUs, the novelty of our work lies in how these components are integrated and tailored specifically for spike sorting, particularly in the context of simulation-based pretraining.\n\n### 2. Inference speed evaluation and practicality (W2 & Q3)\n\nThis is a nice point. SimSort is actually fast and we will update our paper to clarify its inference speed. As shown in the demonstration video included in our originally submitted supplementary material, SimSort processes a 30-second tetrode recording in 9.5 seconds for the whole process including data pre-processing, spike detection, spike identification and visualization on a single A100 GPU. This pipeline has not undergone dedicated speed optimization. We believe that techniques such as batching, GPU-based preprocessing and clustering could further improve runtime. Even in its current form, SimSort demonstrates practical inference speed on tetrode recording and exhibits potential for efficient large-scale spike sorting. The following Table 1 shows the detailed computational speed.\n\nTable 1: Computational Requirements of SimSort models\n| Quantity | Spike Detection (Transformer) | Spike Identification (GRU) |\n|--|--|--|\n| **Configuration** | | |\n| Sequence Length per sample | 2000 | 60 |\n| Number of Parameters | 3,160,577 | 795,648 |\n| **Inference time** | | |\n| CPU Time / per sample (ms)  | 15 | 7 |\n| CUDA Time / per sample (ms) | 10 | 5 |\n\n### 3. Scaling law of the training data size (W3)\n\n> discussion on data scale-up laws\n\nThank you for your careful review. Are you suggesting that detection accuracy is exhibiting signs of saturation? We note, however, that the other two metrics—Sorting Accuracy and Identification ARI (solid green and purple curves in Fig. 7)—do not show comparable saturation.\n\nConsider the Hybrid (static) dataset. When the data size increases from $2^{12}$ to $2^{13}$, Sorting Accuracy rises by ≈ 0.02 and Identification ARI by ≈ 0.04. Although these absolute gains may appear modest, they are meaningful because the metrics are approaching their upper bounds. The similar pattern holds for the Hybrid (drift) dataset. \n\nRegarding detection accuracy (dashed purple curves), we agree that the trend is approaching saturation. Importantly, though, enlarging the dataset never degrades detection performance, so increasing sample size remains advantageous—even if the marginal gains taper off.\n\nWe appreciate your suggestion about adding more discussion about the results regarding the scaling law, which will be reflected in the revision.\n\n> more diversified simulation data or improvements in the model architecture \n\nThank you for raising this thoughtful question. We agree that a critical direction for future improvement lies in examining how the composition and diversity of synthetic datasets influence model generalization.\n\nAs you suggested, merely increasing the volume of data from a single simulation paradigm may yield diminishing returns, especially if the additional data do not introduce novel variability in waveform structure or network-level dynamics.\n\nFurther performance gains are more likely to come from increasing simulation diversity rather than scale alone. This includes:\n- Incorporating neuron models from different brain regions, which can differ in cell-type composition, firing patterns, and background activity.\n- Including recurrent connectivity of neuron models, which can introduce spike synchrony and temporal correlations—important challenges for realistic spike sorting.\n- Utilizing 3D neuron morphologies (rather than 2d) and more realistic tissue conductivity, which can generate more heterogeneous extracellular waveforms.\n\nAt the same time, improvements in model architecture, particularly mechanisms that account for temporal overlap or drift (e.g., invariant representations across waveform deformations), are also promising directions.\n\nIn short, we see this work as a first step demonstrating the viability of simulation-based pretraining, and we fully agree that diversifying simulation conditions and evolving model design will be key to further advancing generalization. We plan to pursue both directions in future work.\n\n### 4. factors in the simulation process that are key to zero-shot transfer (Q1)\n\nThis is a thoughtful question. Several aspects of our simulation design contributed to the observed generalization:\n\n- **Electrode geometry and neuron positioning**.  We explicitly model the 3D spatial configuration of electrodes and surrounding neurons. The relative distance, orientation, and density of nearby cells strongly influence extracellular signals. This spatial variability is essential for training a detection model to distinguish spikes from noise across diverse waveform shapes and amplitudes. \n- **Biophysical realism of neuron models**. We use morphologically and electrophysiologically realistic neurons with diverse firing properties, generating a wide range of biologically plausible waveforms.\n- **Naturalistic noise-driven activity**. Temporally correlated stochastic current injection introduces realistic subthreshold fluctuations and irregular spike timing, enhancing robustness to background noise and temporal variability.\n- **Large-scale, ground-truth-labeled training data**. The volume and diversity of training samples help the model learn generalizable features and avoid overfitting to specific conditions.\n\nOur results suggest that combining spatial modeling, biophysical diversity, and statistical variability can help bridge the sim-to-real gap. Supporting high-density probes is part of our ongoing efforts.\n\n### 5. Model architecture design intention(Q2)\nFor spike detection, the task is formulated as a sequence labeling problem over long, continuous voltage traces. Here, temporal context is critical—not only to suppress noise and artifacts, but also to accurately detect overlapping or low-amplitude spikes that are temporally modulated. Transformers are particularly well-suited for this setting due to their ability to model long-range dependencies and contextual interactions between time points, without the limitations of recurrence or fixed receptive fields. Furthermore, the self-attention mechanism allows the model to adaptively weigh different parts of the signal for detecting spike events under varying background conditions.\n\nFor spike identification, the input consists of short waveform segments (~2 ms, typically 60 samples), where the objective is to learn compact, discriminative embeddings of spike shapes for clustering. In this case, we chose a GRU-based encoder after empirical comparison with Transformer. GRUs have significantly fewer parameters and lower computational cost than Transformer. \n\nTo validate these design choices, we conducted an ablation study comparing different combinations of detection and identification architectures. \n\nFirst, we replaced the detection module with a simple threshold detector and kept the spike identification module unchanged. Please kindly refer to our response to reviewer jc2X's W3 & Q3 for the results. We observed that SimSort's detection model consistently performs well, whereas the threshold-based method is sensitive to the choice of voltage threshold.\n\nThen, we tested whether a simpler GRU-based detection model can replace the Transformer-based detector in the spike detection stage. As shown in Table 2, the detection performance drops significantly. \n\nTable 2. Detection Performance Comparison Between GRU and Transformer\n|Detection Model| Accuracy (Static)| Recall (Static) | Precision (Static) |Accuracy (Drift)| Recall (Drift)| Precision (Drift)|\n|--|--|--|--|--|--|--|\n|GRU|0.34±0.01|0.38±0.02|0.75±0.02|0.34±0.02|0.39±0.02|0.75±0.01|\n|Transformer (SimSort)|**0.72**±0.03|**0.84**±0.02|**0.82**±0.02|**0.68**±0.03|**0.82**±0.02|**0.81**±0.02|\n\nTransformers are particularly well-suited for spike detection due to their ability to model long-range dependencies and contextual interactions between time points, without the limitations of recurrence or fixed receptive fields. Furthermore, the self-attention mechanism allows the model to adaptively weigh different parts of the signal for detecting spike events under varying background conditions.\n\nTo evaluate the role of encoder architecture and representation learning objective in the spike identification model, we compared different encoder backbones and learning algorithms as follows.\n\nTable 3. Identification performance across objectives, encoders, and denoising\n|Objective|Encoder|Denoiser|ARI(Hybrid-static)|ARI(Hybrid-drift)|\n|--|--|--|--|--|\n|Contrastive|GRU|✓|**0.91**±0.02|**0.89**±0.03|\n|Contrastive|GRU|✗|0.88±0.03|0.85±0.02|\n|Contrastive|Transformer|✓|0.89±0.03|0.85±0.03|\n|Supervised|GRU|✓|0.24±0.02|0.26±0.02|\n\nAs shown in Table 3, the contrastive approach yields substantially better performance, indicating its critical role in robust spike identification. Among the contrastive models, the GRU encoder slightly outperforms the Transformer. We also noticed a modest performance drop when denoiser is not applied.\n\n> The font size in the tables in Figures 5 and 6 is too small.\n\nWe appreciate and will revise.\n\nThanks again for your valuable suggestions which have helped us improve the clarity and rigor of our work, we will include these results in the revised manuscript. We welcome any further questions and are happy to engage in continued discussion."}, {"review_id": "2QZ5V5Nro9", "reviewer": "Reviewer_jc2X", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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?", "was_revised": true, "final_justification": "The authors address most of the concerns. I will maintain a positive score", "rebuttal": "Thank you for your positive feedback and helpful suggestions. Below, we respond in detail to the concerns you raised:\n### 1. Generalization to other datasets and recording techniques (W1 & Q1)\n\nWe agree that evaluating SimSort on a broader range of datasets and recording configurations is an important direction. In this work, we focused on tetrode recordings as an initial step toward validating simulation-pretrained models on real data. Extending SimSort to higher-density recordings like Neuropixels presents additional engineering and algorithmic challenges (e.g., larger input dimensionality, probe geometry encoding), which we are actively working to address in future versions.\n\n### 2. Comparison with KiloSort 3.5 and 4.0 (W2 & Q2)\n\nThank you for raising this important point. There are two keys reasons that we did not include Kilosort 3.5 and 4.0 in our comparisons.\n\nFirst, KiloSort 3.5 and 4.0 introduced enhancements primarily optimized for high-density Neuropixels recordings. Since our current study focuses on tetrode recordings, we did not include KiloSort 3.5 and 4 in our comparisons.\n\nMoreover, the goal of our work is to demonstrate, for the first time, that large-scale, simulation-driven pretraining provides a potentially practical and generalizable strategy for robust spike sorting.\n\n### 3. Ablation study on model architecture and detection method (W3 & Q3)\n\nWe appreciate your suggestion. We further conduct ablation experiments to analyze the contribution of different components in SimSort to the overall performance. First, we replaced the detection module with a simple threshold detector and kept the spike identification module unchanged. The results on the hybrid dataset are summarized in Table 1.\n\nTable 1. Sorting Results with Threshold Detector vs SimSort (Hybrid Dataset)\n| Method | Accuracy (Static) | Recall (Static) | Precision (Static) | Accuracy (Drift) | Recall (Drift) | Precision (Drift) |\n|-----------|-------------------|------------------|---------------------|------------------|----------------|--------------------|\n| Threshold (2.0)       | 0.24 ± 0.01        | 0.44 ± 0.01       | 0.26 ± 0.01          | 0.22 ± 0.01       | 0.42 ± 0.01     | 0.24 ± 0.01         |\n| Threshold (2.5)       | 0.30 ± 0.02        | 0.49 ± 0.01       | 0.33 ± 0.02          | 0.30 ± 0.02       | 0.47 ± 0.02     | 0.33 ± 0.02         |\n| Threshold (3.0)       | 0.47 ± 0.05        | 0.59 ± 0.03       | 0.53 ± 0.06          | 0.46 ± 0.05       | 0.57 ± 0.04     | 0.51 ± 0.05         |\n| Threshold (3.5)       | 0.57 ± 0.03        | 0.62 ± 0.02       | 0.69 ± 0.04          | 0.55 ± 0.04       | 0.60 ± 0.03     | 0.65 ± 0.04         |\n| Threshold (4.0)       | 0.55 ± 0.02        | 0.58 ± 0.02       | 0.76 ± 0.02          | 0.50 ± 0.03       | 0.53 ± 0.03     | **0.70** ± 0.03         |\n| Threshold (4.5)       | 0.44 ± 0.02        | 0.47 ± 0.02       | 0.71 ± 0.02          | 0.44 ± 0.04       | 0.46 ± 0.04     | 0.68 ± 0.03         |\n| Threshold (5.0)       | 0.41 ± 0.03        | 0.42 ± 0.03       | 0.68 ± 0.02          | 0.37 ± 0.03       | 0.39 ± 0.03     | 0.65 ± 0.03         |\n| SimSort        | **0.62** ± 0.04      | **0.68** ± 0.03       | **0.77** ± 0.03          | **0.56** ± 0.03       | **0.63** ± 0.03     | 0.69 ± 0.03         |\n\nFrom Table 1, we can see that SimSort consistently performs well, whereas the threshold-based method is sensitive to the choice of voltage threshold. \n\n\nThen, we tested whether a simpler GRU-based detection model can replace the Transformer-based detector in the spike detection stage. As shown in Table 2, the detection performance drops significantly. \n\nTable 2. Detection Performance Comparison Between GRU and Transformer\n| Detection Model      |  Accuracy (Static)| Recall (Static)     | Precision (Static)    |Accuracy (Drift)| Recall (Drift)     | Precision (Drift)    |\n|----------------------|----------------|--------------|--------------|--------------|--------------|--------------|\n| GRU                  | 0.34 ± 0.01    | 0.38 ± 0.02  | 0.75 ± 0.02  | 0.34 ± 0.02    | 0.39 ± 0.02  | 0.75 ± 0.01  |\n| Transformer (SimSort)| **0.72** ± 0.03    | **0.84** ± 0.02  | **0.82** ± 0.02  |**0.68** ± 0.03    |**0.82** ± 0.02  | **0.81** ± 0.02  |\n\nTransformers are particularly well-suited for spike detection due to their ability to model long-range dependencies and contextual interactions between time points, without the limitations of recurrence or fixed receptive fields. Furthermore, the self-attention mechanism allows the model to adaptively weigh different parts of the signal for detecting spike events under varying background conditions.\n\nTo evaluate the role of encoder architecture and representation learning objective in the spike identification model, we compared three configurations:  \n(1) a GRU encoder trained with contrastive loss (SimSort), \n(2) a Transformer encoder with the same loss,\n(3) a GRU encoder trained with a non-contrastive supervised objective.\n\nTable 3. Identification performance across objectives, encoders, and denoising\n\n| Objective        | Encoder     | Denoiser | ARI ± Std (Hybrid-static) | ARI ± Std (Hybrid-drift) |\n|------------------|-------------|----------|----------------------------|---------------------------|\n| Contrastive      | GRU         | ✓        | **0.91** ± 0.02                | **0.89** ± 0.03               |\n| Contrastive      | GRU         | ✗        | 0.88 ± 0.03                | 0.85 ± 0.02               |\n| Contrastive      | Transformer | ✓        | 0.89 ± 0.03                | 0.85 ± 0.03               |\n| Supervised       | GRU         | ✓        | 0.24 ± 0.02                | 0.26 ± 0.02               |\n\nAs shown in Table 3, the contrastive approach yields substantially better performance, indicating its critical role in robust spike identification. Among the contrastive models, the GRU encoder slightly outperforms the Transformer.\n\nWe also conducted an ablation study to evaluate the impact of the denoising module. By removing the denoiser while keeping the GRU encoder and contrastive learning unchanged, we observed a modest drop in performance, particularly in the drift setting. This indicates that while SimSort’s identification performance is primarily driven by contrastive learning, denoising contributes to robustness under more challenging conditions.\n\nThanks again for your valuable suggestions, we will include these results in the revised manuscript.\n\n### 4. How different data types (for example, spikes from different areas) influence the model performance? (Q4)\n\nThank you for the insightful question, we also have considered this problem. The follows are our answers. \n\nFor general spike sorting tasks, the specificity of the brain region or neuron type play a relatively limited role. Spike detection primarily relies on identifying temporally localized voltage deflections that exceed background noise -- a process more strongly influenced by signal-to-noise ratio and electrode geometry than by regional differences. For spike identification, our contrastive learning framework is designed to distinguish waveforms based on relative similarity, without relying on prior knowledge of cell types or waveform templates.\n\nWe also have shown empirical results about the generalizability of SimSort to different brain regions and animals (Figure 5). we constructed our simulation dataset using morphologically detailed neuron models from the **rat somatosensory cortex**. For real-data evaluation, we successfully applied SimSort to the tetrode recordings from the **mouse primary visual cortex**. This implies the specificity of the brain region or neuron type play a relatively limited role. \n\nTo further investigate the influence of brain-region variability on model performance, we plan to incorporate additional regions into the simulation dataset and the test dataset in future work. \n\nWe sincerely thank you again for your thoughtful and constructive feedback. We hope our response has addressed your concerns, and we would be happy to further discuss any remaining questions during the discussion phase."}, {"review_id": "rhb9dSQrE7", "reviewer": "Reviewer_1rAH", "summary": "The 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.", "questions": "Scientific 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", "limitations": "See above, no potential negative societal impact.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Main 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", "was_revised": true, "final_justification": "During the discussion period, the authors have addressed my concerns. Given their comments, I understand that the final manuscript is improved and I increased my grade to 5.", "rebuttal": "Thank you for your detailed and constructive review. We sincerely appreciate your positive assessment of SimSort’s clarity, reproducibility, and potential impact, as well as your thoughtful comments regarding the simulation dataset. Below we address your concerns point by point:\n\n### 1. Clarification on morphology and modeling assumptions (B1–B5, Q4)\n\nThank you for your insightful comments regarding the dataset design. We appreciate your careful reading and the suggestion to clarify several important modeling assumptions in the main text.\n\nWe confirm that the morphologies used in our simulations were 2D multi-compartmental neuron models. While the models include detailed dendritic and axonal arbors, these structures were embedded in two-dimensional space. Recurrent synaptic connections were omitted, and each neuron was independently driven by pink noise inputs injected at the soma.\n\nWe agree that omitting 3D morphology and recurrent connections limits the biophysical realism of the simulation. As you noted, this simplification may reduce spike synchrony and constrain waveform variability that arises from complex spatial arrangements of neurons. We will explicitly acknowledge this limitation in the revised manuscript.\n\nWe will also add the following clarifications to the revised paper:\n- Each 10-minute trial includes five neurons placed in a 100 μm × 100 μm × 100 μm volume surrounding the tetrode, with random rotations and placements.\n- Extracellular signals are generated using the line-source approximation, then summing contributions from all neurons.\n- Neurons were simulated independently with no recurrent connections.\n- Pink noise was the only driving input.\n\nWe will also move key simulation details from the appendix to the main text to improve accessibility.\n\n### 2. Resolving overlapping spikes across neuron (B5)\n\n>Is it possible/frequent that two neurons in the same time bin can emit a spike in the same time bin?\n\nGood point! In simulation, spike overlap is possible (multiple neurons to emit spikes within the same time bin), particularly under high firing rate conditions. Since the extracellular voltage is the linear sum of contributions from all active neurons, SimSort’s detection module is trained to identify spike events from such composite waveforms, while the task of assigning spikes to their respective source neurons is handled by the subsequent identification module. Overlapping spikes represent one of the key challenges that our contrastive representation learning framework is designed to address. \n\n> How is this handled?\n\nEven when two spikes occur within the same time bin, they typically originate from different spatial positions, resulting in distinct waveforms across channels due to spatial variation in the extracellular potential. This difference in waveform shape across channels provides useful cues for distinguishing overlapping events. An example of such spatial variation can be seen in Figure 5c, where spikes from different neurons exhibit different waveform patterns across the 4 channels.\n\nIn practice, we observed that SimSort can often detect and separate overlapping spikes when their peak times differ by more than 15 sampling points (~0.5 ms), with both events typically detected and identified correctly. We will include these observations in the revised manuscript.\n\n### 3. What additional benefits or difficulties should we encounter with more complex and detailed models? (Q1)\n\nWe agree that more biologically detailed models -- such as those with 3D morphologies, synaptic connectivity, and realistic network dynamics can offer valuable potential for spike sorting research. For example, recurrent connectivity can lead to spike time correlations and population synchrony, which increase the difficulty of resolving temporally overlapping events. In addition, incorporating diverse neuronal morphologies, spatial arrangements can result in more complex and variable extracellular waveforms, placing higher demands on the model’s ability to distinguish and represent waveform features robustly.\n\nImportantly, features such as spike-time correlations can naturally emerge in large-scale simulations even with simplified neuron models. When many independent units are simulated over extended durations, spikes from different neurons may occur closely in time by chance. This exposes the model to temporally ambiguous cases without requiring explicit connectivity or coordinated input.\n\nOn the other hand, highly detailed and complex neuron models present substantial challenges. They are not only computationally expensive, but also make it considerably more difficult to accurately compute and model extracellular potentials because of the increased complexity of morphology, and signal propagation. In this work, we adopt a relatively simplified yet biophysically grounded simulation approach as a foundational step toward developing robust and generalizable spike sorting methods using synthetic data.\n\nWe appreciate the suggestion to include references to more advanced neuron modeling efforts such as Laquitaine et al. (2025), Markram et al. (2015), and Billeh et al. (2019). We agree that incorporating more biologically realistic 3D morphologies and connectivity patterns holds strong potential to further enhance deep learning methods for spike sorting. Such advances may also benefit broader applications in neuroscience and brain–computer interfaces. We will incorporate these references and expand our discussion accordingly in the revised manuscript.\n\n### 4. Failure modes and validation challenges (Q2, Q3)\n\nThis is a valuable comment. Our model may fail in several challenging scenarios. The detector may misclassify noise events with spike-like waveforms as spikes.  Under extremely low SNR conditions, the identification model may struggle to extract stable and discriminative features, even with noise augmentation during training. In long-term recordings with drift, spikes from the same neuron may be split into separate clusters, as the model does not explicitly account for waveform invariance across channels. Additionally, when two spikes occur within a short temporal window (e.g., less than 15 sampling points apart), the smaller one is often ignored to avoid false positives, potentially leading to missed detections. We will add more discussions about failure cases in the revised manuscript.\n\nValidating the model remains challenging, particularly due to the lack of ground-truth labels in real in vivo recordings. While paired intracellular–extracellular recordings or optogenetic tagging provide gold-standard validation, they are difficult to scale. As a practical alternative, we perform indirect validation by analyzing the sensory tuning of sorted V1 neurons, where consistent and selective response patterns support the reliability of the sorting results.\n\nTo better evaluate the model’s limitations, diverse datasets will be valuable. Hybrid datasets with real background noise and drift, long-term recordings, paired ground-truth data, and more realistic simulations with recurrent dynamics can expose specific failure cases and challenge the model’s temporal resolution and representational robustness. Such benchmarks will be essential for future improvements in generalizability and reliability.\n\n### 5. Figure readability and expanded discussion of related work (A1-A2, C)\n\nWe apologize for the small font size in Figures 5 and 6. In the revision, we will enlarge label fonts to improve readability. We will also reference the relevant appendix sections more clearly within the main text to guide the reader. Additionally, we will expand the discussion of prior deep learning models for spike detection and waveform representation in the revised version, to better contextualize our approach within the existing literature.\n\nWe greatly appreciate the time and effort you put into reviewing our work. Please feel free to raise any additional points during the discussion—we are happy to clarify or expand as needed."}, {"review_id": "z7Tl8MdQBd", "reviewer": "Reviewer_N3CQ", "summary": "The 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.", "questions": "One 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.", "limitations": "I 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.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: 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).", "was_revised": true, "final_justification": "I am giving a \"borderline accept\" as the paper presents a cool, novel framework with rigorous evaluation for spike sorting in tetrode probes, but I have doubts about the generalization capacities of the algorithm to multiple probes and brain regions.", "rebuttal": "Thank you for your detailed and thoughtful review. We appreciate your suggestions regarding the evaluation and the need for a more thorough discussion of the limitations. We address your concerns point by point below.\n\n### 1. Real-data comparison with Kilosort and detection of low-amplitude spikes (W1, Q1)\n\n>  SimSort may capture only \"easy\" spikes?\n\nThank you for raising this important question. We agree that evaluating a spike sorting method’s sensitivity to low-amplitude events is essential. To assess this, we compared the total number of spikes detected by SimSort and Kilosort2.5 across six real tetrode recordings. We report the overall number of detected spikes per recording as a preliminary comparison:\n\n|Recording|Kilosort|SimSort|\n|--|--|--|\n|1|11,495|12,891|\n|2|8,906|16,855|\n|3|16,257|18,518|\n|4|5,538|8,933|\n|5|16,685|13,498|\n|6|19,063|16,750|\n\nSimSort detected a comparable or higher number of spikes in four out of six recordings. In recordings 5 and 6, however, SimSort detected fewer spikes than Kilosort. Upon manual inspection, we found that many of the Kilosort-only units in these recordings were detected primarily on a single channel, with no corresponding activity on adjacent channels.\n\nThis detection pattern may reflect the statistical structure of the simulated data used during SimSort pretraining. In our simulation framework, most spikes are generated by neurons whose extracellular potentials are visible across several nearby electrodes. Although we included randomized channel-wise noise augmentation during training, it remains uncommon for simulated spikes to be confined to a single channel while others contain only background fluctuations. This may have led SimSort to be less sensitive to spatially sparse spikes. We consider this a potential limitation of the current framework, which we will discuss in the revised limitations section, and improve in the future version.\n\nTo further investigate whether SimSort is biased toward high-amplitude spikes, we analyzed the SNR distribution of all spikes detected by SimSort in the six real recordings:\n\n|Recording|TotalSpikes|MeanSNR|MedianSNR|MaxSNR|MinSNR|\n|---|---|---|---|---|---|\n|1|12,891|5.71|5.48|15.69|1.63|\n|2|16,855|5.85|5.45|17.86|2.05|\n|3|18,518|8.05|7.19|22.16|1.93|\n|4|8,933|5.65|5.10|14.43|1.82|\n|5|13,498|5.55|5.05|13.90|1.56|\n|6|16,750|5.68|5.51|14.07|1.94|\n\nThese results show that **SimSort detects a wide range of spikes in terms of SNR, including many low-amplitude events with SNR below 2.0.** This indicates that the model is not limited to high-SNR spikes and is capable of recovering lower-amplitude spikes in real recordings.\n> The training dataset also seems to have very high SNR (figure S10). Why not increase the noise here to match real recordings?\n\nThank you for pointing this out. The trace shown in Figure S10 was selected primarily for visualization purposes and may not reflect the full range of signal-to-noise ratios (SNR) in our training dataset. To address the concern that our training data might have uniformly high SNR, we clarify that the simulated dataset includes a broad spectrum of SNRs, encompassing a substantial number of low-SNR spikes. SNR was computed using a robust method based on the median absolute deviation (MAD). A summary of the SNR distribution is provided below:\n\n| Metric         | Value         |\n|----------------|---------------|\n| # Spikes       | 144,690,960   |\n| SNR Range      | 0.62 – 169.89 |\n| SNR Median     | 22.52         |\n\n### 2. Some questions about the preprocessing steps of real recordings (Q2, Q3)\n> Is there drift in the real recordings?\n\nThe real recordings in this study are relatively short (∼250 seconds), during which significant drift is unlikely to occur. We did not observe noticeable drift patterns in the data and therefore did not apply drift correction. We agree, however, that drift can be a critical factor in longer or high-density recordings. We appreciate the suggestion to consider tools such as DREDGE, and plan to incorporate drift quantification and, if necessary, correction modules into future versions of the pipeline.\n\nSimSort’s current contrastive learning framework does not include augmentation for drift. This was a deliberate design choice: for tetrode data, the concept of drift is inherently ambiguous due to the small number of channels and limited spatial resolution. As a result, we did not apply channel invariances during training. Nonetheless, we see drift modeling as an important direction when adapting SimSort to high-density probes.\n> Should the whitening step be included in the preprocessing pipeline?\n\nThank you for the suggestion. In this work, we followed the preprocessing pipeline used in established spike sorting methods such as Kilosort and MountainSort, which includes spatial whitening in signal preprocessing.\n\nTo assess its impact, we conducted an ablation study by replacing spatial whitening with data standardization. As shown below, sorting performance decreased consistently across both datasets:\n\n|Method|Accuracy(Static)|Recall(Static)|Precision(Static)|Accuracy(Drift)|Recall(Drift)|Precision(Drift)|\n|--|--|--|--|--|--|--|\n|**SimSort** (w/whitening)|**0.62**±0.04|**0.68**±0.03|**0.77**±0.03|**0.56**±0.03|**0.63**±0.03|0.69±0.03|\n|No Spatial Whitening (z-score)|0.47±0.03|0.56±0.03|0.64±0.03|0.47±0.02|0.56±0.02|0.62±0.02|\n\nThe results show a consistent decrease in accuracy, recall, and precision when spatial whitening is removed from the preprocessing pipeline.\n### 3. Full pipeline evaluation on IBL Neuropixels data (Q4\n>Why not have tried the full spike sorting pipeline on IBL Neuropixels dataset?\n\nWe appreciate your suggestion to evaluate SimSort as a full spike sorting pipeline on IBL Neuropixels recordings.\n\nSimSort was originally developed and pretrained on tetrode recordings, and directly applying it to high-density Neuropixels data would require adapting the detection model to handle broader spatial input. In this submission, we focused on evaluating the performance of identification model using neuropixels waveforms extracted by Kilosort2.5, following the CEED protocol for fair comparison of contrastive learning approaches.\n\nWe thank for your helpful suggestion to incorporate localization features into the contrastive representation. We agree this could further enhance the model’s applicability to high-density recordings and will test it in future extensions.\n\n### 4. Whether SimSort find additional units in hybrid dataset? (L1)\n>  Could the authors clarify whether or not the sorter learns additional units, and evaluate the quality of these units? \n\nThank you for raising this important point. To examine whether SimSort is limited to detecting only the injected ground-truth spikes, we compared the total number of detected spikes on the hybrid-static dataset. The results are summarized below:\n|Recording ID|SimSort Detected|Ground TruthSpikes|Kilosort Detected|\n|--|--|--|--|\n|rec_4c_1200s_11|74,249|72,168|60,767|\n|rec_4c_1200s_21|94,154|78,274|78,220|\n|rec_4c_1200s_31|89,216|87,027|79,308|\n|rec_4c_600s_11|37,173|36,120|30,551|\n|rec_4c_600s_12|37,119|36,048|30,547|\n|rec_4c_600s_21|46,996|52,874|38,851|\n|rec_4c_600s_22|47,142|47,556|38,837|\n|rec_4c_600s_31|44,619|43,367|40,380|\n|rec_4c_600s_32|44,605|43,305|39,029|\n\nTo better address this question, we respectfully refer you to Figures S11 and S13. Figure S11 shows the identification results based solely on spikes with ground-truth annotations, while Figure S13 shows the output when SimSort performs both detection and identification on the raw hybrid recordings. In Figure S13, more units are identified than in Figure S11, including additional clusters that are not part of the injected ground-truth set. Many of these clusters exhibit well-formed waveforms, suggesting that they likely correspond to meaningful neural activity.\n\nA similar observation can be made from the comparison between Figures S12 and S14. These results indicate that SimSort is capable of discovering additional spike events beyond the injected ground-truth units.\n\n### 6. Statistical significance in Table S4 (L2)\n\nThank you for pointing out this important issue, and we apologize for the omission. The comparison between SimSort and CEED yields p = 0.0012, indicating a statistically significant difference (p < 0.05). The comparison between SimSort and CEED_UMAP yields p = 0.0554 and is not statistically significant. We will include these results in Table S4 and move the table to the main text in the final version.\n\nWe sincerely appreciate your thoughtful and constructive review. We understand that a key concern is the comparison with existing spike sorters on real-world data. Due to the format constraints of the rebuttal stage, we have presented as much comparative analysis with Kilosort as possible to address this point. We also plan to include a more comprehensive discussion of these issues in the limitations section of the final version. We hope these efforts help clarify the performance of SimSort. If we have misunderstood any part of your feedback, or if you have further questions or suggestions, we would be happy to continue the discussion."}]

Author rebuttal:

Meta-review: This paper introduces a simulation-driven deep learning framework for spike sorting. The authors show that large-scale biophysically realistic simulations can enable zero-shot transfer to real tetrode recordings.

During the discussion, reviewers agreed that the paper makes a timely contribution that could be potentially impactful by combining a large simulated dataset with a deep learning pipeline. The main concerns were (i) the limited scope to tetrode recordings and unclear generalization to other probes (e.g., Neuropixels), (ii) the need for more direct comparisons to state-of-the-art spike sorting methods like KiloSort4, (iii) missing details about the simulation setup, and (iv) additional ablations clarifying what drives performance. The authors responded with further experiments (including comparisons with KiloSort, ablations, runtime analysis, and clearer limitations), which addressed (ii)–(iv). The restriction to tetrodes remained and was acknowledged as such but the authors, who argued that tetrodes are still an important method in the field.

As a result, the paper's scores remained borderline after the rebuttal. The restriction to tetrodes could be perceived as severe one in a time where the field is moving quickly towards large-scale high-density probes like Neuropixels. On the other hand, the authors argue (and some reviewers agree) that tetrodes remain in use in several labs. In addition, the reviewers agree that the paper is technically solid and proposes a new method. As a result of the final discussion among the reviewers, I believe the paper has its merits and recommend accepting it.
Decision: Accept (poster)