Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors develop a new analysis pipeline for the detection and localization of spikes in Neuropixel recordings. The pipeline is based on a denoising step applied to all channels, detection of spikes via their amplitudes, triangulation across channels to localize spikes, and finally a motion correction step to counter potential movements of the electrode. The efficiency of the method is demonstrated by comparing results against several state-of-the art methods.
main_review: The paper is well written and everything seems technically correct. The results are nicely demonstrated on various data sets, including those with (partial) ground truth data (motion). I think this paper will be of great interest to anyone recording neural activities with Neuropixel probes. 

The only potential downside I see is that the proposed analysis pipeline consists largely of a sequence of analysis methods taken from the literature. While several of these methods are applied for the first time to the problem of spike localization (such as triangulation), from a purely theoretical or algorithmic point of view, the paper provides little (new) insights. In other words, the paper may not be the best fit for the Neurips audience.

limitations_and_societal_impact: Has been addressed.
review_title: null

final_score: rating: 10: Top 5% of accepted NeurIPS papers, seminal paper
review_content: summary: This paper proposes a novel spike sorting algorithm, specifically for dense arrays such as the NeuroPixels series of electrodes. This is an extensive and detailed report. The comparisons to other approaches are up-to-date and thorough. They present some nice analysis of electrode drift, showing that the new algorithm performs well. It is well written, clear, and includes extensive figures to illustrate the work and results. This is an excellent piece of work.
main_review: They use a point-source model for neurons, then denoise and supress collisions, and finally do triangulation in 3D. This apparently improves clustering fidelity. They also track electrode drift and their algorithm offers excellent performance for that aspect as well.

This work can have impact on both fundamental science, as well as BCI work-- although the latter often disregards spike sorting.

It would be helpful if the report could show some spike raster data comparing their approach and that of Hurwitz and CoM. For example, in Fig 2 there are some clusters that were split in their algorithm and not with Hurwitz et al.'s algorithm. What do those spike rasters look like? Sometimes good spike sorting is evident when aligned to a stimulus or task parameter, e.g., Fig 3 in https://onlinelibrary.wiley.com/doi/epdf/10.1111/joa.12228

The main limitation is that there is no ground truth data, or calibration data. How can we say that this algorithm is better when we don't know what the truth is? This is a general problem for the field, and not a specific problem for this paper, but it does make it awkward to strongly advocate for a particular spike sorting method. Still, I am enthusiastic about this work and strongly support it.

"crisper estimates of neural clusters over time" - This is an unclear, qualitative way to say it. Please use more precise, quantitative language. Perhaps: "more stable unit localization over time, as measured by image entropy"? I defer to the authors, and I will not insist on this, but I would like to encourse the use of more precise, quantitive language.

The model (a point neuron model) is rather simple, which is maybe good, but where does it break down? Spike signals can emerge from different compartments of a neuron. Spike amplitude can be affected by not only distance, but also geometry (e.g., angle of an axon relative to the electrode) and intervening tissue (e.g., glia in front of an electrode). I guess those would just get folded in, and not cause problems as long as they are stable in time-- degrading the precision of the localization, but still aiding the clustering/sorting. I would appreciate the authors commenting on that.

Purely curiosity: The 3-step Poisson denoising is interesting, and seems to work well. I wonder what the author(s) think about the DeepInterpolation work. https://github.com/AllenInstitute/deepinterpolation Have they tried to replace their denoising with this approach and examined performance?
limitations_and_societal_impact: No concerns.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: In “Three-dimensional spike localization and improved motion correction for Neuropixels recordings”, the authors describe a pipeline for denoising and localizing spikes from dense extracellular recordings of neurons. Specifically, recordings are denoised using a previously published neural network based technique, then 3D locations are inferred using a straightforward point-source model. Finally, spikes are localized using Poisson image denoising.
main_review: The manuscript is well-written, and I found the methods easy to follow and design decision are reasonable and justified. This paper in its current form is well-above the acceptance threshold for NeurIPS, and I only have a few comments:

Major:

1)	In the comparison with Hurwitz’s method, my understanding is that the authors do not use any spike amplitude features (that is just x and z are used for Hurwitz). This must be added for a more apples-to-apples comparison between the two methods. Even if it is noisy, the authors should add peak-to-peak estimates to Hurwitz’s feature set.
2)	Re: clustering it would be useful to know how clustering using x, y, and alpha compare with clustering using typical waveform features (e.g. PCA decomposition of the time-course). Directly demonstrating that including position information aids unsupervised clustering would be useful here. Would, for example, someone using Kilosort want to add these position features for clustering neurons?

Minor:

1)	Does the neural network denoising used by the authors induce any distortion of the spike waveforms in these recordings?   

limitations_and_societal_impact: Yes, no issues here.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: The authors describe a pipeline for processing recordings from neuropixel probes which are the current state of the art in in-vivo recordings from single neurons in the brain. 
main_review: The authors describe a pipeline for processing recordings from neuropixel probes which are the current state of the art in in-vivo recordings from single neurons in the brain. Their pipeline consists of (a) a point neuron model for spike localization in 3D, (b) a denoising step for the waveforms and the resulting localizations, (c) motion registration and (d) spatial clustering. This is an interesting approach, but the way the paper is presented this seems more like a description and validation of a toolbox than a machine learning application paper. Also, step (b) is simply reused from another paper (which is totally fine, but hard to sell as a contribution then). 

An additional issue with the paper is that in many instances (e.g. the denoising step for the waveforms and the images) the result is not really validated and only very briefly explained. Why is it “correct” to remove all the events in line 2 of the figure to the right? I suppose these are collisions, but isn’t it dangerous to remove them altogether? Weren’t real spike the cause of these as well?

For the image denoising, how does it lead to different results than simply smoothing the image with a Gaussian kernel? The authors state in line 98 a variety of methods they actually could use for denoising, which was it? Does the choice of method influence the result? Finally, I am not convinced taking the square root of count data and applying Gaussian denoising should be called Poisson denoising. 

The remaining results look very promising, yet extremely dense for such a conference paper. Therefore, I would recommend a more extended format at a neuroinformatics/comp neuro journal venue. After considering the rebuttal and the discussion, I am still not convinced that this form of paper with extensive supplement is ideal for NeurIPS, but can see the point that it is within scope.

limitations_and_societal_impact: Limitations were only superficially discussed, but I don't see negative societal impact of this work. The authors could add a discussion on potential improvements.
review_title: null

Meta-review: This work brings together a number of algorithms to provide a method to detect the 3D location of cells from data collected with new high-density electrode arrays: Neuropixels. All reviewers appreciated the analysis developed by the authors in this work. The main discussion centered around the scope of NeurIPS with respect to engineered solutions to neural imaging problems. The authors, as well as some reviewers, pointed out the historical presence of such papers at NeurIPS, as well as the stated scope. Thus I am happy to recommend this paper for acceptance at NeurIPS.
Decision: Accept (Poster)