LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.
 Thank you for a pleasurable and informative read, I consider the writing and structure of the paper to be coherent and well written. Given an end-to-end learning of neural motifs, a great deal of time can be avoided, reducing the several intermediary steps required to detect motifs from calcium imaging. This paper may very well improve researchers efficiency, in particular when working with calcium imaging. The question remain to what extent these ideas may be useful in other imaging modalities, i.e. fMRI. My main critique would be to be more explicit about why the VAE you propose, is superior to other models in the generative modelling domain.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Thank you for a pleasurable and informative read, I consider the writing and structure of the paper to be coherent and well written. Given an end-to-end learning of neural motifs, a great deal of time can be avoided, reducing the several intermediary steps required to detect motifs from calcium imaging. This paper may very well improve researchers efficiency, in particular when working with calcium imaging. The question remain to what extent these ideas may be useful in other imaging modalities, i.e. fMRI. My main critique would be to be more explicit about why the VAE you propose, is superior to other models in the generative modelling domain.
assistant
 Thank you for a pleasurable and informative read, I consider the writing and structure of the paper to be coherent and well written. Given an end-to-end learning of neural motifs, a great deal of time can be avoided, reducing the several intermediary steps required to detect motifs from calcium imaging. This paper may very well improve researchers efficiency, in particular when working with calcium imaging. The question remain to what extent these ideas may be useful in other imaging modalities, i.e. fMRI. My main critique would be to be more explicit about why the VAE you propose, is superior to other models in the generative modelling domain.