Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions. This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions. Here, we developed an analytical method to factorize a large number of retinal ganglion cells* spike trains into a robust low-dimensional representation that captures efficiently both their spatial and temporal information. In particular, we extended previously used single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity. On data recorded from retinal ganglion cells with strong pre-stimulus baseline, we showed that in situations were the stimulus elicits a strong change in firing rate, our extensions yield a boost in stimulus decoding performance. Our results thus suggest that taking into account the baseline can be important for finding a compact information-rich representation of neural activity.
 In this contribution, the authors propose an improvement of a tensor decomposition method for decoding spike train. Relying on a non-negative matrix factorization, the authors tackle the influence of the baseline activity on the decomposition. The main consequence is that the retrieved components are not necessarily non-negative and the proposed decomposition rely on signed activation coefficients. An experimental validation shows that for high frequency baseline (> 0.7 Hz), the baseline corrected algorithm yields better classification results than non-corrected version (and other common factorization techniques). The objective function is defined with a Frobenius norm, which has an important influence on the obtained solutions, as it could be seen on Figure 2. The proposed method seems to provide a more discriminant factorization than the NMF one, at the expense of the sparsity of spatial and temporal components, impeding the biological interpretability. A possible solution is to add a regularization term to the objective function to ensure the sparsity of the factorization.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions. This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions. Here, we developed an analytical method to factorize a large number of retinal ganglion cells* spike trains into a robust low-dimensional representation that captures efficiently both their spatial and temporal information. In particular, we extended previously used single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity. On data recorded from retinal ganglion cells with strong pre-stimulus baseline, we showed that in situations were the stimulus elicits a strong change in firing rate, our extensions yield a boost in stimulus decoding performance. Our results thus suggest that taking into account the baseline can be important for finding a compact information-rich representation of neural activity.
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>



 In this contribution, the authors propose an improvement of a tensor decomposition method for decoding spike train. Relying on a non-negative matrix factorization, the authors tackle the influence of the baseline activity on the decomposition. The main consequence is that the retrieved components are not necessarily non-negative and the proposed decomposition rely on signed activation coefficients. An experimental validation shows that for high frequency baseline (> 0.7 Hz), the baseline corrected algorithm yields better classification results than non-corrected version (and other common factorization techniques). The objective function is defined with a Frobenius norm, which has an important influence on the obtained solutions, as it could be seen on Figure 2. The proposed method seems to provide a more discriminant factorization than the NMF one, at the expense of the sparsity of spatial and temporal components, impeding the biological interpretability. A possible solution is to add a regularization term to the objective function to ensure the sparsity of the factorization.
assistant
 In this contribution, the authors propose an improvement of a tensor decomposition method for decoding spike train. Relying on a non-negative matrix factorization, the authors tackle the influence of the baseline activity on the decomposition. The main consequence is that the retrieved components are not necessarily non-negative and the proposed decomposition rely on signed activation coefficients. An experimental validation shows that for high frequency baseline (> 0.7 Hz), the baseline corrected algorithm yields better classification results than non-corrected version (and other common factorization techniques). The objective function is defined with a Frobenius norm, which has an important influence on the obtained solutions, as it could be seen on Figure 2. The proposed method seems to provide a more discriminant factorization than the NMF one, at the expense of the sparsity of spatial and temporal components, impeding the biological interpretability. A possible solution is to add a regularization term to the objective function to ensure the sparsity of the factorization.