From: NeurIPS.cc/2019/Workshop/Neuro_AI/Program_Chairs
title: Paper Decision
decision: Accept (Poster)

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer2
title: Good start but some details unclear 
importance: 3: Important
importance_comment: Understanding cortical acoustic processing is an important neuroscience goal. This paper doesn't however motivate the specific model being chosen, or what different layers mean. It is stated more like a prediction task instead of an understanding the brain task.
rigor_comment: From what I understood, the R2 is being computed as the maximum over an ROI, at least in one part of the paper if not all. the maximum is a very noisy statistic and is not very reliable as a metric for model fitting and improvement. 

From the paper, it seems that the authors picked the best fold to interpret (after looking at the results). This is effectively double dipping and negatively affects reproducibility.

R2 values in fMRI single trials are typically low and that is ok. The solution is not to use the maximum (if I understood correctly the motivation). The authors should be reporting single voxel metrics (over the brain) or should be computing some mean statistics. 
clarity_comment: The paper is understandable but some methods crucial methods details are left out (3.1 and 3.2)
clarity: 3: Average readability
evaluation: 2: Poor
intersection_comment: Using an AI algorithm as a model of what the brain is doing. Although here how the model (SoundNet) could be an analogy of the brain (e.g. what could the different layers correspond to) could be more elaborated on.
intersection: 4: High
technical_rigor: 2: Marginally convincing
category: AI->Neuro

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer3
evaluation: 3: Good
intersection: 4: High
importance_comment: Not sure how much we can gain from this sort of study. The authors show that deeper layers of a pre-trained auditory network can be used to predict fMRI responses, albeit not very well. But what can we conclude from this? Would the same be true of a different auditory network with very different properties? How much does it depend on the specific structure of that network? Could it just be the case that higher level features appear deeper in the network and correspond to areas recorded by fMRI?
clarity: 4: Well-written
technical_rigor: 3: Convincing
intersection_comment: Definitely relevant, a similar approach to what has been tried with much success in vision.
rigor_comment: All seemed reasonable, but I would have liked to have seen controls against different architectures.
comment: I've given this the "good" evaluation because even though I personally am not convinced by this type of approach, it seems to be reasonably well done and I know that a number of people do find it useful and convincing.
importance: 2: Marginally important
title: Deeper layers of a pre-trained auditory model can be used to partly predict fMRI responses, but not sure what we can conclude from that
category: AI->Neuro
clarity_comment: No problem understanding this work.

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper38/AnonReviewer1
title: Straightforward application of DNN-based encoding models in the auditory domain
category: AI->Neuro
intersection_comment: Use of DNNs to explain observed brain responses falls right at the intersection.
evaluation: 2: Poor
importance_comment: This paper provides a straightforward application of SoundNet to predict fMRI responses to an audio stream. The question remains to what extent the presented results provide new results beyond those of https://www.cell.com/neuron/abstract/S0896-6273(18)30250-2 and https://arxiv.org/abs/1606.02627.
rigor_comment: Standard statistics are performed. Thresholding for threshold map seems quite arbitrarily chosen. It remains unclear which stimulus features are driving the response predictions.
importance: 2: Marginally important
clarity_comment: Some typos. E.g. improvising => improving; this results; ...


intersection: 4: High
technical_rigor: 2: Marginally convincing
comment: + Use of audio-based DNNs can provide interesting insights into which stimulus properties drive neural responses
+ DLPFC results could potentially point to novel properties that are predictive of responses

- Novelty compared to existing work unclear
- Insights about what stimulus properties are driving the predicted responses will strengthen the paper
clarity: 3: Average readability