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

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper19/AnonReviewer1
category: AI->Neuro
title: Interesting paper on decoding cognitive states from fMRI data with graph convolutional NNs
importance: 3: Important
comment: The contribution is relevant to the workshop and is well written.
evaluation: 3: Good
intersection: 3: Medium
rigor_comment: The methods appear to be correct and are well explained.
clarity: 4: Well-written
intersection_comment: Interesting use of ML methods to analyze neural datasets.
technical_rigor: 3: Convincing
clarity_comment: The paper is well written but is well over the limit for this workshop. Its clear that the authors didn't try to shorten their paper for this workshop.

Figure 2 says "Sates" instead of "States"
importance_comment: Neural decoding is an important problem with applications throughout neuroscience.

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper19/AnonReviewer2
title: Well-written and solid paper providing biologically meaningful results about neural grounding of cognitive states
category: AI->Neuro
intersection_comment: Nice application of GCNs for cognitive state decoding.
evaluation: 5: Excellent
importance_comment: This paper convincingly shows how graph convolutional networks can provide very robust and interpretable predictions of cognitive states from brain activity.
rigor_comment: This is simply a solid paper which includes various control analyses.
importance: 4: Very important
clarity_comment: Clear and well-written
intersection: 4: High
technical_rigor: 4: Very convincing
comment: Thorough theoretically rigorous application of a new approach for rapid decoding of cognitive states.
clarity: 5: Impeccable