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

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer2
evaluation: 2: Poor
intersection: 5: Outstanding
importance_comment: The importance of the topic covered in this paper - namely, the role of unsupervised learning in biology and artificial intelligence - is very high. However, this point has been highlighted frequently in the past, and this paper stops short of offering any concrete or novel contributions.
clarity: 4: Well-written
technical_rigor: 1: Not convincing
intersection_comment: This topic surely should and will be discussed at this workshop.
rigor_comment: No concrete results or proposals are offered to solve the (important) problem of unsupervised learning in AI and the brain. Experimental findings regarding NMDA-mediated plasticity in cortex are briefly reviewed, but not connected back to this problem, providing little insight into how to solve it.
comment: I strongly believe the topics covered in this paper should be discussed in this workshop. However, I do not believe this paper stands to contribute much to such a discussion, as it provides few novel insights or directions. One somewhat novel idea that this reviewer was able to walk away with was the suggestion that, in order to obtain good models of biological learning, we should focus on solving ecologically relevant statistical problems in AI.
importance: 3: Important
title: A critical topic for discussion at the intersection of neuroscience and AI, but few novel ideas proposed
category: Common question to both AI & Neuro
clarity_comment: Generally well-written.

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer3
evaluation: 3: Good
intersection: 5: Outstanding
importance_comment: The importance of understanding unsupervised learning in the brain cannot be understated. If we could emulate the unsupervised learning used by the brain in ANNs it would be a massive leap forward in AI. Thus, the goals of this submission are very important. However, this submission only gestures at potential solutions, so the importance of this specific contribution is more limited.
clarity: 5: Impeccable
technical_rigor: 1: Not convincing
intersection_comment: It is a perfect mix of neuroscience and AI.
rigor_comment: This submission contains much speculation, and some discussion of known biological facts. But, there is no analytical or empirical demonstration that the biological mechanisms described actually would provide the sort of unsupervised learning proposed. Furthermore, the claim that such unsupervised mechanisms would prevent susceptibility to adversarial attacks is unconvincing, and not backed up by any data or math.
comment: There are some great ideas in here, and potentially excellent topics for discussion. But, there is very little in terms of actual material contributions. It is essentially an opinion piece.
importance: 4: Very important
title: An interesting proposal, but still very preliminary/loose
category: Neuro->AI
clarity_comment: It's a very well written submission.

From: NeurIPS.cc/2019/Workshop/Neuro_AI/Paper47/AnonReviewer1
title: Discussion of various ideas in neuroscience and machine learning, but details largely absent
category: Neuro->AI
intersection_comment: The submission discusses both fields. However, as mentioned, the details in connecting these areas are largely absent.
evaluation: 2: Poor
importance_comment: The submission discusses a few observations about the neocortex and emphasizes the importance of unsupervised learning of representations. However, the connections between these points are unclear, and it is therefore difficult to determine if any novel ideas are proposed. Because the submission does not elaborate on how neocortical principles could assist in improving unsupervised representation learning, the importance of this work seems lacking.
rigor_comment: The submission does not present any empirical results or theoretical formulations. Technical concepts are not explored in detail.
importance: 2: Marginally important
clarity_comment: The submission was, at times, difficult to follow. For instance, connections between the neuroscience discussion (e.g. cliques of pyramidal neurons) and better forms of representation learning are unclear.
intersection: 4: High
technical_rigor: 2: Marginally convincing
comment: I would challenge the authors to expand the discussion around Martinotti neurons to include a model of how such mechanisms could facilitate unsupervised / representation learning.

The figure could be improved by converting the hand-drawn diagrams into more professional looking graphics.

The introduction contains several overstated claims. For instance, it’s difficult to say whether the “task” of synaptic plasticity in the neocortex is to learn disentangled representations. 
clarity: 2: Can get the general idea