Venue: NeurIPS 2019 Workshop Neuro_AI

Reviews:

final_score: rating: null
review_content: importance_comment: ANNs based on the olfactory circuit, which uses dimensionality compression followed by expansion, have already provided performance on nearest-neighbor lookup comparable with modern hashing algorithms. Continued research into understanding why the olfactory circuit has evolved its unique architecture could provide key insight into designing a new class of biological-inspired neural networks. 
rigor_comment: The findings in this paper appear strong, as they match experimental findings of PN-KC connectivity. I do have some questions about the model:
- Do you see the same connectivity results if you include an APL component in the model? 
- Do you see the same results if you implement divisive normalization in the model? (Something like a softmax function between the ORN and PN)
- Does each odor class have the same number of odors, so it assumes that the fly encounters all odors evenly, or are some odor classes rarer than others?
clarity_comment: This paper is well-written, I have some small questions:
- Is the data used synthetic data, or is it based on a real dataset? (Hallem?)
- How was the data partitioned into training/validation/test sets?
- For the odor classification, what was the precision and recall across odors?
- "activities of different PNs are uncorrelated", how was this determined?
- At the end of a paragraph, "The formation of glomeruli is minimally dependent on input noise", what does this refer to?
- Fig. 3: Does this include changes to the number of PNs and KCs? Or just ORs?
intersection_comment: This paper trains an artificial feed-forward neural network inspired by the architecture of the fly olfaction circuit. The authors perform experiments in silico and examine how the architecture of the model makes the network resistant to fluctuations in the weights.
comment: I think this paper is quite good, as provides a hypothesis on why the olfactory circuit has evolved its distinct architecture. I had some comments on what kind of data was used to train the model (real or synthetic), and also on if the same connectivity results persist if the model includes components such as divisive normalization and the APL.
review_title: Understanding the olfactory circuit by perturbing it in silico

final_score: rating: null
review_content: importance_comment: This work is important because it uses stochastic gradient descent (SGD) as an optimisation tool, rather than a 'learning model' as is so commonly done these days. The authors then argue that certain quantities in the model are robust to optimisation / optimal under certain constraints by converging to these parameters  values for a wide range of initial conditions.
rigor_comment: Though the space search is still numerical, the search methodology (i.e. optimisation) is adequate for the claims made.
An interesting step further would be to look at how 'sharp' posteriors over these parameters are (e.g. as in work like [Lueckmann, J. et al. Amortised inference for mechanistic models of neural dynamics; COSYNE2019])
clarity_comment: Nice clear paper.
intersection_comment: This is really a theoretical neuroscience paper, I think.
comment: This work is interesting and has nice conclusions, though its relevance to this exact workshop maybe a little off. It does not specify any sort of "idea cross pollination" from AI<->neuroscience. Still I think many in the attending audience will find it interesting.
review_title: Using SGD as an optimisation tool to find robust principles in a neuroscience model

Decision: Accept (Poster)