Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: In this paper the authors introduce a model consisting of multiple connected RNNs which are trained to perform decision making tasks.
It is shown that imposing specific biological constraints (e.g. Dale's law) is important to yield biological solutions. Their results support the modular information storage often attributed to the brain, and opens the door for comparing multi-brain area experimental observations with models.
main_review: The authors built a model consisting of 3 RNNs, and tested the necessary ingredients needed to capture experimental observations. Their model proposes an explanation for why information about colour is lost in the dorsal premotor cortex recordings, which is due to colour information becoming irrelevant in later computations. Overall this detailed study brings novel, interesting ideas and makes some points that would be primarily of interest for neuroscience, but also for deep learning. 

Major points:
 - A proper discussion on related work is missing. There is work in deep learning using multiple RNNs, it would be good to discuss this. Also, there is some (not much I agree) on using multiple RNNs. For example: Kang B, Druckmann S (2020) Approaches to inferring multi-regional interactions from simultaneous population recordings, Current Opinion in Neurobiology (Review)
 - Also I failed to find a discussion on the mapping between the Area 1 and 2 with specific areas in the brain.
 - What would happen if more brain areas would be added? Presumably there are more brain areas than just one (area 2) between (the visual cortex, area 1) and the premotor cortex (area 3).
 - It should be discussed that using BPTT to train RNNs is not plausible, and that using more plausible training algorithms may impact the representations learnt and therefore the results.

Minor points:
 - Fig. 1 is not very clear. The caption does not refer clearly to the panels. Why use a) if there is only one panel?
 - It would be good to make it clear which training algorithm was used in the main text. I know its stated in the supplementary material, but it can be briefly stated in the main text.
limitations_and_societal_impact: No proper contrast with previous work.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The paper presents a multi-compartment recurrent neural network model. The network is trained to perform decision tasks similarly to non-human primates, and the network’s activity is compared to population activity recorded in primate cortex. Similarity between the artificial network and real data are compared with a focus on how similarity depends on biologically plausible constraints, such as Dale’s law. Analysis of the artificial networks showed how relevant information was propagated between modules to achieve the pMD-like choice encoding in higher areas.
main_review: The results build on current applications of RNNs in systems neuroscience. The results show how easy-to-incorporate biological constraints are important for comparing artificial networks to neural data, and such considerations will be important for many experimentalists. 

The methods and results were generally clear. However, a couple minor edits could improve clarity. In particular, the conclusions of Figure 3 A-E were a little difficult to interpret. Labeling which quantities on the x-axis were more “brain-like” would help.

Although the data comparison includes only 1 region, this analysis offers some predictions about low-dimensional inter-area communication for future multi-area recordings. However, the impact of this modeling study could be more clearly outlined.
-	The abstract promised “testable hypotheses” from multi-area RNNs, but I did not see concrete suggestions for experimental tests of specific hypotheses explicitly laid out beyond what’s been proposed in terms of “minimal representations” in classifiers. What computationally relevant effects does the network predict from different experimental perturbations (whether with microstimulation, inactivation, targeted probes with specific stimuli, etc.)?

limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: In this study, the authors trained a three-layer recurrent network and studied how the multiple networks (brain areas) work together to complete decision making. By constraining network architecture and parameters from biology, the authors demonstrated the last layer of the trained network reproduce the characteristics of neuronal responses in the dorsal premotor cortex, i.e., only encoding the direction of decision choices but filtering out sensory information, and conducted comprehensive analyses to show how decision variables emerge from network connections. 
main_review: The trained network successfully reproduces key characteristics of neurons in the dorsal premotor area, and found the inter-network connections are preferentially aligned to the choice axis (direction), but not the axis of sensory variable or the axes of maximal variance. It is a solid study by using artificial neural network training to help us understand the neural computation in the brain, and the paper did systematic state space analysis of the network. From the discussions it is not very clear whether similar work has been done before to study sensorimotor transformation in the brain.

### Main suggestions
Although I understand that PMd responses cannot be reproduced by a single recurrent network, I hope this study could provide more mechanistic understanding of why the three-layer network is necessary to reproduce the key results, or given a two-layer network what extra mechanism is needed to reproduce the key results. It is a bit pity that the paper only briefly mentioned with less than three areas the last layer cannot reproduce the responses in PMd (line 147 and Fig. 3e). 

It is better to see some discussions that how the 1st and 2nd layer in the network model can be regarded as sensory cortex in the brain.

### Clarify
The paper is structure-wise and clearly written in neuroscience point of view. As a NeurIPS paper I suggest the authors compress the description in Results a little bit and adding more computational justifications and motivations. When I read the papers I realize there are not sufficient technical details in the main text, and I need to look for them in the supplementary information. 

### Minor
I realize there is some wrong cross-reference in the paper which is probably resulted from this is a compressed version from an earlier long manuscript submitted to a neuroscience journal. I wish the author proofread the manuscript again to correct all of these typos. 
- Figure  1 caption mentions Methods section but no Methods in either the paper or Supplementary information.
- Figure 2C: what is the difference between the top and bottom panels?
- Line 112: a wrong cross-reference. The details should be in Appendix 5, not 5.4.
- line 184: there is no figure 4e in this paper.
- line 227: what is the full name of DV? It has never been defined.
- Appendix line 527, how Z_des represents left or right choice?
- Figure 4a: I suggest authors arrange the three plots vertically, and plot the input trajectories separately, i.e., not overlaid with activity.
- line 545 in Appendix: why you stop training to prevent the network from achieving nearly perfect performance? Is it necessary to reproduce 
limitations_and_societal_impact: Yes
review_title: null

Meta-review: This paper builds a multi-areal model of a decision making task. Neuroscience-inspired architecture constraints on the network is shown to be critical for learning solutions that resemble cortical dynamics. All reviewers agreed on the significance and the novelty of the work.
Decision: Accept (Poster)