Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper builds on past work that uses synthetic gradients (estimated by small networks) to decouple parts of a network so that they can run in parallel. The current paper proposes that the cerebellum may produce such synthetic gradients to deal with delayed feedback to recurrent networks. To illustrate how this could work, the authors model a cortico-cerebellar network as a LSTM (cortex) and feedforward gradient-estimating network (cerebellum). They show that the system can learn various tasks, including line and digit drawing and caption generation. The further show that disrupting the cerebellar input leads to something resembling ataxic behaviour due to cerebellar damage. 
main_review: Originality: 
As far as I know, this is a new idea. It combines the well-established neuroscience concept of the cerebellum as a shallow feedforward supervised network and a recent approach in deep learning. 

Quality: 
The work seems to be technically sound and suitably thorough. 

I have a reservation related to the analogy between the synthetic gradient network and the cerebellum. Reversible cerebellar lesions not only affect learning, but induce ataxia in previously learned movements (e.g. Hore, J., & Flament, D. (1988). Changes in motor cortex neural discharge associated with the development of cerebellar limb ataxia. Journal of neurophysiology, 60(4), 1285-1302). This seems to conflict with Figure 3c, which shows that the model isn’t sensitive to cerebellar ablation after a certain point in learning. 

Clarity: 
The writing is very clear and the paper is well organized. Some methodological details could be moved to the main text, e.g. the network architecture and the number of time steps of the line-drawing movements. I don’t think the plant that carries out the movements was ever described. Is it a 2D point mass with force inputs? 

Significance: 
As the authors point out, if this idea turns out to be physiologically realistic, it will provide an important new perspective on the cerebellum. It could also point to the physiological details of the cerebellum as a source of inspiration for future machine learning work on decoupled neural interfaces. The paper gives some preliminary evidence of physiological relevance of the model, but much more would be needed to make a convincing case (more than would be expected in a single paper). So I think the significance is unclear at this point, but it is promising. 

Minor: 
I believe the cerebellum provides input mainly to frontal, cingulate, and a few parietal areas (Strick, P. L., Dum, R. P., & Fiez, J. A. (2009). Cerebellum and nonmotor function. Annual review of neuroscience, 32, 413-434.) Could the authors comment on whether this restriction relates to the proposed role of cerebellum? 

In Figure 2d, my understanding is that the “cortical temporal feedback %” applies to both cRNN and ccRNN. So cRNN works better if there is a long enough feedback window, otherwise synthetic gradients are helpful. If this is right, I’d suggest expanding the caption slightly as I missed it the first time. 

Line 126: “our model suggests that the cerebellum should encode error signals, consistent with neural recordings showing cerebellar-encoding of kinematic errors during motor learning” I think the model suggests that the cerebellum should encode error gradients, which may only be consistent given additional assumptions. 

Line 226: “pairwise correlations between all the neural activity in the main cortical network and the neural activity of the cerebellar module (see SM).” I had to check the SM to understand this, but a slight rephrasing would make it clear in the main text. 

limitations_and_societal_impact: The work has a minor societal impact. In the checklist, the authors say they have discussed limitations in sections 2.1.2 and 4, but I don’t see such a discussion.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: In this work, the authors propose a neural architecture inspired by the anatomy of the cortico-cerebellar pathways in the brain.  The method relies on a previously proposed framework of decoupling neural interfaces. Here, the authors suggest that the cerebellar circuit calculates the future error of the cortical representation. It allows the implementation of Back Propagation Through Time without the phase-lock problem—where the network has to be wither in a feedforward of a feedback phase to calculate the local weight gradients. 
main_review: **Strengths**
- Considering the cerebellum as a circuit that calculates the expected error is a promising avenue that aligns with many current ideas about the cerebellum function.
- The decoupling neural interfaces framework is a concrete model that could be used in the future to study cerebellar function.
- The authors show the benefits of the architecture on various tasks that would (potentially) be associated with the cerebellum.

**Concerns and weaknesses**
- The authors show that the methods work well (or at all) only when there are intermediate inputs of external feedback. It seems to me that the role of the cerebellar layer here is to make sure the output is interpolated smoothly between the supervised feedback. It would also explain the higher error in the non-smooth outputs.
- I found the details on the temporal feedback in RNNs (lines 96-108) hard to follow. In particular, the loss function for the temporal feedback. In here, delta-hat is the feedback from the cerebellar layer; why does the cerebellar activity also appear explicitly within the expression for delta-bar? This section should be revised and better explain the loss function being used; it is fundamental to this work and should not be left for a reference to make sense.
- The feedforward-backward phases of training are only one problem of several problems of implementing  BP and BPTT in a biological realistic network. While the paper does not mention other substantial issues, I don't
limitations_and_societal_impact: yes
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The authors propose to interpret the function of the cerebellum through the recently developed theory of decoupled neural interfaces, referred to as DNI, allowing for online training of a deep neural network and not requiring locking. After introducing the DNI, the paper extensively explains the possible mapping between the model and observe cortico-cerebellar networks, leading to a clear explanation of how they conceive feedback to be implemented in the brain. Finally, the paper proposes to compare the results obtained with their architecture on a variety of tasks validating their model. 
main_review: The paper is well-written and proposes a rather uncommon way of addressing feedback in the ML/DL literature. It is highly appreciated that the paper clearly delineates structural properties, i.e., mapping onto known neural structures and the model of computation and learning.  

However, an important weakness of the paper is that it does not try to understand the nature of the connections and neurotransmitters that would allow for such a model to be viable. The mapping only vaguely covers these aspects and weakens the paper. It would have been appreciated that either the paper itself a "biologically plausible" model and structure rather than forcefully mapping it onto the cerebellum and cortical networks. That does certainly not invalidates the point of the paper, but even with mitigating those arguments, the paper would remain strong while avoiding some oversimplification. 

One could have also appreciated comparison to other models than itself in the experiments. It is not possible to know how well or poorly it performs compared to existing non-biologically plausible alternatives either SOTA or simply relevant to the paper. 
limitations_and_societal_impact: Limitations and possible research directions are nicely described.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: During a network update step all layers need to wait for the feedforward pass (or its temporal RNN analogue) to reach "the end" and only then the error is calculated, gradient computed, weights updates. So effectively most of the time the lower layers are just waiting for the feedback to trickle down. This is called "feedback locking" and is inefficient. The paper proposes to add a second network to the architecture that learn to predict intermediate layer gradient from their activations, allowing an immediate update. The overall architecture bears hypothesized similarity to the role of cerebellum in primate brain.

I find the idea interesting and the execution faithful and detailed. My main concern is whether the chosen control experiment is doing a good job at eliminating confounding factors potentially responsible for the better performance of the proposed method (see below for detailed explanations and a proposal).

I am reserving my final assessment till after authors' rebuttal.

---- UPDATE ----
Authors' rebuttal has fully addressed my concerns and resolved them in a positive manner. I am now much more confident in the value of the contribution. Raising the score to "8: Top 50% of accepted NeurIPS papers, clear accept"
main_review: General: What is the supporting evidence for the brain not being afflicted by the "feedback locking" problem. Do we know for certain that there is such a mechanism in place in the brain? Or could it be the case that the brain offers no solution for the "feedback locking" problem and this locking is actually part of normal operation?

88-89: Related to the question above. It would be ideal to see a reference to a in-vivo study where the up-stream portion of a certain brain "circuit" is being disabled so that we are certain the the signal is not reaching the higher level where the "loss" is computed (so that the credit assignment cannot happen). And then we feed some input to the lower portion of the "circuit" and confirm that is still learns.

General: If the cerebellum is believed to act as a producer of such a "synthetic gradient", doesn't this require the cerebellum to have direct connections to the part of the cortex where the "synthetic gradient" is (presumably) being used, say the motor cortex? But there is quite some distance, anatomically, between the motor cortex and cerebellum. Do such connections exist and if not, how it can still be responsible for carrying out the ascribed function?

General: There seems to be an issue of infinite regress - which brain structure will play the role of cerebellum for the cerebellum itself? Same applies to ccRNN - how do we avoid "feedback locking" when training the C network? I would imagine both the C and the Main network are being trained simultaneously so we cannot pre-train C in advance, can we?

201: "network needs to produce a straight line towards" - is the output of the network at each time t an (x, y) tuple of coordinates for the next target on the line? Perhaps this could be explicitly stated here for clarity.

209: Can we be certain that the positive effect of ccRNN here comes from the ability to avoid "feedback locking" and not because the C network is acting as a learning stabilizer / variance reducer, which then leads to more efficient learning? Here is a control experiment to consider: what if cRNN would also have a "cerebellum", but this time it would provide the learning signal only at the last time step? So instead of giving it directly to the RNN, we would pass it though the cerebellum network (but only at the last stage, preserving the "feedback locking" problem). If in this setup the resulting trajectories would still be more ataxious than ccRNN, then we would know that it is the precisely the ability to provide gradients and the intermediate steps is what is behind the improved performance, and not such factors as (a) the potential stabilizing effects of the C network, (b) the added overall system capacity (more weights), etc. What you imagine the outcome of such a control experiment would be? Could you run it in time for the rebuttal?

226: "pairwise" refer here to the pairs of what? Nodes? So the correlation between the activity of each node in the "cortical" network with each node of the "cerebellum"? At each time step?

252: Figure 4c: Error bars seem to be very small (compared to the ones on Figure 2c for examples). Given here the experiments was repeated only 3 times (compared to 10 before), one would expect longer error bars. Or was the results very consistent in this case? Why only 3 runs by the way, and not 10 as before?
limitations_and_societal_impact: The authors do provide some disclaimer statements regarding the limitations of their model and its predictions. It would be curious however to also learn authors' opinion about the limitations in (a) model's actual correspondence with the biological counterparts, (b) "failure" modes where the this model would not be applicable and might even be detrimental (if such cases can be identified). Is it always a good idea to approximate the gradient and provide this information mid-update or could it lead to unintended consequences in both biological and artificial netoworks?
review_title: null

Meta-review: This paper proposes that the previously published synthetic gradient approach, decoupled neural interfaces (Jaderberg et al. 2017), may serve as a model of the cortico-cerebellar learning. The reviewers found the proposed connection between DNI and cortico-cerebellar learning to be a promising lead for future biological investigation.  Since understanding cerebellar function and role in human cognition is a deep and important problem, the initial proposal of this possible connection to a recent AI approach is considered a valuable contribution.  While this connection is not validated in this work, the connection is considered in light of what is already generally known about the cerebellum, and the work explores the consequences of this comparison.

The results of the paper involve comparisons between a baseline RNN model which uses truncated BPTT and a ccRNN model that learns using the DNI mechanism.  While the DNI mechanism is not new, the tasks chosen are ones that are supposed to provide further illustrate the implications of the connection to cortico-cerebellar learning.

Personally, I found the submission to be creative in identifying the potential relationship between the existing ML model and the architecture of the brain.  I would emphasize that the ML portion is not new, so the contribution of this paper is specifically the proposal of how the DNI approach might serve as an analogy to cortico-cerebellar learning.  I initially found some details, especially around the presentation of the results, a bit unclear.  However, the authors and reviewers engaged during the discussion phase in a way that I believe will make the final version of the paper sufficiently clear.  Multiple reviewers raised their scores during the exchange.  I'm willing to endorse the reviewer consensus that this paper be accepted.

Decision: Accept (Poster)