Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: Backpropagation, while allowing ANNs to learn the statistical regularities of their inputs, is not a biologically plausible method for how biological networks learn.  Different methods have arisen recently to bridge the gap between local Hebbian rules that are biologically realistic but limited in performance, and non-local rules like backprop that are not biologically realizable but perform closer to brain-like levels (or better).  The authors add to this field, providing a learning method that utilizes different spiking patterns ("events" and "bursts") and electrotonically separated compartments (somas, basal and apical dendrites) that, in one phase, is able to approximate backprop and learn complex visual labeling tasks in a more biologically plausible manner.
strengths_and_weaknesses: **Originality:**
As far as I know, no previous, biologically plausible learning rule has allowed for an approximation to backprop that has only one phase of learning.

**Quality:**
The authors pose their questions clearly and provide believable initial answers to these questions

**Clarity:**
The question, methods, and results of the study are all quite clear.

**Significance:**
By allowing for dynamic learning (see the sinusoid example) and biologically realistic modleing assumptions, the present work  of some of the most biologically realistic ANNs  The fact that they have been demonstrated to be able to learn in multiple layers in a single-phase makes a compelling case that the authors' setup may provide insight into a thorny biological problem

questions:  
limitations: Yes
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper proposes a novel framework for biologically-plausible hierarchical learning in deep feedforward neural networks. Notably, they present an algorithm for credit assignment, that can approximate backprop, by leveraging certain known properties of cortical networks. The proposed algorithm leverages the dendritic segregation of neurons to have a two-compartment neuron model coupled with burst-dependent synaptic plasticity to perform credit assignment in a single phase. The authors do a commendable job in presenting the key components of their model, both for updating the feedforward weights as well as updating the dendritic feedback projections from higher layer neurons. Furthermore, they demonstrate that under specific assumptions, the proposed credit assignment rule can approximate backprop-based weight update. Finally, the algorithm was able to learn the XOR task and a non-linear regression task as well as achieved competitive performance on image classification task on MNIST and CIFAR-10 datasets. Overall, this paper makes a significant contribution to biologically-plausible deep learning.
strengths_and_weaknesses: Strengths:
1. The paper is generally well-written and addresses a very important topic in bio-plausible deep learning.
2. The authors present convincing evidence from neurophysiology to demonstrate the biological plausibility of their model architecture. 
3. Along with the performance on image classification, the authors demonstrate how their algorithm solves toy tasks, specifically the XOR problem where the single-phase analog of burstprop fails. 
4. The alignment to the backpropagation updates has interesting trends, especially when the corresponding performance is taken into account. However, this probably needs more investigation and would come under the scope of future work to try and understand how the alignment could be improved and if doing so would yield better performance.
5. The proposed model demonstrates impressive performance on image classification tasks (although it is unclear how this would scale to residual networks on big datasets) and incorporates a great deal of biological realism to make experimental predictions, thus contributing to the understanding of credit assignment in the brain.

Weakness:
1. I felt Section 3.3.1 could be better presented. It's a bit tedious to read right now and slightly unclear without looking into the derivations in the Appendix. It might be helpful if the authors could summarize the derivation as well as clarify the steps in the derivation in appendix to make this section more readable. 
2. It is slightly unclear why the algorithms achieve similar performance although there is a difference in the alignment angle for MNIST. Is the model able to achieve good performance despite the misalignment with the gradient due to dataset size? Or is it because of the size of the parameter space? This issue is slightly more pronounced for CIFAR-10 where burstprop achieves better alignment but worse performance. I am wondering if this has to do with the hyperparameters, specifically burstprop using a higher lr compared to BurstCCNs? 
3. Although the authors motivated the Q and Y matrices as feedback projections from higher layer pyramidal neurons and dendrite-targeting interneurons (inhibitory populations?), it is not necessarily imposed that weights in Q should be positive and Y should be negative. This is an issue that the authors already note in their Discusions section. I have another query related to this point which I have mentioned in the Questions section of the review (see Question #4 below).
questions: 1. Your derivation showing the equivalence of the learning rule to backprop assumes the sigmoid scaling factor to be 4 (Line 5 of equations on top of Page 3 in Supplementary Section B). Is there any specific reason to do so? Could you please elaborate in the text. If not, could you kindly add that this is a specific assumption. Also, it would be nice to comment whether the scaling factor adds a bias to the learning rule when it's not 4. 
2. What are the shaded lines in Fig 4. A & E? I initially thought they were stdev about mean or something. But if that's the case, why are they not centered about the mean line?
3. Could the Q weights being plastic, as opposed to Y, be motivated by the fact that inhibitory weights exhibit lower plasticity than excitatory weights? I understand that the plasticity can be instead imposed on Y but just wondering if there is a specific reason to induce plasticity in only one of them as opposed to both?
4. Doesn't having a full-rank Y matrix require the same number of interneurons as the number of pyramidal neurons in the layer? Is that biologically realistic?

limitations: The authors don't specifically mention any potential negative societal impact of their work but I don't think there are any straightforward implications either.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors proposed a bursting cortico-cortical network which can perform backprop-like credit assignment in deep cortical networks. By combining bursting, short-term plasticity and dendrite-targeting inhibition, they showed that the model can effectively learn with a single learning phase, as well as in a continuous-time way.  They carried out both empirical and theoretical analyses to show that the weight learning approximates backprop-derived gradients. They also demonstrated the mode performance on learning complex image classification tasks on two benchmarks. 
strengths_and_weaknesses: The paper is written clearly with a good logical flow. The question the authors tackled is instereting and may provide a solution for single-phase efficient deep learning in the brain (if the brain does learn in a back-prop way, see Q4 below). Both empirical and theoretical analyses are carried out to support the model. The model is a bit complex in its design but it seems novel.
questions: Q1: why the feedforward weights are determiend in a short-term depression (STD) way while the feedback weights are determined in a joint way of STF and STD? Do these assumption have experimental support? STP are quite different across different brain regions and between different types of cell types (PC, PV SST, VIP, etc.). It seems there was a big assumption here.

Q2: In the dynamic input-output task, is the sinusoidal input fed into the network in a stream way?

Q3: When extending the model to a deep network (> 10 layers), how is the approximation of the weight update in the shallow layers with the proposed method? Does the approximation gradually become worse as the error signal propagates to the shallow layers?

Q4: Does the brain learn in a back-prop way, is there any direct evidence? If not, since deep neural networks with back-prop have already shown great performance in many ML tasks, why is the 'biological plausible deep learning' a problem? 
limitations: Yes, the authors addressed the limitations of their work
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper introduces a biological learning algorithm in multi-layer neural networks that uses information in neurons' spike trains (specifically, the distinction between overall firing rate and frequency of burst firing) to multiplex forward-propagated signals and backward-propagated error signals.  This approach relies heavily on short-term plasticity in feedforward and feedback signals to transmit firing rate and bursting information independently, and is shown to approximately implement backpropagation under certain conditions.  Unlike prior work, the model is capable of learning in a single phase, rather than separate inference and supervision phases, and can also handle learning from time-varying input.


strengths_and_weaknesses: This paper addresses an important question -- how biological neural networks can usefully multiplex inference and learning signals without unrealistic switching between network phases.  As far as I know this work is the first to resolve this question in a reasonably biologically realistic way.  The presentation and derivations are clear, the mechanisms seem biologically plausible and have some grounding in evidence from neuroscience, and the experimental results are compelling.

One main potential weakness of this paper is that it is based heavily on a prior model, BurstProp.  Many of the key ideas of the model (multiplexing firing rate + burst probability information, short-term faciliation and depression to transmit signals independently) were present already in the BurstProp model.  The contributions of this paper enable single-phase learning, which I do believe is an important advance, but the paper would benefit from clarifying and emphasizing which features of the model are novel (and needed to enable single-phase learning) and which are derived from prior work.

Another concern I have is that the experiments are limited in scope and scale.  A variety of biologically plausible learning algorithms have been proposed in the literature that perform well on datasets like MNIST and CIFAR-10 but fail to scale to e.g. ImageNet, or to other non-image-classification tasks.

Finally, I find it a bit unclear what the precise relationship is between the spiking and rate-based implementations.  Are they exactly equivalent in some limit?  I am aware that training spiking networks is generally more difficult than training rate networks, so I don't have a problem with the use of the rate model for the more complicated tasks, but it would help to clarify the link between the two models.
questions: My main suggestion for the authors would be to more clearly delineate the contributions of their work relative to BurstProp (this is done somewhat in the Appendix, but I think it warrants more treatment in the main text).

Also, as mentioned above, it would be helpful to clarify the relationship between the spiking and rate-based model.  I would also suggest further empirical evaluation of the model on another (larger) dataset.

I don't hold it against the paper for not including this, but it seems like it could be fairly straightforward to implement a version of the model with mechanisms to enforce weight symmetry (as mentioned in the conclusion section) that could improve performance.
limitations: The authors are clear and upfront about the limitations of the work.
review_title: null

Meta-review: metareview: This paper describes a biologically plausible model of credit assignment in neocortical microcircuits, building on previous work in this area that uses apical dendrites and burst multiplexing. This model expands on these ideas and incorporates additional biological information related to short-term plasticity and cell-types to develop a model that can learn in a single phase, i.e. with no signal required to gate plasticity.

The reviewers were very positive about this paper and agreed that it makes a novel, insightful, and important contribution to the biological credit assignment field, and. As such, an accept decision was unanimously reached.
confidence: Certain
Decision: Accept