Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors propose a bio-plausible realization of the Adam algorithm in biological neural systems. They similarly propose a method to establish weight symmetry in biological neural networks based on the concept of predisposition.
strength_and_weaknesses: Strengths
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The idea of biological neural systems implementing an adaptive gradient optimization procedure is an interesting and novel contribution to our knowledge of learning in neural circuits.

Weaknesses
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1. There are spelling errors:
    (A) “Variable” on first line on Page 2 
    (B) “Mechanism” on  second line of last paragraph in Introduction (Page 2)
2. The RMSProp bio-plausible realization is somewhat arbitrary and underdeveloped. The paper doesn’t provide the corresponding biological substance that is co-consumed by potentiation and depression. This justification was provided for momentum.
3. The empirical experiments are inconclusive. Evaluating Bio-Adam on a single architecture (ResNet18) is not a sufficient justification for how the method compares to its contemporaries. This makes me question the robustness of the hyperparameters and the Bio-Adam model.


clarity,_quality,_novelty_and_reproducibility: The paper is clearly written and good quality work.
summary_of_the_review: The bio-plausible realization of ADAM is an interesting contribution to the learning dynamics of neural circuits. My main criticism of this work is the empirical experiments conducted in this paper. The experiments are not comprehensive, more work can be done to empirically evaluate Bio-Adam.

I am willing to revise my score if more empirical experiments are conducted.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This work presents a biologically motivated implementation of ADAM and weight transport solution. 

strength_and_weaknesses: Strengths
- Understanding how the brain achieves credit assignment is an important and very interesting research direction. This paper contributes to this field and provides a hypothesis for experimental neuroscience to explore. 
- Bio-adam is an interesting and novel algorithm. 
- The paper is well written and clear. 

Weaknesses
- The main contribution of this work is to present a biologically motivated implementation of adam (there are no computational advantages proposed). However, in my view the biological connections are undeveloped, and only loosely motivated by neurobiology. As a result I find this work’s contributions limited.   
- I do not find gradient transport and the weight symmetry learning rule particularly biologically plausible, and as such do not find this aspect of the work that convincing. First, there is little to no evidence for 1 to 1 forward backward connections in the brain being a general principle. Second, backward synapses would need to be able to communicate signed errors, which they cannot, or alternatively there should be two sets of backward synapses for each sign. Third, in my understanding axonal signaling is not equipped to signal with the time resolution that would be required. 
- There is no advantage of bio-adam shown over a more simple update such as sgd+momentum, which would most likely be simpler for biological systems to approximate. 

clarity,_quality,_novelty_and_reproducibility: The paper is clear and of high quality.
summary_of_the_review: Although interesting and novel, there are no computational advantages presented and the biological connections are approximate. Therefore I find this work’s contributions limited.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors propose an interpretation of the first-order gradient model based on the dynamic evolution of synapse. Overall, it is interesting but the explanation itself seems to be not based on the ground truth of neuroscience thus appears to be a bit post-hoc given the establishment of ADAM and RMSProp. 
strength_and_weaknesses: Strength: 
The idea of explaining the gradient update from neuroscience view is interesting. 

Weakness:
1. In terms of equation (6) and (7), it seems to be a very deliberate operation to split the concentration and gradient into both positive and negative parts here. What do you refer to by the $m^+$ and $m^-$ here? The high flux or low concentrations of $Ca^{2+}$? Why can you assume that it is depending on the "gradient" with a linear relation? For me, there lacks some evidence here to support the reasoning procedure beyond assumption. For example, ref [1] gives the dynamics equation for LTP/LTD. 

2. The model for RMSProp has the same issue as above. Rewrite the existing updating rule into a flow is not convincing as you can associate the parameter values with high arbitrariness. I expect that there should be some intermediate process that explains why it takes the specific parameters. 


Ref:
[1] https://www.pnas.org/doi/epdf/10.1073/pnas.132651299
clarity,_quality,_novelty_and_reproducibility: The paper is well written and easy to follow. However, its current originality is limited based on my current view.
summary_of_the_review: Overall, I think this is an interesting paper with an promising view to explain the gradient descent model with biological mechanism. However, there lacks some rigorous and detailed reasoning on how they are actually connected to each other.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: ### Summary
Gradient based adaptive learning rule algorithms such as Adam and RMSProp have been instrumental at achieving state of art deep learning results. This paper aims to develop a biologically-plausible variant of Adam. The proposed learning rule can run in continuous time and works with spiking neural networks. In this paper, they show comparable results to Adam with a more biologically plausible algorithm.

Below I will summarize some of the strengths and the weaknesses of the paper as pointed out by the reviewers:

### Strengths

- Understanding how the brain achieves credit assignment is an important research direction. 
- The proposed biologically plausible Adam is a novel algorithm.
- The paper is well-written and easy-to-follow.

### Weaknesses

- The experiments are not comprehensive enough. More work needs to be done to empirically evaluate Bio-Adam.
- The biological connections are not developed well enough. The method is only loosely motivated by neurobiology. 
- The proposed model i like a posterior interpretation rather than a mechanism-driven brain-inspired algorithm since we know that Adam works well for DL. It feels like an adaptation to Neuroscience.
- The model bio-Adam is based from is not a widely accepted paradigm in neuroscience. 

### Decision

The subject this paper is studying is an interesting one and the proposed algorithm is novel. However, only one of the reviewer of this paper was leaning towards acceptance and with a weak confidence level. The weaknesses pointed out by the reviewers in terms of experimentations and motivations indicate that this paper is not ready for publication yet.

justification_for_why_not_higher_score: This paper unfortunately does not provide sufficient experimental evidence to prove that the proposed bio-Adam algorithm works well. The proposed algorithm seems a bit posthoc as well. Only one reviewer was leaning towards acceptance and they had a weak confidence score.
Decision: Reject