Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The authors study spiking neural networks (SNNs) with a biologically-motivated dynamic spiking threshold.  The underlying hypothesis is that having a dynamic threshold allows the network to fire similarly with a wide array of stimuli / external conditions, allowing the network to potentially generalize across such conditions better.  They show that the networks increase robots' performance under degraded conditions, and increase homeostasis per metrics they provide, such as the mean and standard deviation of the firing rates across trials.
strengths_and_weaknesses: **Originality:**

To my knowledge, dynamic thresholds have not been studied in the context of ANNs.

**Quality:**

The authors have provided biologically motivated models and intuitions for why these models might be beneficial in ANNs.  They have then provided highly applied use cases to test the performance of their models.  However, while the performance of the networks is illustrated, no statistical tests are undertaken.  The reader is thus left to wonder as to whether which, if any, of the performance improvements are statistically significant.  Moreover, while the authors have shown both an increase in homeostasis with their metrics and an increase in performance in 2 robotic tests, the reader is left with only this correspondence.  Presumably there are ways in which generalization can occur without improved homeostasis, and ways to increase homeostasis too far, resulting in decreased learning performance?  Yet these possibilities are not raised in the present work.

**Clarity:**

Overall, the motivation, intuitions, experimental setups, and analyses are all clear. However, several aspects remain unclear to me.  Please see questions, below.  Also, it is only made clear in the appendix that the robotic experimental setup is indeed a virtual one, from what I have been able to see.  The reader's understanding would be facilitated if this information were included in the main text and in Fig 2's caption.  


**Significance:**

Biological neurons are known, as pointed out by the authors, to have dynamic thresholds, though the function of such thresholds is, as yet, uncertain. This study represents a welcome introduction of these known dynamics into ANNs, where such functionality can be queried in the context of feedforward networks, and the authors provide an existence proof that in certain contexts, such dynamics might contribute to increased generalization performance.  However, without stronger statistical analysis and, especially, a substantively deeper exploration of how homeostasis and generalization are linked beyond example cases, it is unclear how robust the authors' findings are, and whether the hypothesized link from increased homeostasis --> increased generalization performance is causal, and under what circumstances if so.
questions: 1- What is the motivation for choosing the 3 statistics outlined for homeostatsis? While the mean and standard deviation of the firing rates are 2 natural statistics to measure, why they (and the 3rd) should be the ones to choose for measuring homeostasis is unclear. Are there drawbacks in deciding on these particular ones (e.g., perhaps in choosing these, the networks perform worse wrt other candidate metrics?) ?

2 - Do the shaded regions in Figs 2e and 3d represent SDs, SEMs, or something different?

2- L234: "soft-reset" is unclear
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: In this work, the authors aim at bridging this gap by introducing a novel bioinspired dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks (SNNs). Meanwhile, the authors propose a BDETT scheme mirrors two bioplausible observations: a dynamic threshold has 1) a positive correlation with the average membrane potential and 2) a negative correlation with the preceding rate of depolarization. 
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The author's revisions address all my concerns and I would further recommend this manuscript.
strengths_and_weaknesses: Strenghts:
1. The authors designed a bio-inspired dynamic threshold mechanism to enhance the generalization of SNNs.
2. The new dynamic energy-temporal threshold mechanism reflects the two biological observations.
3. The parameter setting of the dynamic threshold mechanism can use layerwise control of statistical information.

Weaknesses:
1. The interaction of DET and DTT does not seem to be supported by ablation experiments.
questions: 1. Since the authors claim in the methods section that the two dynamic threshold mechanisms can help each other achieve optimal settings. Thus, more sufficient evidence needs to be provided to support this viewpoint.
limitations: The authors emphasize in the introduction section that it still requires hardware engineering efforts.
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 new bioinspired dynamic energy-temporal threshold (BDETT) scheme for spiking neural networks (SNNs), and 
validates the strong homeostasis along with its generalization to diverse degraded conditions. Experiments are conducted on the robot obstacle avoidance and continuous control tasks. The result shows that BDETT outperforms existing static and heuristic threshold approaches.  The supplemental video is impressive. 

strengths_and_weaknesses: Strenghts:
1. Introduces a bioinspired dynamic threshold scheme for SNNs that increases their generalizability.
2, Designs a new method that uses layerwise statistical cues of SNNs to set the parameters of our bioinspired threshold method.
3. The proposed threshold scheme achieves bioplausible homeostasis, dramatically enhancing the generalizability across tasks, including obstacle avoidance and robotic control, and in normal and degraded conditions. 


Weakness:
1. The paper combines two dynamic thresholds that exhibit positive and negative correlations with the average membrane potential. It would be better to explain the reason and the related mathematical formulation in detail.  Plus, the author compares BDETT with four variants of the spiking actor-network (SAN), are there any other recent SNNs to compare with?
2. The experiments are restricted to the robot and control tasks. It would be better to include more tasks such as tasks in computer vision. 3. The supplementary material has a heavy overlap with the main body part.
questions: See the weakness part. 
limitations: None.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The authors propose a dynamic spiking threshold function that consists of DET and DTT. DET is determined by the distributions of the thresholds and membrane potentials over the neurons in a given layer at a given timestep. DET is reconfigured s.t. the larger the average potential over the in-layer neurons, the larger the threshold, so that it avoids too high firing rate over the neurons as well as it induces competition among the neurons. DTT effectively realizes a refractory period s.t. it raises the spiking threshold when the potential decreases. The effect of the proposed spiking threshold (BDETT) applied to SNN-based policy functions for reinforcement learning on two tasks, i.e., robot obstacle avoidance and robotic continuous control. The results highlight a better performance for the use of BDETT in terms of success rate and homeostasis than the baseline methods.
strengths_and_weaknesses: $\textbf{Strengths}$:

S1. BDETT highlights a better performance than the baseline methods at least for a given set of parameters. 
S2. As the authors highlight, BDETT has bio-plausible grounds and is novel.

$\textbf{Weaknesses}$:

W1. Weakness of baseline. the baseline performance is questionable given that the performance of BDETT is not compared with the performance of any previously published works. I am convinced that BDETT outperforms the previous methods that the authors addressed for a given set of parameters. However, I am not convinced if the proposed baseline is the ground-truth or close to the ground-truth.

W2. Wrong citations. I found that Refs. 24 and 26 are irrelevant to the baseline methods DT1 and DT2. 

W3. Lack of in-depth analysis of BDETT. I am slightly tired of the authors’ emphasis on the bio-plausibility of BDETT. I agree on the point that bio-plausibility of a newly proposed method is good. But, such bio-plausibility does not justify the proposed method. The authors took a biological notion and recreated the notion largely, so that I do not think the fidelity of BDETT to biological notions is very high. Instead, I would like to suggest the authors to systematically address the advantages of BDETT over the previous methods from an engineering viewpoint rather that bio-plausibility.

W4. Additional computation and space complexity. The evaluation and memorization of BDETT for all neurons cause additional computation and space complexity, which should have been addressed in detail.

questions: Q1. Applicability of BDETT to other domains. It is required to apply the proposed method to different application domains, e.g., computer vision. The goal is two-fold: (i) the impact of BDETT on SNNs can be highlighted, and (ii) true baseline performances are available, particularly, for vision domain, there exist tons of previous publications reporting their official performances. Can the authors evaluate the impact of BDETT on other application domains?

Q2. Complexity analysis. Can the authors evaluate the computation and space complexities of the proposed method and compare with previous works? 

limitations: The authors did not address the limitations of the present work. But, I believe that the current result limits the impact of the present work to merely two simple tasks with unreliable baseline methods.
review_title: null

Meta-review: metareview: The paper proposes a biologically plausible dynamic thresholding mechanism. Spiking neural nets with dynamic thresholding appears to be novel. The paper does a good job of motivating the choice of the model and illustrating its benefits across a series of control tasks. All reviewers support the acceptance of the paper conditional on the following points to be included in the revised manuscript: 

- The new experiments on image processing performed during the discussion phase have to be included in the revised version.
- I agree with Reviewer r3Sy that the paper overly emphasizes the biological plausibility of the method as a point of strength. Please try to focus the paper more on the technical benefits and analysis of the proposed method, following the instructions of the reviewer.
- Include the complexity analysis of the model in the revised version. 
- Please import some of the tables provided during the rebuttal to the revised manuscript.
- Explicitly denote the details of the statistics of your experimental results. It might be helpful to import some of the tables from the supplementary materials to the main text. 

I recommend the acceptance of this paper. 
confidence: Less certain
Decision: Accept