Temporal Misinformation and Conversion through  Probabilistic Spiking Neurons

Authors: Velibor Bojkovic, Xiaofeng Wu, Bin Gu
Primary area: learning theory
Keywords: spiking neural networks, probabilistic spiking, ann-snn conversion

Abstract:
In the age of large neural network models and their high energy demand, Spiking Neural Networks (SNNs) offer a compelling alternative to Artificial Neural Networks (ANNs) due to their energy efficiency and resemblance to biological brains. However, directly training SNNs with spatio-temporal backpropagation remains challenging due to their discrete signal processing and temporal dynamics. Alternative methods, notably ANN-SNN conversion, have enabled SNNs to achieve performance in various machine learning tasks, comparable to ANNs, but often to the expense of long latency needed to achieve such performance, especially on large scale complex datasets. The present work deals with ANN-SNN setting and identifies a new phenomenon we term ``temporal misinformation'', where random spike rearrangement through time in the converted SNN model improves its performance. To account for this, we propose bio-plausible, two-phase probabilistic (TPP) spiking neurons to be used in ANN-SNN conversion. We showcase the benefits of our proposed methods both theoretically and empirically through extensive experiments on CIFAR-10/100 and a large-scale dataset ImageNet over a variety of architectures, reaching SOTA performance. Code is available on GitHub.

Decision: reject
Reviewer scores: 8, 6, 3, 3

Reviews and discussion:
<thread>
creation date:
1730314522177

thread_type:
official_review

<thread_object>
creator: reviewer_7dmg

<content>
summary:
This paper identified a new phenomenon in SNNs, termed “temporal misinformation”, and proposed a solution. Results are validated on CIFAR-10, and CIFAR-100, and ImageNet

strengths:
The overall problem identification in the introduction section sounds interesting. However, the writing of the paper is very poor and it is very hard to follow the paper to understand the motivation, method, and detailed strengths of the method. Also, I cannot find detailed descriptions of Figure 1, which prevents me from understanding the main motivation of this work. 

Basically, I lost at the very beginning phase when I read this paper, so have not been able to identify the strengths.

I encourage the author to make major changes to the clearness of the paper and writing before any potential resubmission, so readers can understand the paper better.

weaknesses:
1. Line 11. "age of large neural network models", what "age of large neural network models" means? Is this a scientific term that deep learning researchers usually use? When did this age start? Which paper used the term "large neural network models" before?

2. I searched for the "Fig" but did not find any results that refer to Figure 1 explains Figure 1. Could you make it clearer to readers where the explanation in Figure 1 is located in your paper? It is NOT friendly for reviewers as you want to use Figure 1 to attract attention but then when a reviewer tries to enjoy the detailed explanation of this figure, you just hide explanations. 

3. Line 158 "\textbf{ANN-SNN} conversion" and Line 188 "\textbf{Direct training} This method a". Why "conversion "is not capitalized in Line 158, but "This" is capitalized in Line 188? What does "ANN-SNN" mean? Is this a term you created? If not, please cite papers that used "ANN-SNN" before, or explain this new term clearly.

4. Line 25. "Code is available on GitHub.". What do you mean by "Code is available on GitHub."? There is not any Github link in the paper. Please do not mention something that does not exist in the abstract.



--------------------------------------------------------
Updates After Rebuttal

After multiple rounds of discussion with the authors, only Point 2 has been addressed. Point 3 remains unresolved and has not been corrected in the latest paper version (19 Nov 2024). The other two points have not been responded to by the authors.

I have provided my final comments to the authors, requesting a revision of the paper to solve Point 3. I will not respond to any further comments from the authors, as the authors ignored my previous comments and it has been the second time I EXPLICITLY asked the author to correct it in the paper. I strongly suggest author read my comments sincerely and revise the paper, instead of continuing to give pointless discussions. 

Since the authors have addressed only one out of the four issues I raised, I have kept the score unchanged but increased the confidence level from 4 to 5.

------------------------
Second Update After Rebuttal:

This update focuses on the author's unprofessional behavior. I am extremely frustrated with how the author treats the reviewer and disregards the reviewer's comments.

I pointed out that Figure 1 in the paper is never referred to even once in the text. This is the first figure of the paper and should be clearly explained in detail. In the latest version of the paper (submitted on November 19, 2024), the author addressed one issue by correcting "Table 1" to "Figure 1" and responded that Figure 1 is described in the Introduction section and in Section 3.1. However, neither of these sections explains Figure 1 in detail, and Figure 1 is still not referred to in Section 3.1.

Additionally, I noted an issue in Line 158 regarding the term "ANN-SNN" I explained that "ANN-SNN" is not a term commonly used by researchers and that the first word of the sentence should be capitalized. The authors questioned the reviewer's expertise and professionalism, claiming that "ANN-SNN" is a widely used scientific term and suggested I look it up on Google Scholar. This response was unprofessional. I checked 30+ SNN papers on Google Scholar and confirmed that "ANN-SNN" is not used; instead, the technique is commonly referred to as "ANN-SNN Conversion." I informed the authors of this and requested that they revise the term explicitly in the paper and not let the efforts of the reviewer be wasted. Despite this, the authors refused to make the revision, arguing that the correct term is used 32 times elsewhere in the paper and dismissing my comments as "pointless discussions."

Even after providing evidence and explicitly asking for the revision, the authors have not made the changes. As of November 20, 2024, the term "\textbf{ANN-SNN}" has still not been revised to "\textbf{ANN-SNN Conversion}," and the first word of the sentence ("conversion") remains uncapitalized. The authors' repeated dismissal of my comments as "pointless" and their refusal to revise the paper is baffling and disrespectful to the review process. Their behavior is very unprofessional.

If the authors continue to treat the reviewer like this, I will escalate this matter to the Area Chair to address why the authors failed to revise the paper after the issues were clearly identified and why they treated the reviewer in such a dismissive manner.

questions:
See Weakness for detailed questions.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730568796936

thread_type:
official_review

<thread_object>
creator: reviewer_ub8u

<content>
summary:
This paper analyzes the assumption in ANN-SNN conversion that information transfer relies solely on spike firing frequency. By permuting spike sequences, the paper observes the issue of “temporal misinformation” within the temporal domain. Further, it proposes an accumulation -> firing approach to avoid temporal misinformation, establishing an efficient ANN-SNN training method. The method is validated on datasets such as CIFAR and ImageNet, demonstrating advantages in model performance and other aspects.

strengths:
The paper presents a compelling perspective: there exists a phenomenon of “temporal misinformation” in the ANN-SNN conversion method.

weaknesses:
The novelty. The Ideas in this paper are quite similar to those in at least two other papers.

Please See the Question Part.

questions:
1.Prior to this paper, many works have discussed the equivalence between spiking neurons with temporal information and neurons with quantized activation values. This includes papers in the ANN-SNN domain [A], as well as works in the direct training domain [B]. Could you highlight the key innovations in your paper?

2.I noticed that you cited paper [A], pointing out precision issues in its conversion process. In fact, your methods appear quite similar to theirs; could you provide a comparison between your approach and theirs?

3.Could your method be applied to direct training, as in [B]? Would you be able to discuss the similarities, differences, and advantages or disadvantages of your method when used in direct training compared to ANN-SNN conversion?

[A]Hu, Y., Zheng, Q., Jiang, X., & Pan, G. (2023). Fast-SNN: fast spiking neural network by converting quantized ANN. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[B]Luo, Xinhao, et al. "Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection." arXiv preprint arXiv:2407.20708 (2024).
</content>
</thread_object>

</thread>
<thread>
creation date:
1730702911243

thread_type:
official_review

<thread_object>
creator: reviewer_gkwm

<content>
summary:
This paper introduces the concept of "temporal misinformation" in the ANN-to-SNN conversion process. It employs two-phase probabilistic (TPP) spiking neurons as the neurons in the SNN. The converted SNN model improves performance by using probabilistic neurons that fires spikes randomly.

strengths:
1. The method is relatively easy to understand.
2. The problem is relevant to the scope of ICLR.
3. The paper identifies an interesting phenomenon in the ANN-SNN conversion process, which could serve as a complementary insight.

weaknesses:
1. This work primarily proposes a spiking neuron model rather than a novel conversion paradigm, which limits its originality, as similar work on probabilistic neurons has been done previously.
2. The authors’ definition of "temporal misinformation" is unclear. While the paper introduces the "temporal misinformation" phenomenon and experimentally demonstrates its impact on SNN performance, it lacks a sufficient theoretical basis to explain this phenomenon and does not provide quantifiable error analysis.
3. The authors use "permutation" to introduce two-phase probabilistic spiking neurons (TPP). Theorem 1 suggests that the spikes emitted by probabilistic spiking neurons achieve an optimal spike firing order; however, this proof lacks persuasiveness.

questions:
1.  For T time steps, there are T! possible permutations. Does every permutation yield better results than the original, non-permuted approach?
2. Please clarify what the additional c steps in Tables 1 and 2 represent. 
3. In Tables 4 and 5, when comparing with SNNC, data suggests that at short time steps such as T=4 and T=8, "Permute" seems more effective than TPP. I would like to see if a similar phenomenon occurs with QCFS. Please provide additional comparative experiments between "Permute" and TPP on QCFS across CIFAR-10 and CIFAR-100 datasets. 
4.  Does the probabilistic neuron model introduce extra overhead for hardware implementation?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730707365069

thread_type:
official_review

<thread_object>
creator: reviewer_ega6

<content>
summary:
This paper primarily conducts further research on the ANN-SNN conversion methods. In particular, the authors have discovered a new phenomenon termed "temporal error information" and proposed biologically plausible two-phase probabilistic (TPP) spiking neurons for ANN-SNN conversion. The experimental results demonstrate the advantages of this method.

I believe that the method proposed by the authors is effective; however, I have some questions that need clarification, mainly regarding the explanation and interpretation of the phenomenon. At this stage, my rating is "6: Marginally above acceptance threshold." If the authors could address and clarify these questions, I would be very willing to raise my score.

strengths:
1. The method is simple and effective, supported by comprehensive experimental validation. The proposed approach achieves state-of-the-art (SOTA) accuracy on the CIFAR-10/100 and ImageNet datasets, advancing the further development of ANN-SNN conversion.

2. Unlike SNN research that tends to focus on computational efficiency, the two-phase mechanism and probabilistic spike discharge proposed in this work are both biologically plausible and have similar implementations in certain neuromorphic hardware.

weaknesses:
1. The description in lines 94-107 is difficult to understand. Table 1 indeed shows that the "permuted" model performs better, but how does this relate to the previously acknowledged yet erroneous assumption that "the precise timing of the spikes should not affect the performance of the SNN"? There is no explanation of what the "permuted" operation is or how it relates to "the precise timing of the spikes." Additionally, why is the phenomenon named "temporal error information"? Where does the error manifest? Section 3.1 addresses these questions, but lines 94-107 should also provide some insight for readers encountering this for the first time.

2. I find the experimental work convincing; however, the description of the phenomenon of "temporal error information" needs further clarification. The current description leaves me unclear about what constitutes "temporal error information" in the original spike sequence. Which time steps contain erroneous spikes? What portion of the temporal errors is addressed by the "permuted" operation, and how does this improve the accuracy of the conversion?

questions:
1. I am unclear about the details of the "permuted" operation. Figure 2(a) mentions that "the second Spiking phase outputs the same spike trains, but permuted." Since the output spike sequences are the same, where does the permutation manifest? Is it the firing times that have been rearranged?

2. In ANN-SNN conversion, even if the spike firing rates match the ANN activation values, the conversion is not lossless. Some works have discussed this, noting that in early time steps, there can be spikes that should not have been fired, resulting in erroneous spikes [1]. Is this related to the "temporal misinformation" mentioned in the paper? Furthermore, uneven errors also point to this issue [2]. What is the relationship between "temporal misinformation" and uneven errors? Could the authors provide some discussion on this aspect?

[1] X. He, Y. Li, D. Zhao, Q. Kong, and Y. Zeng, “Msat: biologically inspired multistage adaptive threshold for conversion of spiking neural networks,” Neural Computing and Applications, pp. 1–17, 2024.

[2] Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Y u, and Tiejun Huang. Optimal ANN-SNN
conversion for high-accuracy and ultra-low-latency spiking neural networks. In International
Conference on Learning Representations, 2022c.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732009578610

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Common answer to all the reviewers

comment:
Thank you very much for your time in assessing our paper, and especially to reviewers _egA6_ and _gkwM_ for their valuable comments and questions. Here, we assess a common point for some of the reviews and try to clarify the notion of _temporal misinformation_.

**Permutation of a spike train:** By a permutation of a one-dimensional spike train $s$, we mean permutation of the entries of $s$. In other words, we rearrange the spike timings according to the permutation. One can refer to Figure 2 in the paper for the visual explanation.


+ **Temporal information**: If permuting spike trains after spike layers degrades the performance of the SNN, it means that the _positioning_ of the spikes carries **useful** information for the network and its performance. Hence, we would call this occurrence **"Temporal information"** in the spike trains/networks.
+ **Temporal misinformation**: In the opposite situation, when the performance of the network increases when permuting spike trains, it means that the spiking layers produce spike trains with timings which are "misinformative", and we call this occurrence **"Temporal misinformation"**. Our article studies temporal misinformation in ANN-SNN conversion setting. 


**Name "Temporal misinformation":** By a temporal misinformation we mean a phenomenon in rate encoded SNNs when permuting spike trains after spiking layers of the model increases the performance of the model (in the expectation). Permutations are done in orderly fashion: the output spike trains of one layer are permuted and then passed to the next spiking layer for processing, whose output spike trains are then permuted and passed to the following spiking layer and so on. 

**New experiments in the APPENDIX:** As few reviewers commented on the source of temporal misinformation, we provided our point of view on this, and extended experiments to support it. Our claim is that the baseline SNN models suffer from unstable firing rates in the early spike times, due to the their inability to approximate the ANN values with few time steps. 

In the Appendices F, G, and H we provided new experiments concerning permutation effect of SNN baselines:


+ In Appendix F we show how the permutations affect the early time steps in the output layer, explaining why the performance of the baseline drastically increases when permutations are applied. This is Theorem 2 of the paper in visible action.
+ In Appendix G, we plot membrane potential distributions before firing of the baselines and contrast them with the distributions of permuted and TPP models. These plots show how permutations and TPP neurons provide wider distributions, especially so in lower time steps, increasing the networks variability and spiking activity. 
+ In Appendix H, we provide new experimental results concerning the effect of permutations in some particular cases.
    - Figure 10: We plot the accuracies of the model when all of $T!$ ($T=4$) different permutations are applied. In particular, we notice that the permutations anchoring the first time step, show the least effect, confirming our claim above.
    - Figure 11: We plot the accuracies of the model when various classes (28) of them, of permutations of 8 elements ($T=8$) are applied, fixing two time steps. The figure shows one again, that the permutations fixing the first element (in combination with any other) show lower performance increase as compared to the variations.
    - Figure 12 and 13 show the effect of a permutation when it is applied only on a single layer in the baseline model. We notice that the permutation show the least effect when they are applied in the early layers (as the SNN model is still approximating the ANN well), and in the late layers (as it is already too late to fix the accumulated approximation error from the previous layers).  

Finally, in the Appendix I, we provide a new theoretical result concerning TPP setting, which provides further insights into conversion error made when using them in ANN-SNN conversion.

We hope that these new results shed more light on our method, and confirm its potential in ANN-SNN conversion.
</content>
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</thread>


Metareview:
metareview:
This paper presents a new form of spiking neural network (SNN) unit to be used for ANN-to-SNN conversion. Specifically, the authors begin by examining the importance of precise spike times in an SNN following conversion. They observe a surprising phenomenon which they dub “temporal misinformation”, whereby the spike times do matter but in the opposite way to what a naive observer may have predicted. Namely, if the spike times are permuted the performance *increases* rather than decreasing, showing the spike times matter, but the spike times obtained following conversion may not be optimal. Inspired by this, the authors develop a two-stage spiking neuron model that uses a phase of integration followed by a phase of stochastic spike generation. They claim that their SNN model provides better performance following ANN-to-SNN conversion, and they support this claim with data on a series of classic image categorization datasets.

The strengths of this paper are that it uncovers an interesting, surprising phenomenon in ANN-to-SNN conversion models (temporal misinformation) and uses this observation to improve on current techniques. The weaknesses are (1) the reasons for this interesting phenomenon and the success of the new technique are not thoroughly explored leaving the reader feeling unsure what is really happening, and (2) the paper’s presentation is not great, with the writing and methods very hard to follow (e.g. the specific contributions were not originally well described, and the description of the conversion process in Section 3 and Appendix A do not provide critical pieces of information for understanding). 

On the first point, the authors provided some additional experiments in rebuttal that helped, though could be greatly expanded. The second point remained largely unaddressed. Indeed, on the second point several of the reviewers seemed perplexed by the paper, and none of them gave it a good rating on presentation (even the positive reviewers). In the end, the final scores left this paper as a borderline case, and the AC had to make a judgement call. Based on the considerations above, a decision to reject was reached. It is the AC’s assessment that there is something potentially very interesting in this paper, but the authors should revise the paper significantly and submit elsewhere, so that they have the time to properly focus on explaining and understanding “temporal misinformation” and providing more grounded explanations for their technique. As well, the AC encourages the authors to generally review the clarity of the paper before resubmitting elsewhere.