Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The manuscript tackles the problem of supervised learning in spiking neural nets (SNNs). The authors claim that SNNs loose information in feed-forward pass because of the binary nature of their responses and propose an information-maximizing loss function to reduce such loss. 
For gradient back-propagation they propose to extend the popular surrogate gradient approach with a surrogate gradient who's spike activity function changes during the training. In multiple experiments the authors demonstrate advantages of their approach. 
strengths_and_weaknesses: Strengths. The authors propose two interesting extensions of existing surrogate backprop learning method for SNNs. The first one, infomax-inspired loss, maximizes the entropy of individual neuronal outputs under the assumption of independent neuronal responses. This is a valuable insight that SNN community may find of interest. The second one, using a schedule to adjust the activation function is quite innovative. The experiments with two SNN architectures and ablations provide compelling evidence that the proposed approach has advantages over previous work.
Weaknesses. While the aforementioned ideas are somewhat innovative, the implementation is quite limited. For example, the infomax assumes that the network outputs are independent, thus optimizing only spiking rate. Moreover, the "Evolutionary Surrogate Gradients" is essentially a manually set schedule for adjusting a (hyper)parameter, gradient width, with training time. THe form of ESG change with time is hand-picked and no experiments are offered to shed light as to why this form is preferred and how it compares with alternative schedules.
I also found the term "evolutionary" highly confusing as the approach has nothing to do with evolutionary optimization, and only referes to the aforementioned manually selected dependence of gradient width on time. A much better term would be "adjustable, train time-dependent" or alike.


questions: Section 4.2 is confusing. If the spikes can not reach the output as shown in figure 2, how the SNNs can be trained at all?

The dependence of results on the integration time of outputs is stated only for selected numbers (lines 283-291). It would be helpful to instead plot them as in figure  4. Without these plots these results seem as cherry-picked.


limitations: The authors did not address limitations of their approach.
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 paper proposes an IM loss based training method for SNNs that shows high accuracy at low latency.
strengths_and_weaknesses: +The paper's experiments shows that the IM loss trains SNNs with SOTA results compared to works cited by the author.
-The paper presents a direct training method using BP for SNNs. This work is very derivative and incremental. There is a lot of work from Priya Panda's group at Yale, Emre Neftci's group, and many others with regard to SNN training. The authors have failed to acknowledge most recent works and the method they are proposing is very incremental in the context of those works. Further, many recent works on SNNs have targeted larger datatsets including video segmenattion with direct training. 
-Recent works like [5] show SNN training at very low latency with novel architectures. [10] uses a BN rule to train temproal SNNs. Is there a relationship between the implicit IM loss normalizationa nd the expicit normalization employed in [10]?

Below is a list of publications (not exhaustive) that the author should check:

[1] Towards spike-based machine intelligence with neuromorphic computing K Roy, A Jaiswal, P Panda Nature 575 (7784), 607-617

[2] Enabling spike-based backpropagation for training deep neural network architectures C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy Frontiers in neuroscience, 119

[3] Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? Y Kim, H Park, A Moitra, A Bhattacharjee, Y Venkatesha, P Panda ICASSP 2022-2022

[4] Neuromorphic Data Augmentation for Training Spiking Neural Networks Y Li, Y Kim, H Park, T Geller, P Panda arXiv preprint arXiv:2203.06145

[5] Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda arXiv preprint arXiv:2201.10355

[6] Optimizing deeper spiking neural networks for dynamic vision sensing Y Kim, P Panda Neural Networks 144, 686-698

[7] Federated Learning with Spiking Neural Networks Y Venkatesha, Y Kim, L Tassiulas, P Pand IEEE Transactions on Signal Processing 2021

[8] Beyond classification: directly training spiking neural networks for semantic segmentation Y Kim, J Chough, P Panda arXiv preprint arXiv:2110.07742

[9] Visual explanations from spiking neural networks using interspike intervals Y Kim, P Panda Scientific Reports 11, Article number: 19037 (2021)

[10] Revisiting batch normalization for training low-latency deep spiking neural networks from scratch Y Kim, P Panda Frontiers in neuroscience, 1638
questions: Please see my above comments on weakness and clarify teh technical novelty of the work.
limitations: Please see weakness section comments.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper proposed the information maximization (IM) loss for training deep SNN which maximizes information flow of the network and indirectly provides normalization during training. The IM loss is constructed to strengthen the mutual information between the membrane potential and the following spiking activity. In addition, the ESG method is propossed to improve network training. Experiments on benchmark image classification tasks show that the method improves the accuracy of SNN, and on CIFAR10 it surpass current SOTA SNN.   
strengths_and_weaknesses: The work proposed the IM loss to reduce information loss from membrane potential to spike, which moves the mean membrane potential towards the spiking threshold during training. The idea is new for the training of SNN and the soundness of the approach is sufficient, based on the derived equal relation between the mutual information (MI) of the membrane potential and the spike and the entropy of spiking distribution. However, driving the mean membrane potential near the threshold becomes an inductive bias, which could be suboptimal for the network performance under certain situation. Another outcome of the approach is that it increases the activation of the resulting network, which impairs the sparse computation advantage of SNN.
The ESG function is similar to the Dspike function (NeurIPS 2021, Yuhang Li, et. al), which should be cited.
In the experiment, except CIFAR10, the approach is outperformed by current SOTA SNN (e.g. Dspike (2021 NeurIPS), TET (2022 ICLR) have higher accuracies on CIFAR100, ImageNet and CIFAR10-DVS, though not listed on the table), leaving the general usage of the method questionable.

questions: The ESG method seems quite intuitive and empirical, is there any theoretical justification that can help to support it?

limitations: It would be helpful if the potential negative impact of moving membrane potential towards the spiking threshold can be discussed.
All SOTA SNN works should be listed on table to avoid misleading.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The paper focuses on improving spiking neural networks' performance, and presents two major contributions:

### 1. IM-loss

The information maximization loss (IM-loss) is evaluated by the $l_2$ distance between membrane potential and the neuron threshold. This form is based on three assumptions:

- Maximizing the information flow between membrane potential and output spike train can benefit SNNs' accuracy performance.

- The information flow is maximized when the spike trains' probability of 0 and 1 are equal.

- The membrane potential follows the Gaussian distribution, which implies the distribution's mean value is roughly equal to the distribution's half probability point (cumulative distribution function = 0.5).

### 2. ESG

The evolutionary surrogate gradient (ESG) adjusts the shape of the surrogate gradient function along the training process. The strategy is based on two assumptions:

- The wider shape of the surrogated gradient of the activation function, the stronger weight updating ability.

- The more accurate (sharp) of the gradient, the better training accuracy can be reached.


strengths_and_weaknesses: ### Strengths

The paper is well-drafted and presents the ideas clearly. The ideas are well evaluated experimentally.

### Weaknesses

1. Novelty. 

- Adding homeostatic mechanisms to regularize spiking neuron's firing activity is not a new trick. Previously, [1] adds a regularizer to drive SNNs' firing activity more sparse to save energy. However in this work, the author proposed regularizer drives membrane potential near threshold, which potentially increases the firing activity and leads to poorer energy efficiency. 

- Updating the shape of the surrogated function is also not new. [2] proposed to automatically adjust the temperature of the surrogated activation function in order to achieve the best training performance. Comparing to this work, [2] is more flexible.

2. Soundness

- The theoretical reasons (see summary above) the authors proposed that lead to the IM-loss are questionable. First, information loss is necessary for classification tasks (The whole image is compressed through a neural network and leaves its classes information only in the output layer). Therefore we do not know whether maximizing informaton flow benefits the performance. Second, the measurement of information flow neglects the important temporal dependency of LIF neurons, so the result that p(0)=p(1)=0.5 maximizes the information flow based on elementary information theory is oversimplified. Third, the membrane potential actually does not follow the Gaussian dist with free to change mean value. The paper cites [3] here to back up their claim, yet [3] only mentioned that the membrane potential distribution approximately follows a *zero mean* Gaussian distribution.

- The heuristic description of the ESG is also questionable. How do the authors evaluate the "weight updating ability"? I understand that too sharp a surrogated gradient function cannot sustain training, but the function is also not "the wider the stronger". Too wide the function is also harmful for training. Besides, it is also dubious to claim adjusting the function more and more accurate benefits the training of SNNs. Please refer to discussions in [2] [4] [5].


[1] Zenke F, Ganguli S. Superspike: Supervised learning in multilayer spiking neural networks[J]. Neural computation, 2018, 30(6): 1514-1541.

[2] Li Y, Guo Y, Zhang S, et al. Differentiable spike: Rethinking gradient-descent for training spiking neural networks[J]. Advances in Neural Information Processing Systems, 2021, 34: 23426-23439.

[3] Zheng H, Wu Y, Deng L, et al. Going deeper with directly-trained larger spiking neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(12): 11062-11070.

[4] Zenke F, Vogels T P. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks[J]. Neural computation, 2021, 33(4): 899-925.

[5] Yang Y, Zhang W, Li P. Backpropagated neighborhood aggregation for accurate training of spiking neural networks[C]//International Conference on Machine Learning. PMLR, 2021: 11852-11862.
questions: 1. Can you prove that the proposed regularizer can achieve a better trade-off between spiking sparsity and accuracy?

2. If one includes the temporal dependency into consideration, will the results of information flow maximization change?

3. What does the membrane potential distribution looks like in your experiment? Does it approximately follow the Gaussian distribution with a free-to-change mean value?

4. How do you evaluate the difference between ESG and  [2] mentioned above?

limitations: Yes.
review_title: null

Meta-review: metareview: This paper proposes a novel loss for training a spiking neural network that mitigates errors due to quantization. All reviewers agreed that the contributions of this paper were above the acceptance threshold.
confidence: Certain
Decision: Accept