Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 2: Strong Reject: For instance, a paper with major technical flaws, and/or poor evaluation, limited impact, poor reproducibility and mostly unaddressed ethical considerations.
review_content: summary: Authors present an empirical study on exploiting a classical coincidence detection based method for classification. Experiments on classifying pathogenic bacteria from Raman spectroscopy data is performed, showing a 0.5% increase in classification accuracy in comparison to using a deep learning approach with a ResNet-26 as performed in [Ho et al., 2019].
strengths_and_weaknesses: Paper is written as a very short report on an empirical observation. No details on the relevance of the approach to existing methods are present. A clear narrative on the methodological contribution is lacking. It is not really suitable for publication at NeurIPS in its current form.
questions: There is an interesting empirical observation here, yet the narrative is too shallow and clarity of the paper is weak. Generally, it is still not clear in text how the proposed "coincidence detection" application is applied without checking the implementation (lack of a thorough Methods section). There is also no relationship drawn with any existing neuromorphic processing algorithms that exploit temporal information encoding (lack of a thorough Related Work section). Perhaps the authors may consider extending their findings with a clear methodological contribution in a complete manuscript.

limitations: No discussions regarding potential societal impact or limitations.
review_title: null

final_score: rating: 1: Very Strong Reject: For instance, a paper with trivial results or unaddressed ethical considerations
review_content: summary: The authors draw inspiration from Rosenblatt’s perceptrons to investigate if coincidence detection can be an important element for neuromorphic signal processing. To illustrate their approach, the authors apply a perceptron to a bacteria identification problem, showing that it outperforms a recent deep neural network approach.
strengths_and_weaknesses: Strengths:
* The paper is thought-provoking, drawing attention to how early works on neural networks focused on neural temporal dynamics that are now making a come-back in the SNN domain.
* Coincidence detection as a basis for analog pattern recognition (as explained by Hopfield, [7] in the paper) is highly interesting.

Weaknesses:
* Arriving at equation 1 was a desillusion for me, as I very much liked the text leading to it. In this equation, coincidence detection is approximated with a linear multiplication (and a max function for picking the most probable class). This takes away the main idea of coincidence detection, which requires temporal delays.
* The simple "coincidence" detector gives very good results compared with a deep net. Although this could be demonstrating an advantage of coincidence detection, it may also be that the classification problem is actually not that difficult. Paper [10] seems to only apply a deep net to the problem. The authors only apply a linear function. What do other functions do? k-nearest  neighbors, SVMs, ...? 
* The paper is very short and fails to delve into the details of why the proposed method works so well. It seems logical that it has to be applied to other tasks as well to get more insight.

questions: 1. Is there no more suitable implementation of coincidence detection, e.g., within a spiking net?
2. Is your model in eq 1 not simply a perceptron? (With normalized inputs and a max on the outputs)
3. Can you report on other methods on the task of ref [10]?
4. Can you show similarly good performance on other tasks?
limitations: N/A
review_title: null

final_score: rating: 2: Strong Reject: For instance, a paper with major technical flaws, and/or poor evaluation, limited impact, poor reproducibility and mostly unaddressed ethical considerations.
review_content: summary: The authors make an argument for revisiting the use of neural circuits for pattern recognition problems citing the existence of similar neural circuits in animal brains. In that direction, the authors refer to the experiments in [10], which uses deep learning to classify pathogenic bacteria. 
With motivation from coincidence detection theory, the authors propose a multinomial logistic regression model to solve the problem. The dataset is preprocessed with a specific standardization and smoothening that allows the model to demonstrate the translation and scale-invariance of coincidence detection. The results show higher accuracy than the residual neural network used in [10] at isolate-level classification. 

strengths_and_weaknesses: The authors show a higher accuracy compared to a deep learning algorithm which helps show the potential of framing classification problems with the coincidence detection approach. However, the authors show the accuracy for one single train/validation split whereas the reference [10] shows a statistical mean of 82.2 +- 0.3% over 5 runs. For a fair comparison the authors should rerun the experiment and generate statistical mean/variance results for random train/validation split to remove any bias due to specific train/validation split selection. Additional details about the baseline model (ResNet-26) e.g., network architecture/training time/training accuracy would be helpful to get an all-round comparison. 

Further, the authors provide no explanation as to why their model fares better than the deep learning model for the specific problem being addressed. The paper lacks any detailed analysis on the contribution of the standardization and smoothening steps which could be generated by selectively applying these preprocessing steps. 

Lastly, the authors have added a few figures to their supplementary material which would have been valuable additions to the main paper. A discussion on these figures to help understand their results and how it compares to the baseline results would be helpful. For instance, the baseline has a higher antibiotic treatment classification accuracy than the proposed model, but this isn’t discussed and no justification is provided for the same.
questions: Authors should consider generating more stats on their accuracy % and provide a more thorough comparison with the baseline (ResNet-26). 
Further, authors should share additional experiments breaking down the contribution of standardization and smoothing steps. Lastly, explaining why their model fares better than the deep learning model for this problem will be helpful to understand the kind of problems this model can help solve.

limitations: The authors do not discuss the limitations of their work, kindly refer to Questions section for suggestions on how to improve this work.
review_title: null

Meta-review: metareview: The authors use a simple coincidence detection algorithm on pathogenic bacteria data set and increase performance by 0.5% with respect to a ResNet-26 that was published previously.

The manuscript is quite thought provoking showing that a simple algorithm can potentially outperform a complex deep neural network. However, all reviewers agreed that the study should be extended in order to be publishable at NeurIPS. In particular, they raised the following points.

- Relevance with respect to existing methods is not discussed. Related to that, the model seems similar to a perceptron with some normalization as preprocessing. This relation should be discussed.
- The method is tested only on a single data set. More evaluation would be needed in order to assess the generality of the method. Also, a clean statistical analysis is missing.
- An analysis for why method performs well on this (and potentially also other) data set is missing.
confidence: Certain
Decision: Reject