Coincidence Detection Is All You Need
Authors: Celestine Preetham Lawrence
Venue: NeurIPS 2022 Submitted (2022)
Keywords: coincidence detection
pattern recognition
neuromorphic signal processing
spiking neural network

Abstract: This paper demonstrates that the performance of coincidence detection - a classic neuromorphic signal processing method found in Rosenblatt's perceptrons with distributed transmission times, can be competitive to a state-of-the-art deep learning method for pattern recognition. Hence, we cannot remain comfortably numb to the prevailing dogma that efficient matrix-vector operations is all we need; but should enquire with greater vigour if more advanced continual learning methods (running on spiking-neural network hardware with neuromodulatory mechanisms at multiple timescales) can beat the accuracy of task-specific deep learning methods.   

Coincidence Detection Is All You Need
Anonymous Author(s) Affiliation Address email
Abstract
This paper demonstrates that the performance of coincidence detection a classic neuromorphic signal processing method found Rosenblatt's 's perceptrons with distributed transmission times, can be competitive to a state-of oftee-of-hh-arr deep learning method for pattern recognition. Hence, we cannot remain comfortably numb to the prevailing dogma that efficient matrix-vector operations is all we need; but should enquire with greater vigour if more advanced continual learning methods (running spiking neural network hardware with neuromodulatory mechanisms at multiple timescales) can beat the accuracy of task-specific deep learning methods.
Introduction
Frank Rosenblatt and his team (1957-1971) built and analyzed several kinds of perceptrons 1
11 networks of sensory, association and receptor neurons; which in contemporary deep learning termi- nology relates to the input, hidden and output layers. The propagating signals were binary (compatible 13 with spike-based view), the synaptic delays (transmission times) and weights (memory states) could be analog, the network could be recurrent and was often randomly interconnected, and learning subnetwork by some error-correctivy reinforcement. The synaptic delays were not learnt but instead randomly distributed in Rosenblatt's Tobermory perceptrons [5], and this was rich enough to realize concentration innentrraion-invariaat and uniform time-warp invariant spatiotemporal classification by logarithmic encoding and coincidence detection. However, the processing speed of commercial Von Neumann computers advanced exponentially and outperformed neuromorphic hardware on yesterdecade's ' benchmarks [6]. The Tobermory per-
15 often meant tuning the weights the association-recepue
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21 ceptron was forgotten, nevertheless, the utility of logarithmic encoding and coincidence detection was formalized by John Hopfield 7 an efficient solution the analog match problem in pattern recognition.
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Now, half century after the accidental demise of Rosenblatt, neuromorphic signal processors 25 making comeback. For example, (1) Intel's Loihi with spike-time dependent plasticity mechanisms for learning olfactory pattern recognizers [8]; (2) Physical reservoir computing networks 9 where
27 the interconnectivity of the hidden layer is unchanged, closer to the spirit of Rosenblatt's randomly interconnected sensory-associatee subnetwork.
29 Here, to strengthen the case for revisiting classic methods on novel and modern hardware, we evaluate the performance of coincidence detection in comparison deep learning method. Nothing more, 31 nothing less, although this work was triggered by a rabid interest employing artificial intelligence to sniff out infections and prevent future pandemics.
Submitted to 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Do not distribute.

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Table 1: Test accuracy (%)
ResNet-26
Coincidence detection
82.2 0.3 (from [0]
82.7 (this work)
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Methods
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Here, we consider the work 10 of an interdisciplinary team, where layer convolutional neural network with residual connections (ResNet-26) was successfully trained for classifying pathogenic bacteria by Raman spectroscopy. In their work, there are N 30 classes of bacterial isolates and they begin with ResNet-26 pre-trained on N x 2000 spectra, then for each class n 1 N there are M 100 training spectra, and similarly N M 3000 test spectra. Each spectrum æ: contains 1000 floating- foating-point numbers ranging between 0 and 1. Although compute intensive, their deep learning method proved be tool of great convenience for pattern recognition in a challenging dataset, where intra-isolate spectra were often more dissimilar than inter-isolate spectra.
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Our method tackle the above dataset, is inspired by the theory of how coincidence detection in animal brains is fundamental for odour classification in complex and turbulent mixtures. Each class n has a vector representation Wy, that learnt, and an input vector results in an output class y( = arg,, max æ A wn) where we introduce the operator A to represent the coincidence between two signals. The analytical nature of coincidence detection depends on the specificities of the ion-channels and the membranes involved [11], and may even incorporate nonlinear leaky-integrate
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12 multiple timescale mechanisms. We not yet have a complete theory of neuromorphic signal 48
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processing, so here we introduce an approximation for the translation and scale-invariant property of coincidence detection as
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arg, max( A wn) s arg, max(w, +),
where è is the zero-mean
normalization of æ.
52 Thus, the approximation in Eq. (1) allows y(æ) to be learnt by logistic regression on the normalized
53 dataset. We discard the pre-training data, pre-process the training and test spectra by range-1 mean
54 filter, and use the default method for logistic regression in Wolfram Mathematica (1.2-regularization 0.0001, optimization method limited-memory BFGS), Code is provided in the supplemental
56 material for reproducibility.
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Result and outlook
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The coincidence detection (via normalized logistic regression) method introduced here achieves test 59 accuracy greater than ResNet-26 (see Table 1), and it took less than 3 seconds to train the classifier
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60 on modern desktop (without any special-purpose GPUs). Check the Appendix for a confusion matrix plot of the training and test data. Note that the training data was fit all at once 62 accuracy. With a more neuromorphic coincidence detection method and a learning method that adapts the synaptic delays continually, to keep track under changing environmental conditions, may achieve even greater accuracies.
100%
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References
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[1] Frank Rosenblatt. The perceptron, perceiving and recognizing automaton Project Para. Cornell Acronautical Laboratory, Inc. Report no. 85-460-1, 1957.
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[2] Frank Rosenblatt. The perceptron: A theory of statistical separability in cognitive systems. Cornell Aeronautical Laboratory, Inc. Report no. VG-1196-G-1, 1958.
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[3] Frank Rosenblatt. Principles of neurodynamics. perceptrons and the theory of bruin mechanisms. Coreell Acronautical Laboratory, Inc. Report no. 1196-G-8, 1961.

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[4] Frank Rosenblatt. Cognitive systems research program. Technical report, Cornell University, Ithaca, New York, 1971.
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[5] Frank Rosenblatt. A description of the tobermory perceptron. In Collected Technical Papers, volume 2. Cornell University, Ithaca, New York, 1963.
[6] George Nagy. Neural networks-then and now, IEEE Transactions Neural Networks, 2(2):316-
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318, 1991.
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[7] John J Hopfield. Pattern recogmition computation using action potential timing for stimulus representation. Nature, 376(6535)333-366 1995.
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[8] Nabil Imam and Thomas A Cleland. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nature Machine Intelligence, 2(3):181-191, 2020.
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[9] G. Tanaka, T. Yamane, J.B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose. Recent advances in physical reservoir computing: A review. Neural Networks, 115:100-1 15::3,0-13,, 2019.
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[10]
Chi-Sing Ho, Neal Jean, Catherine A Hogan, Lena Blackmon, Stefanie S Jeffrey, Mark Holodniy, Niaz Banaei, Amr AE Saleh, Stefano Ermon, and Jennifer Dionne. Rapid identification of pathogenic bacteria using raman spectroscopy and deep learning. Nature communications, 10(1):1-8, 2019.
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[11] Nelson Spruston. Pyramidal neurons: dendritic structure and synaptic integration. Nature Reviews Neuroscience, 9()): 221, 2008.
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[12] Wondimu Teka, Toma M Marinov, and Fidel Santamaria. Neuronal spike timing adapta- tion described with a fractional leaky integrate-and-five model. PL.S computational biology, 1003352,,, 2014.
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Checklist
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1. For all authors...
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(a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? [Yes] See Table
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(b) Did you describe the limitations of your work? [Yes] Equation (1) makes it clear that employ an approximation for coincidence detection.
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(c) Did you discuss any potential negative societal impacts of your work? [N/A]
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(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results... 103
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(a) Did you state the full set of assumptions of all theoretical results? [N/A]
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(b) Did you include complete proofs of all theoretical results? [N/A]
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3. If you ran experiments..
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(a) Did you include the code, data, and instructions needed reproduce the main ex- perimental results (either in the supplemental material or as URL)? [Yes] Check supplemental material
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(b) Did you specify all the training details (c.g., data splits, hyperparameters, how they were chosen)? [Yes]
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(c) Did you report error bars (e.g., with respect the random seed after running experi- ments multiple times)? [N/A]
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(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] qualitatively, the results section
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4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators? [Yes]
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(b) Did you mention the license of the assets? [Yes] In the supplemental information
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(c) Did you include any new assets either in the supplemental material or as URL? [No]
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(d) Did you discuss whether and how consent was obtained from people whose data you' ree using/curating? [N/A]
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(c) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [N/A]
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5. If you used crowdsourcing or conducted research with human subjects...
(a) Did you include the full text of instructions given participants and screenshots, if applicable? [N/A]
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(b) Did you deseribe any potential participant risks, with links to Institutional Review Board (IRB) approvals, applicable? [N/A]
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(c) Did you include the estimated hourly wage paid participants and the total amount spent on participant compensation? [N/A]
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A
Appendix
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132 Confusion matrix of the training and test data. Wolfram Mathematica code to reproduce these results provided as supplemental material.

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procicted dass


Reviews:
[{"review_id": "v_VbuPkvHb", "reviewer": "Reviewer_V6Wx", "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.", "questions": "1. Is there no more suitable implementation of coincidence detection, e.g., within a spiking net?\n2. Is your model in eq 1 not simply a perceptron? (With normalized inputs and a max on the outputs)\n3. Can you report on other methods on the task of ref [10]?\n4. Can you show similarly good performance on other tasks?", "limitations": "N/A", "rating": 1, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n* 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.\n* Coincidence detection as a basis for analog pattern recognition (as explained by Hopfield, [7] in the paper) is highly interesting.\n\nWeaknesses:\n* 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.\n* 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, ...? \n* 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.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "Ww16UitdxKm", "reviewer": "Reviewer_ctyh", "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].", "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.\n", "limitations": "No discussions regarding potential societal impact or limitations.", "rating": 2, "confidence": 5, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "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.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "FEi2c9H8kHX", "reviewer": "Reviewer_QphW", "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. \nWith 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. \n", "questions": "Authors should consider generating more stats on their accuracy % and provide a more thorough comparison with the baseline (ResNet-26). \nFurther, 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.\n", "limitations": "The authors do not discuss the limitations of their work, kindly refer to Questions section for suggestions on how to improve this work. ", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "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. \n\nFurther, 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. \n\nLastly, 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.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Reject