Structured PREreview of A Practical Tutorial on Spiking Neural Networks: Comprehensive Review, Models, Experiments, Software Tools, and Implementation Guidelines

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction explains the objective of the research by initially highlighting the motivation: the rapidly growing computational and energy costs of modern deep neural networks and the resulting sustainability concerns, which position Spiking Neural Networks (SNNs) as a biologically inspired, event-driven alternative promising competitive accuracy at substantially lower energy. The core objective is framed by addressing a key "Gap," identified as the lack of a unified, practice-oriented analysis and limited "apples-to-apples evidence on accuracy–energy trade-offs against equivalent ANN baselines". To resolve this, the work combines a comprehensive critical review, a hands-on tutorial, and standardized benchmarking by systematizing SNN components (models, encodings, and learning paradigms), providing a practical tutorial using a representative neuromorphic software stack (e.g., Lava), and establishing a side-by-side evaluation protocol comparing SNNs with architecturally matched ANNs on both shallow (MNIST) and deeper convolutional (CIFAR-10) tasks. The final stated aim is to measure accuracy and power-oriented proxies, ultimately distilling actionable design guidelines that offer a "coherent pathway from principles to practice" to design SNNs that "balance accuracy with energy efficiency in real-world settings

Are the methods well-suited for this research?

Highly appropriate

Yes, the methods employed in the preprint are well-suited for the research objective, as they are specifically designed to address the identified gap concerning the lack of a unified, practice-oriented analysis linking SNN design choices to measurable performance and power consumption. The approach establishes a rigorous side-by-side evaluation protocol by comparing SNN configurations with architecturally matched ANN baselines on both a shallow Fully Connected Network (FCN) for MNIST and a deeper VGG7 architecture for CIFAR-10, thereby providing the crucial "apples-to-apples evidence" sought by the study. The methodology systematically explores the core design knobs of SNNs by experimenting with a diverse set of nine neuron models (including LIF, Sigma-Delta, and AdEx) and seven input encoding schemes (such as Direct Coding, Rate Encoding, and Temporal TTFS), all trained using supervised surrogate-gradient methods

Are the conclusions supported by the data?

Highly supported

Yes, the conclusions presented in the preprint are thoroughly supported by the empirical data derived from the standardized benchmarking experiments on MNIST and CIFAR-10. 1. The first key conclusion, asserting a real yet tunable accuracy–energy trade-off, is evidenced by comparing the top-performing SNN configurations against the baseline Artificial Neural Network (ANN); for instance, on MNIST, ΣΔ neurons achieved 98.10% accuracy, closely matching the 98.23% ANN baseline, while remaining energetically below the ANN proxy, confirming the SNN energy advantage. Conversely, highly frugal encodings like R-NoM consistently produced the lowest energy consumption demonstrating the sharp trade-off by incurring larger accuracy drops, while Direct and ΣΔ encodings narrowed the accuracy gap while only demanding a moderate energy premium compared to the most frugal options. 2. The second conclusion regarding practical configuration rules is directly substantiated by data showing that accuracy-critical applications benefit from ΣΔ neurons paired with Direct coding on CIFAR-10 (achieving 83.0% at two time steps against the 83.6% ANN baseline) or Rate/ΣΔ encoding on MNIST, whereas energy-constrained scenarios achieved maximal efficiency using simpler IF/LIF neurons with Burst or R-NoM encoding at minimal time steps. Furthermore, data confirms that thresholds and time steps are decisive knobs, as increasing the threshold generally reduced energy consumption but sometimes severely impacted accuracy, validating the guideline that intermediate thresholds often deliver the best accuracy-per-joule, and that the number of time steps (T) should be kept minimal once the accuracy target is met. 3. Finally, the third conclusion concerning the neuromorphic potential is conceptually supported by the observation that the quantified per-inference energy reductions, stemming from SNN advantages like sparsity and reduced spike rates observed empirically, can be amplified into larger system-level savings when deployed on dedicated event-driven hardware capable of exploiting asynchrony and local computation.

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

The data presentations, which heavily rely on systematic tables and supplementary architectural visualizations, are well-suited to represent the complex, multi-dimensional comparisons necessary to fulfill the research objective of assessing accuracy–energy trade-offs across various Spiking Neural Network (SNN) configurations.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The authors clearly discuss, explain, and interpret their findings through a dedicated comparative analysis and a detailed conclusion, making the implications and next steps readily apparent. The interpretation is firmly grounded in the empirical data, establishing a consistent but tunable accuracy–energy tension across both shallow (MNIST FCN) and deep (CIFAR-10 VGG7) regimes, explaining that while configurations maximizing accuracy (such as ΣΔ neurons with Rate or Direct coding) incur a moderate energy premium, they generally remain below the architecturally matched ANN baseline, whereas highly frugal encodings (like R-NoM/Burst) sacrifice accuracy for minimal energy consumption. Furthermore, the authors explain the "neuromorphic potential," interpreting the per-inference energy reductions observed on the GPU proxy as benefits that can be amplified into larger system-level savings when deployed on dedicated event-driven hardware. Potential next steps are clearly outlined, focusing on addressing the study's primary limitation "the use of an operation-based proxy for energy" by suggesting future work that includes hardware-in-the-loop metering, tighter algorithm-hardware co-design, and the development of dynamic T and threshold schedules during inference

Is the preprint likely to advance academic knowledge?

Highly likely

The preprint is likely to advance academic knowledge because it explicitly addresses a recognized "key gap" in the field: the lack of a unified, practice-oriented analysis offering "apples-to-apples evidence on accuracy-energy trade-offs" against equivalent Artificial Neural Network (ANN) baselines across both shallow and deep network regimes. The methodology employed, combining a critical review, a hands-on tutorial, and standardized benchmarking across diverse combinations of nine neuron models and seven encoding schemes, is specifically designed to fill this void and systematize SNN components. By establishing a rigorous side-by-side evaluation protocol comparing SNNs with architecturally matched ANNs on MNIST and CIFAR-10 and quantifying performance using both accuracy and a transparent per-inference energy proxy, the research delivers concrete empirical evidence. This systematic empirical approach results in "actionable design guidelines" that interpret the consistent yet tunable accuracy-energy trade-off observed in the data, guiding future researchers and practitioners on how to select neuron models, encoding schemes, thresholds, and time steps to meet specific application goals, thereby offering a "coherent pathway from principles to practice" necessary for developing efficient and sustainable AI.

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, it’s of high quality

The preprint is highly recommended, particularly for researchers and practitioners focused on energy-efficient and sustainable AI, because it directly addresses the critical "key gap" identified in the field: the lack of a unified, practice-oriented analysis linking Spiking Neural Network (SNN) design choices to measurable accuracy and energy trade-offs

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.