Pith. sign in

REVIEW 3 major objections 3 minor 54 references

Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions

T0 review · 3 major / 3 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Directly trained spiking YOLO can now match ANN accuracy on static object detection.

desk verdict The claimed near-zero SNN/ANN gap rests on mAP numbers that don't survive contact with the paper's own tables; the 3D-conv idea is reasonable, but the evidence is not there yet. read the letter →

arxiv 2412.17654 v1 pith:2EEGU3W4 submitted 2024-12-23 cs.AI cs.CVcs.NE

Huaxu He This is my paper · ORCID
classification cs.AIcs.CVcs.NE
keywords spikingneuralnetworks3DconvolutionobjectdetectiontemporalinformationprocessingdirecttrainingmembranepotentialrecurrenceYOLOstaticimagedatasets
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Spiking neural networks (SNNs) promise low-energy, event-driven computation, but directly trained SNNs have lagged far behind ordinary artificial neural networks (ANNs) on frame-based object detection. This paper claims to close that gap by giving the network a stronger temporal processing pipeline: replacing 2D convolutions with 3D convolutions so information flows across time steps inside the convolution itself, and adding a recurrence mechanism that initializes the membrane potential with the last time step's input. On the COCO and VOC datasets, the resulting spiking YOLOv5n reaches mAP@0.5 within 0.008 (VOC) and 0.001 (COCO) of the same-architecture ANN, and exceeds it when trained longer on VOC. If true, this removes a major obstacle to deploying directly trained SNNs on standard static-vision workloads while keeping their event-driven, energy-efficient character.

What carries the argument

The load-bearing machinery is a pair of changes to the standard SNN pipeline. First, the 2D convolution kernel, applied independently to each time step's feature map with the same weights, is replaced by a 3D convolution kernel that also convolves along the time axis, directly mixing information from adjacent time steps. Second, the spiking neuron's membrane potential is no longer initialized to zero: after a backward pass, the input current of the last time step seeds the membrane potential at the first time step, so temporal information flows in a loop rather than only forward in time. The neuron update is $V[t] = \ell_t V[t-1] + i_t X[t]$, with learnable per-time-step decay $\ell_t$ and input constants $i_t$, inherited from the hybrid-coding neuron and the leakage/threshold scheme the paper combines. These components let the network treat each time step's spikes as part of a spatiotemporal signal rather than as repeated presentations of the same static image.

What would settle it

Train the same network while shuffling only the input currents but keeping each time step's learnable constants aligned with their original time index, and compare the mAP drop; if the drop disappears or reverses under realignment, the temporal-information interpretation is falsified. Alternatively, retrain the full model with the 3D convolutions widened in the channel dimension instead of the time dimension to match the parameter count: if the accuracy gap versus the ANN persists, the gain is parameter-driven rather than temporal.

Watch

Extended reading notes

Core claim

The paper's central claim is that the long-standing performance gap between directly trained SNNs and ANNs on static object detection is not a fundamental limitation of spiking neurons, but a deficit in how temporal information is communicated. The author proposes two interventions: replace the 2D convolution kernels, which process each time step independently with shared weights, with 3D convolution kernels that slide over the time dimension as well; and modify the spiking neuron so that the membrane potential at the first time step is initialized with the input current of the last time step, creating a recurrence loop. The neuron model combines learnable per-time-step decay and input constants with the leakage and threshold design of the cited hybrid-coding and direct-encoding neurons. The reported experiments show the resulting network, built on YOLOv5n with SEW-ResNet connections, reaching mAP@0.5 of 0.601 on VOC against an ANN score of 0.609, and 0.265 on COCO against 0.266, at 15 time steps; with 400 training epochs the VOC score rises to 0.618, surpassing the ANN. The author frames this as the first successful application of a directly trained spiking YOLO to a static dataset at ANN-comparable accuracy.

Load-bearing premise

The paper's evidence that its gains come specifically from temporal processing rests on the shuffling experiment in Section 4.5, which assumes that shuffling input currents along time removes temporal order without otherwise changing the task; if the learnable per-time-step neuron parameters are time-indexed, the shuffle also breaks their alignment with the inputs.

Editorial extensions

If this is right

  • Directly trained SNNs can serve as drop-in replacements for ANNs on frame-based object detection at comparable accuracy, at least at the YOLOv5n scale with the reported 15 time steps and 300 to 400 training epochs.
  • Longer training from 300 to 400 epochs raised the SNN's VOC mAP@0.5 from 0.601 to 0.618, overtaking the same-architecture ANN, which suggests SNNs need more epochs to converge.
  • Ablation results attribute most of the gain to the 3D convolution (removing it drops mAP@0.5 from 0.567 to 0.534 at 4 time steps) and to the recurrence mechanism (removing it drops mAP to 0.552), so both components are needed for the full effect.
  • Scaling the backbone from YOLOv5n to YOLOv5s narrowed the SNN-to-ANN gap from 0.028 to 0.011, suggesting the method transfers to larger models.
  • The temporal-shuffling experiments indicate that larger time steps and 3D convolutions make the network more dependent on temporal order, while a traditional 2D-convolution LIF network performs slightly better when temporal order is destroyed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same 3D-convolution-plus-recurrence recipe could be applied to other spiking backbones, such as spiking ResNets or transformers, where per-time-step weight sharing limits temporal communication; the reported scaling trend from YOLOv5n to YOLOv5s suggests the benefit may grow with model capacity.
  • If the temporal mechanisms are truly responsible for the gain, the method should also improve SNN performance on event-based datasets, where temporal order carries object motion; the paper does not test this, but its own temporal-dependence results predict it.
  • The parameter-count increase from 3D convolutions trades away part of the energy-efficiency advantage the paper motivates, so a complete comparison would require an energy or latency measurement that accounts for the extra multiply-accumulate operations.
  • Because the shuffling control is potentially confounded by time-indexed learnable constants, the temporal-information interpretation is the part of the paper most worth probing; an ablation with shared, time-independent decay and input constants would cleanly separate the two mechanisms.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper proposes improving directly trained spiking neural networks (SNNs) for static object detection by (i) replacing 2D convolutions with 3D convolutions to mix information across the time dimension, and (ii) introducing a temporal recurrence mechanism that initializes the first membrane potential with the last time step's input current. The neuron model is a combination of a learnable-decay/input-current model from the author's prior work [33] and a direct-input neuron [34], and hybrid encoding with Gated Attention Coding is optionally employed. Using YOLOv5n as the backbone, the paper reports mAP@0.5 of 0.601 versus 0.609 on VOC and 0.265 versus 0.266 on COCO, claiming that this is the first directly trained spiking YOLO to match the same-architecture ANN on static datasets. The paper also presents ablations over the 3D convolution, recurrence, neuron model, encoding, and time steps, plus a temporal-shuffling experiment intended to show that performance gains come from temporal information processing.

Significance. If the reported numbers are reliable, the contribution would be a meaningful step toward closing the ANN-SNN performance gap in object detection: the gap shrinks from 0.078 to 0.008 on VOC and to 0.001 on COCO using the same architecture. The paper is also commendable for including ablated comparisons of each proposed component, for testing different model scales and input sizes, and for explicitly acknowledging the energy-efficiency cost of 3D convolutions. However, the central claim is purely empirical and is not backed by multiple seeds, error bars, or released code. Moreover, the same configuration appears to have two very different reported mAP values in Table 1 and Table 9, and the temporal-shuffling control is confounded by the time-indexed learnable parameters in Eq. (6). These issues mean the central empirical claim is not currently established.

major comments (3)
  1. [§4.2, Table 1; §4.5, Table 9] The headline parity claim rests on Table 1, which reports mAP@0.5 = 0.601 for the proposed method on VOC with T=15, versus 0.609 for the ANN. However, Table 9 row 1, labeled '3D, recurrence, This work, T=15', reports the with-temporal-information mAP@0.5 as 0.551. The paper does not state Table 9's encoding scheme, GAC setting, input resolution, or dataset split, so the reader cannot tell whether this is a different configuration or the same VOC configuration. If it is the same VOC configuration, the 0.050 discrepancy is roughly six times the entire claimed ANN gap of 0.008. This internal inconsistency directly undermines the central empirical claim.
  2. [§4.5, Eq. (6)] The temporal-shuffling experiment is intended to isolate the network's use of temporal order by shuffling input currents along the time dimension. But Eq. (6) defines l_t and i_t as learnable parameters with a time subscript, and Section 2.3 credits [33] with introducing per-time-step learnable membrane-potential parameters. If l_t and i_t are not shared across time, shuffling the inputs while leaving each parameter at its original time index changes the input-parameter alignment, so the resulting mAP drop could be caused by misalignment rather than by the loss of temporal order. The paper never states whether these parameters are shared or time-indexed, and therefore the mechanistic conclusion from the shuffling experiment is unsupported.
  3. [§4.1, Table 1; §4.3, Tables 3-5] All reported mAP values are single runs with no error bars, no number of seeds, and no indication of variance. The claimed ANN-SNN differences are 0.008 on VOC and 0.001 on COCO; these margins are within the range of typical random-seed variation for object detection training. Since the central claim is exactly that the SNN is comparable to the ANN, statistical reproducibility is load-bearing. The absence of code, seeds, and repeated runs leaves the central claim unsupported even apart from the Table 1/Table 9 discrepancy.
minor comments (3)
  1. [Table 9] The Difference column lists '0.028/0.05%' for row 1; since 0.028/0.551 = 0.0508, the second entry appears to be a fraction expressed as a decimal rather than a percentage. Rows 2-6 have the same issue (e.g., 0.022/0.518 = 0.0425, not 0.042%), and the label 'Difference' should be corrected or clarified.
  2. [§4.4, Table 8] The time step used for the input-size experiments is not stated in the text or the table caption. The 224-pixel entry of 0.567 appears to match T=4 in Table 5, but this should be explicit, especially because the preceding model-size experiment was described as using six time steps.
  3. [Abstract] The statement 'The code for this job can be contacted via email' is not a usable reproducibility statement; the authors should provide a public repository or a clear release plan.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central mAP claims are empirical measurements, and the self-citation to [33] is supported by in-paper ablations rather than used as a substitute for derivation.

full rationale

I walked the paper's claimed derivation chain. The paper's central claims are empirical: Tables 1-5 report measured mAP@0.5 values for networks with and without 3D convolutions, recurrence, hybrid coding, and the borrowed neuron model. These are direct experimental measurements, not quantities derived from an input by construction. The learnable neuron model in Eq. (6), V[t] = l_t V[t-1] + i_t X[t], is explicitly attributed to the author's prior work [33] and to [34]; it is a borrowed component, not a result that the paper derives from its own conclusion. Its contribution is validated inside this paper by Table 3 (adding the neuron raises mAP from 0.542 to 0.565) and Table 6 (0.545 vs 0.530 for LIF), so the self-citation is not load-bearing in the sense of replacing evidence. No fitted parameter is renamed as a prediction; no uniqueness theorem is imported; no known result is repackaged under new coordinates. I also considered the Section 4.5 shuffling control: because l_t and i_t may be time-indexed learnable parameters from [33], shuffling input currents can change input-parameter alignment, and Table 9 row 1 (0.551) is inconsistent with Table 1 (0.601). However, a confounded control or an internal numerical inconsistency is a validity and reproducibility concern, not a circular reduction of the kind defined by the rubric. The paper is self-contained against external benchmarks in the sense that its headline numbers come from its own training runs. Therefore no significant circularity is present.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper's central claim depends on several learnable neuron parameters whose values are not reported, and on a control experiment that assumes shuffling removes only temporal order. The method heavily borrows from the author's prior work [33], whose components are not independently verified in this paper.

free parameters (6)
  • l_t (membrane potential decay constant)
    Learnable parameter in Eq. (6) controlling the decay of membrane potential between time steps; the paper does not report its fitted values or whether it is per time step.
  • i_t (input current constant)
    Learnable parameter in Eq. (6) scaling the input current; fitted during training.
  • V_th (learnable firing threshold)
    Learnable threshold in Eq. (7) controlling spike generation.
  • V_re (learnable reset potential)
    Learnable reset value in Eq. (8) after a spike.
  • GAC gating parameters
    Gated Attention Coding from [32] is included in the full method and adds learned gating weights in the encoder.
  • Hybrid encoding parameters
    The hybrid coding scheme from [33] concatenates time-encoded and direct-encoded information, likely with learnable encoding weights; not specified.
assumptions (4)
  • domain assumption Surrogate gradient STBP training is valid for direct SNN training
    Invoked throughout Section 3 without proof; standard in the field but an unproved background assumption.
  • standard math LIF/IF dynamics in Eqs. (1) to (5) accurately model spiking neurons
    Standard neuron models are used as the base; accepted from prior literature.
  • domain assumption The ANN baseline YOLOv5n trained at 224x224 is a fair, converged comparator
    The paper does not demonstrate ANN convergence at matched epochs or report variance; used in Tables 1, 7, 8.
  • ad hoc to paper Shuffling input currents along time isolates temporal information
    Used in Section 4.5 to claim that performance drops are due to loss of temporal order; confounded by possible time-indexed parameters.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions." pith.science (2026). https://pith.science/paper/2EEGU3W4

@misc{pith2026241217654,
  author       = {Pith},
  title        = {Pith review of: Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2EEGU3W4}},
  note         = {Machine review of arXiv:2412.17654}
}
read the original abstract

Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. Recently, directly trained SNNs have shown potential to match or surpass the performance of traditional Artificial Neural Networks (ANNs) in classification tasks. However, in object detection tasks, directly trained SNNs still exhibit a significant performance gap compared to ANNs when tested on frame-based static object datasets (such as COCO2017). Therefore, bridging this performance gap and enabling directly trained SNNs to achieve performance comparable to ANNs on these static datasets has become one of the key challenges in the development of SNNs.To address this challenge, this paper focuses on enhancing the SNN's unique ability to process spatiotemporal information. Spiking neurons, as the core components of SNNs, facilitate the exchange of information between different temporal channels during the process of converting input floating-point data into binary spike signals. However, existing neuron models still have certain limitations in the communication of temporal information. Some studies have even suggested that disabling the backpropagation in the time dimension during SNN training can still yield good training results. To improve the SNN handling of temporal information, this paper proposes replacing traditional 2D convolutions with 3D convolutions, thus directly incorporating temporal information into the convolutional process. Additionally, temporal information recurrence mechanism is introduced within the neurons to further enhance the neurons' efficiency in utilizing temporal information.Experimental results show that the proposed method enables directly trained SNNs to achieve performance levels comparable to ANNs on the COCO2017 and VOC datasets.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 31 canonical work pages

  1. [33]

    Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid Coding

    H. He, "Adaptive Spiking Neural Networks with Hybrid Coding," ArXiv, vol. abs/2408.12407, 2024

    work page Pith review arXiv 2024

  2. [34]

    DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks,

    N. Rathi and K. Roy, "DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks," ArXiv, vol. abs/2008.03658, 2020

    arXiv 2008

  3. [1]

    Networks of spiking neurons: The third generation of neural network models,

    W. Maass, "Networks of spiking neurons: The third generation of neural network models," Neural Networks, vol. 10, no. 9, pp. 1659-1671, 1997/12/01/ 1997, doi: https://doi.org/10.1016/S0893-6080(97)00011-7

    doi:10.1016/s0893-6080(97)00011-7 1997

  4. [2]

    Towards spike-based machine intelligence with neuromorphic computing,

    K. Roy, A. Jaiswal, and P. Panda, "Towards spike-based machine intelligence with neuromorphic computing," Nature, vol. 575, no. 7784, pp. 607-617, 2019/11/01 2019, doi: 10.1038/s41586-019-1677-2

    doi:10.1038/s41586-019-1677-2 2019

  5. [3]

    A Hybrid Spiking Neural Network Reinforcement Learning Agent for Energy-Efficient Object Manipulation,

    K. M. Oikonomou, I. Kansizoglou, and A. Gasteratos, "A Hybrid Spiking Neural Network Reinforcement Learning Agent for Energy-Efficient Object Manipulation," Machines, 2023

    work page 2023

  6. [4]

    Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition,

    Y. Cao, Y. Chen, and D. Khosla, "Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition," International Journal of Computer Vision, vol. 113, no. 1, pp. 54-66, 2015/05/01 2015, doi: 10.1007/s11263-014-0788-3

    doi:10.1007/s11263-014-0788-3 2015

  7. [5]

    Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification,

    B. Rueckauer, I.-A. Lungu, Y. Hu, M. Pfeiffer, and S.-C. Liu, "Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification," (in English), Frontiers in Neuroscience, Original Research vol. 11, 2017-December-07 2017, doi: 10.3389/fnins.2017.00682

    arXiv 2017

  8. [6]

    A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration,

    Y. Li, S. Deng, X. Dong, R. Gong, and S. Gu, "A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration," presented at the Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research,

    work page

Show all 54 references
  1. [7]

    Spiking Deep Residual Networks,

    Y. Hu, H. Tang, and G. Pan, "Spiking Deep Residual Networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 5200-5205, 2023, doi: 10.1109/TNNLS.2021.3119238

    2023

  2. [8]

    Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes,

    C. Stöckl and W. Maass, "Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes," Nature Machine Intelligence, vol. 3, no. 3, pp. 230-238, 2021/03/01 2021, doi: 10.1038/s42256-021-00311-4

    2021 doi

  3. [9]

    Training Deep Spiking Neural Networks Using Backpropagation,

    J. H. Lee, T. Delbruck, and M. Pfeiffer, "Training Deep Spiking Neural Networks Using Backpropagation," (in English), Frontiers in Neuroscience, Original Research vol. 10, 2016-November-08 2016, doi: 10.3389/fnins.2016.00508

    2016

  4. [10]

    Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks,

    Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, "Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks," (in English), Frontiers in Neuroscience, Original Research vol. 12, 2018-May-23 2018, doi: 10.3389/fnins.2018.00331

    2018

  5. [11]

    SLAYER: Spike Layer Error Reassignment in Time,

    S. Shrestha and G. Orchard, "SLAYER: Spike Layer Error Reassignment in Time," in Neural Information Processing Systems, 2018

    2018

  6. [12]

    W. A. Zhang, Peng%D%I, Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks. p. Medium: X

  7. [13]

    Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures,

    C. Lee, S. S. Sarwar, P. Panda, G. Srinivasan, and K. Roy, "Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures," (in English), Frontiers in Neuroscience, Original Research vol. 14, 2020-February-28 2020, doi: 10.3389/fnins.2020.00119

    2020

  8. [14]

    Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks,

    J. Kim, K. Kim, and J.-J. Kim, "Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks," ArXiv, vol. abs/2006.02642, 2020

    2006 arXiv

  9. [15]

    Advancing Spiking Neural Networks Toward Deep Residual Learning,

    Y. Hu, L. Deng, Y. Wu, M. Yao, and G. Li, "Advancing Spiking Neural Networks Toward Deep Residual Learning," IEEE transactions on neural networks and learning systems, vol. PP, 2021

    2021

  10. [16]

    Deep Residual Learning in Spiking Neural Networks,

    W. Fang, Z. Yu, Y. Chen, T. Huang, T. Masquelier, and Y. Tian, "Deep Residual Learning in Spiking Neural Networks," in Neural Information Processing Systems, 2021

    2021

  11. [17]

    Spikformer: When Spiking Neural Network Meets Transformer,

    Z. Zhou et al., "Spikformer: When Spiking Neural Network Meets Transformer," p. arXiv:2209.15425doi: 10.48550/arXiv.2209.15425

    doi arXiv

  12. [18]

    CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks,

    Y. Huang et al., "CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks," ArXiv, vol. abs/2402.04663, 2024

    2024 arXiv

  13. [19]

    Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks,

    W. Fang, Z. Yu, Y. Chen, T. Masquelier, T. Huang, and Y. Tian, "Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks," in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 10-17 Oct. 2021 2021, pp. 2641-2651, doi: 10.110...

    2021

  14. [20]

    GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks,

    X.-H. Yao, F. Li, Z. Mo, and J. Cheng, "GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks," ArXiv, vol. abs/2210.13768, 2022

    2022 arXiv

  15. [21]

    Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks,

    G. Shen, D. Zhao, and Y. Zeng, "Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks," Neural networks : the official journal of the International Neural Network Society, vol. 170, pp. 190-201, 2023

    2023

  16. [22]

    Enhancing Training of Spiking Neural Network with Stochastic Latency,

    S. Anumasa, B. P. Mukhoty, V. Bojkovic, G. D. Masi, H. Xiong, and B. Gu, "Enhancing Training of Spiking Neural Network with Stochastic Latency," in AAAI Conference on Artificial Intelligence, 2024

    2024

  17. [23]

    Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting,

    S.-W. Deng, Y. Li, S. Zhang, and S. Gu, "Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting," ArXiv, vol. abs/2202.11946, 2022

    2022 arXiv

  18. [24]

    Going Deeper With Directly-Trained Larger Spiking Neural Networks,

    H. Zheng, Y. Wu, L. Deng, Y. Hu, and G. Li, "Going Deeper With Directly-Trained Larger Spiking Neural Networks," in AAAI Conference on Artificial Intelligence, 2020

    2020

  19. [25]

    Deep learning in spiking neural networks,

    A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. Maida, "Deep learning in spiking neural networks," Neural Networks, vol. 111, pp. 47-63, 2019/03/01/ 2019, doi: https://doi.org/10.1016/j.neunet.2018.12.002

    2019 doi

  20. [26]

    Deep Directly-Trained Spiking Neural Networks for Object Detection,

    Q. Su et al., "Deep Directly-Trained Spiking Neural Networks for Object Detection," 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6532-6542, 2023

    2023

  21. [27]

    High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost,

    J. Hu et al., "High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost," ArXiv, vol. abs/2405.16466, 2024

    2024 arXiv

  22. [28]

    Online Training Through Time for Spiking Neural Networks,

    M. Xiao, Q. Meng, Z. Zhang, D. K. He, and Z. Lin, "Online Training Through Time for Spiking Neural Networks," ArXiv, vol. abs/2210.04195, 2022

    2022 arXiv

  23. [29]

    Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks,

    Q. Meng, M. Xiao, S. Yan, Y. Wang, Z. Lin, and Z. Luo, "Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks," 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6143-6153, 2023

    2023

  24. [30]

    TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks,

    R. J. Zhu, M. Zhang, Q. Zhao, H. Deng, Y. Duan, and L. J. Deng, "TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14, 2024, doi: 10.1109/TNNLS.2024.3377717

    2024

  25. [31]

    Temporal-wise Attention Spiking Neural Networks for Event Streams Classification,

    M. Yao et al., "Temporal-wise Attention Spiking Neural Networks for Event Streams Classification," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10201-10210, 2021

    2021

  26. [32]

    Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks,

    X. Qiu, R. Zhu, Y. Chou, Z. Wang, L.-J. Deng, and G. Li, "Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks," ArXiv, vol. abs/2308.06582, 2023

    2023 arXiv

  27. [35]

    Microsoft COCO: Common Objects in Context,

    T.-Y. Lin et al., "Microsoft COCO: Common Objects in Context," in European Conference on Computer Vision, 2014

    2014

  28. [36]

    The Pascal Visual Object Classes (VOC) Challenge,

    M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, 2010/06/01 2010, doi: 10.1007/s11263-009-0275-4

    2010 doi

  29. [37]

    Retinomorphic Object Detection in Asynchronous Visual Streams,

    J. Li, X. Wang, L. Zhu, J. Li, T. Huang, and Y. Tian, "Retinomorphic Object Detection in Asynchronous Visual Streams," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 2, pp. 1332-1340, 06/28 2022, doi: 10.1609/aaai.v36i2.20021

    2022 doi

  30. [38]

    You Only Look Once: Unified, Real-Time Object Detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016 2016, pp. 779-788, doi: 10.1109/CVPR.2016.91

    2016 doi

  31. [39]

    Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection,

    S. Kim, S. Park, B. Na, and S. Yoon, "Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection," in AAAI Conference on Artificial Intelligence, 2019

    2019

  32. [40]

    Towards Fast and Accurate Object Detection in Bio-Inspired Spiking Neural Networks Through Bayesian Optimization,

    S. Kim, S. Park, B. Na, J. Kim, and S. Yoon, "Towards Fast and Accurate Object Detection in Bio-Inspired Spiking Neural Networks Through Bayesian Optimization," IEEE Access, vol. 9, pp. 2633-2643, 2021, doi: 10.1109/ACCESS.2020.3047071

    2021

  33. [41]

    Trainable Spiking-YOLO for low-latency and high-performance object detection,

    M. Yuan, C. Zhang, Z. Wang, H. Liu, G. Pan, and H. Tang, "Trainable Spiking-YOLO for low-latency and high-performance object detection," Neural networks : the official journal of the International Neural Network Society, vol. 172, p. 106092, 2023

    2023

  34. [42]

    SFOD: Spiking Fusion Object Detector,

    Y. Fan, W. Zhang, C. Liu, M. Li, and W.-C. Lu, "SFOD: Spiking Fusion Object Detector," 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17191-17200, 2024

    2024

  35. [43]

    Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency,

    D. Zhao, G. Shen, Y. Dong, Y. Li, and Y. Zeng, "Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency," ArXiv, vol. abs/2305.14174, 2023

    2023 arXiv

  36. [44]

    Direct Training for Spiking Neural Networks: Faster, Larger, Better,

    Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, "Direct Training for Spiking Neural Networks: Faster, Larger, Better," ArXiv, vol. abs/1809.05793, 2018

    2018 arXiv

  37. [45]

    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, and P. Panda, "Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?," in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Sign...

    2022

  38. [46]

    Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems,

    W. Guo, M. E. Fouda, A. M. Eltawil, and K. N. Salama, "Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems," (in English), Frontiers in Neuroscience, Original Research vol. 15, 2021-March-04 2021, doi: 10.3389/fnins.2021.638474

    2021

  39. [47]

    Conversion of analog to spiking neural networks using sparse temporal coding,

    B. Rueckauer and S. C. Liu, "Conversion of analog to spiking neural networks using sparse temporal coding," in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 27-30 May 2018 2018, pp. 1-5, doi: 10.1109/ISCAS.2018.8351295

    2018

  40. [48]

    T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding,

    S. Park, S. Kim, B. Na, and S. Yoon, "T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding," in 2020 57th ACM/IEEE Design Automation Conference (DAC), 20-24 July 2020 2020, pp. 1-6, doi: 10.1109/DAC18072.2020.9218689

    2020

  41. [49]

    Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks,

    E. O. Neftci, H. Mostafa, and F. Zenke, "Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks," IEEE Signal Processing Magazine, vol. 36, pp. 51-63, 2019

    2019

  42. [50]

    Cooperative behavior in periodically driven noisy integrate-fire models of neuronal dynamics,

    A. R. Bulsara, T. C. Elston, C. R. Doering, S. B. Lowen, and K. Lindenberg, "Cooperative behavior in periodically driven noisy integrate-fire models of neuronal dynamics," Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics, vol. 53 4...

    1996

  43. [51]

    Spiking Deep Networks with LIF Neurons,

    E. Hunsberger and C. Eliasmith, "Spiking Deep Networks with LIF Neurons," ArXiv, vol. abs/1510.08829, 2015

    2015 arXiv

  44. [52]

    SPIKING NEURON MODELS Single Neurons , Populations , Plasticity,

    W. Gerstner, "SPIKING NEURON MODELS Single Neurons , Populations , Plasticity," 2002

    2002

  45. [53]

    Threaten Spiking Neural Networks through Combining Rate and Temporal Information,

    Z. Hao, T. Bu, X. Shi, Z. Huang, Z. Yu, and T. Huang, "Threaten Spiking Neural Networks through Combining Rate and Temporal Information," in International Conference on Learning Representations, 2024

    2024

  46. [2021]

    Available: https://proceedings.mlr.press/v139/li21d.html

    [Online]. Available: https://proceedings.mlr.press/v139/li21d.html

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.