pith. machine review for the scientific record. sign in

arxiv: 2510.14235 · v2 · submitted 2025-10-16 · 💻 cs.NE

Spiking Neural Network Architecture Search: A Survey

Kama Svoboda , Tosiron Adegbija This is my paper

Pith reviewed 2026-05-18 06:56 UTC · model grok-4.3

classification 💻 cs.NE
keywords Spiking Neural NetworksNeural Architecture SearchNeuromorphic ComputingHardware-Software Co-DesignEdge ComputingPower EfficiencyInternet of Things
0
0 comments X

The pith

Spiking neural networks require specialized architecture search methods that incorporate hardware constraints to realize their energy advantages.

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

The paper examines how to search for effective architectures in spiking neural networks, which process data through discrete spikes rather than continuous activations like conventional networks. It highlights that standard neural architecture search techniques from artificial neural networks encounter obstacles when transferred directly, mainly due to SNN training difficulties and the close coupling between model behavior and physical hardware limits. A co-design lens that treats hardware and software together surfaces architectures better suited to power-constrained settings such as edge sensors and IoT nodes. The survey reviews existing SNN-specific search strategies and outlines open directions that could expand the practical reach of neuromorphic systems.

Core claim

Spiking neural network architecture search advances most effectively when viewed through a hardware/software co-design perspective, because the inherent complexity of SNN training and the direct influence of hardware constraints on model performance render unmodified ANN search methods insufficient for unlocking the power-efficiency and real-time capabilities of neuromorphic computing.

What carries the argument

Hardware/software co-design perspective applied to SNN-specific neural architecture search methods that accounts for spike-based computation and hardware-model interplay.

Load-bearing premise

That the training complexities and hardware-model interactions unique to SNNs make direct use of ANN architecture search techniques inadequate without dedicated adaptations.

What would settle it

An experiment showing that standard ANN NAS algorithms produce SNN architectures with equal or better accuracy and energy metrics on target neuromorphic hardware without modification would weaken the case for specialized SNNaS approaches.

Figures

Figures reproduced from arXiv: 2510.14235 by Kama Svoboda, Tosiron Adegbija.

Figure 1
Figure 1. Figure 1: Overview of the Neural Architecture Search (NAS) workflow for Spiking Neural Networks: From defining the search space (e.g., layer configurations, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Neural Architecture Search has three important components: search space, search strategy, and evaluation [73]. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of a cell with four nodes and five possible operations. The [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example of a three-level hierarchy taken from Liu et al. [85]. Level-1 primitive operations [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example of a memory-bank representation taken from Brock et al. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of knowledge distillation [89]. and Auto-Spikformer [17] show that carefully coupling ar￾chitecture search with hardware-specific constraints yields high accuracy alongside notable improvements in energy and resource efficiency. D. Co-Exploring Neural Architecture Search Space and Hard￾ware Design Space Co-exploration simultaneously searches both the neural ar￾chitecture and the hardware design, go… view at source ↗
Figure 7
Figure 7. Figure 7: Evolutionary Algorithm for NAS based on [136]. After initializing [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Learning curve extrapolation illustrating the use of partial training data [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
read the original abstract

This survey paper presents a comprehensive examination of Spiking Neural Network (SNN) architecture search (SNNaS) from a unique hardware/software co-design perspective. SNNs, inspired by biological neurons, have emerged as a promising approach to neuromorphic computing. They offer significant advantages in terms of power efficiency and real-time resource-constrained processing, making them ideal for edge computing and IoT applications. However, designing optimal SNN architectures poses significant challenges, due to their inherent complexity (e.g., with respect to training) and the interplay between hardware constraints and SNN models. We begin by providing an overview of SNNs, emphasizing their operational principles and key distinctions from traditional artificial neural networks (ANNs). We then provide a brief overview of the state of the art in NAS for ANNs, highlighting the challenges of directly applying these approaches to SNNs. We then survey the state of the art in SNN-specific NAS approaches. Finally, we conclude with insights into future research directions for SNN research, emphasizing the potential of hardware/software co-design in unlocking the full capabilities of SNNs. This survey aims to serve as a valuable resource for researchers and practitioners in the field, offering a holistic view of SNNaS and underscoring the importance of a co-design approach to harness the true potential of neuromorphic computing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. This survey examines Spiking Neural Network Architecture Search (SNNaS) from a hardware/software co-design perspective. It opens with SNN operational principles and distinctions from ANNs, reviews the state of ANN NAS and the difficulties of direct transfer to SNNs (training complexity and hardware interplay), surveys existing SNN-specific NAS methods, and closes with future research directions that stress co-design to realize neuromorphic advantages in power-efficient edge computing.

Significance. If the coverage is balanced and up-to-date, the survey would provide a useful organizing framework for an emerging subfield. By foregrounding hardware/software co-design as both motivation and lens, the paper could help researchers avoid purely algorithmic NAS solutions that ignore neuromorphic constraints, thereby supporting more practical deployments in IoT and resource-limited settings.

major comments (1)
  1. [Section on challenges of applying ANN NAS to SNNs] The motivation for dedicated SNN NAS rests on the claim that direct transfer of ANN NAS methods is hindered primarily by SNN training complexity and hardware-model interplay. No quantitative comparison or concrete failure cases (e.g., accuracy drop or search-time overhead when an ANN NAS pipeline is applied to an SNN) are referenced to substantiate the primacy of these factors over other issues such as spike encoding or surrogate-gradient choices.
minor comments (3)
  1. [Survey of SNN-specific NAS approaches] A summary table listing the surveyed SNN NAS methods, their search spaces, optimization objectives, and reported hardware metrics (energy, latency) would make cross-method comparison far easier for readers.
  2. [Abstract and Introduction] The abstract and introduction should state the time window of the literature search and the approximate number of papers reviewed so that readers can gauge completeness.
  3. [Conclusion] Future-directions paragraph would benefit from one or two concrete, falsifiable research questions rather than high-level suggestions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive overall assessment and for highlighting this point on the motivation section. We address the comment below and will revise the manuscript to strengthen the discussion.

read point-by-point responses

  1. Referee: [Section on challenges of applying ANN NAS to SNNs] The motivation for dedicated SNN NAS rests on the claim that direct transfer of ANN NAS methods is hindered primarily by SNN training complexity and hardware-model interplay. No quantitative comparison or concrete failure cases (e.g., accuracy drop or search-time overhead when an ANN NAS pipeline is applied to an SNN) are referenced to substantiate the primacy of these factors over other issues such as spike encoding or surrogate-gradient choices.

    Authors: We agree that the current text would benefit from more concrete support. While the survey draws on the established SNN literature to identify training complexity (arising from non-differentiable spikes and surrogate gradients) and hardware-model interplay as central obstacles, we acknowledge the absence of direct quantitative benchmarks comparing ANN NAS pipelines on SNNs. We will revise the section to cite specific studies that report accuracy degradation, extended search times, or convergence failures when standard ANN NAS techniques are applied without SNN-specific adaptations. We will also briefly clarify the relationship to spike encoding and surrogate-gradient design, noting that these factors are frequently coupled with the training and hardware challenges we emphasize. This change will make the motivation more robust while preserving the survey's scope. revision: yes

Circularity Check

0 steps flagged

No significant circularity; survey is descriptive with no derivations or self-referential reductions

full rationale

This is a literature survey paper whose structure consists of overviews of SNN principles, ANN NAS challenges, existing SNN-specific NAS methods, and future directions. No equations, fitted parameters, predictions, or formal derivations are present. The motivation regarding challenges in applying ANN NAS to SNNs is presented as established background rather than a novel claim requiring internal proof. No load-bearing steps reduce to self-citation chains or definitions by construction; the paper functions as an organizational review of external work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper; it introduces no free parameters, axioms, or invented entities. All content rests on summarizing previously published research in the field.

pith-pipeline@v0.9.0 · 5769 in / 1056 out tokens · 41858 ms · 2026-05-18T06:56:47.073787+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

178 extracted references · 178 canonical work pages · 17 internal anchors

  1. [1]

    Neuromorphic sensory systems,

    S.-C. Liu and T. Delbruck, “Neuromorphic sensory systems,”Current opinion in neurobiology, vol. 20, no. 3, pp. 288–295, 2010

    work page 2010

  2. [2]

    A spiking neural network model of 3d perception for event-based neuromorphic stereo vision systems,

    M. Osswald, S.-H. Ieng, R. Benosman, and G. Indiveri, “A spiking neural network model of 3d perception for event-based neuromorphic stereo vision systems,”Scientific reports, vol. 7, no. 1, p. 40703, 2017

    work page 2017

  3. [3]

    Rethinking the performance comparison between snns and anns,

    L. Deng, Y. Wu, X. Hu, L. Liang, Y. Ding, G. Li, G. Zhao, P. Li, and Y. Xie, “Rethinking the performance comparison between snns and anns,”Neural networks, vol. 121, pp. 294–307, 2020

    work page 2020

  4. [4]

    Brain simu- lation and spiking neural networks,

    Z. Sun, V. Cutsuridis, C. F. Caiafa, and J. Solé-Casals, “Brain simu- lation and spiking neural networks,”Cognitive Computation, vol. 15, no. 4, pp. 1103–1105, 2023

    work page 2023

  5. [5]

    Spiking neural networks: A survey,

    J. D. Nunes, M. Carvalho, D. Carneiro, and J. S. Cardoso, “Spiking neural networks: A survey,”IEEE Access, vol. 10, pp. 60738–60764, 2022

    work page 2022

  6. [6]

    Deep learning with spiking neurons: oppor- tunities and challenges,

    M. Pfeiffer and T. Pfeil, “Deep learning with spiking neurons: oppor- tunities and challenges,”Frontiers in neuroscience, vol. 12, p. 409662, 2018

    work page 2018

  7. [7]

    Autosnn: Towards energy-efficient spiking neural networks,

    B. Na, J. Mok, S. Park, D. Lee, H. Choe, and S. Yoon, “Autosnn: Towards energy-efficient spiking neural networks,” inInternational Conference on Machine Learning, pp. 16253–16269, PMLR, 2022

    work page 2022

  8. [8]

    Neural architec- ture search for spiking neural networks,

    Y. Kim, Y. Li, H. Park, Y. Venkatesha, and P. Panda, “Neural architec- ture search for spiking neural networks,” inEuropean Conference on Computer Vision, pp. 36–56, Springer, 2022

    work page 2022

  9. [9]

    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 net- works, vol. 111, pp. 47–63, 2019

    work page 2019

  10. [10]

    Very Deep Convolutional Networks for Large-Scale Image Recognition

    K. Simonyan, “Very deep convolutional networks for large-scale image recognition,”arXiv preprint arXiv:1409.1556, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  11. [11]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,”inProceedingsoftheIEEEconferenceoncomputervision and pattern recognition, pp. 770–778, 2016

    work page 2016

  12. [12]

    Spikenas: A fast memory-aware neu- ral architecture search framework for spiking neural network systems,

    R. V. W. Putra and M. Shafique, “Spikenas: A fast memory-aware neu- ral architecture search framework for spiking neural network systems,” arXiv preprint arXiv:2402.11322, 2024

    work page arXiv 2024

  13. [13]

    Surrogate-assisted evolutionary search of spiking neural architectures in liquid state machines,

    Y. Zhou, Y. Jin, and J. Ding, “Surrogate-assisted evolutionary search of spiking neural architectures in liquid state machines,”Neurocomputing, vol. 406, pp. 12–23, 2020

    work page 2020

  14. [14]

    Using nas to improve accuracy of snns,

    J. Nijenhuis, “Using nas to improve accuracy of snns,” 2021. 13

    work page 2021

  15. [15]

    Differentiable hierarchical and surrogate gradient search for spiking neural networks,

    K. Che, L. Leng, K. Zhang, J. Zhang, Q. Meng, J. Cheng, Q. Guo, and J. Liao, “Differentiable hierarchical and surrogate gradient search for spiking neural networks,”Advances in Neural Information Processing Systems, vol. 35, pp. 24975–24990, 2022

    work page 2022

  16. [16]

    Gaq-snn: A genetic algorithm based quantization framework for deep spiking neural networks,

    D.-A. Nguyen, X.-T. Tran, and F. Iacopi, “Gaq-snn: A genetic algorithm based quantization framework for deep spiking neural networks,” in 2022 International Conference on IC Design and Technology (ICI- CDT), pp. 93–96, 2022

    work page 2022

  17. [17]

    Auto-spikformer: Spikformer architecture search,

    K. Che, Z. Zhou, Z. Ma, W. Fang, Y. Chen, S. Shen, L. Yuan, and Y. Tian, “Auto-spikformer: Spikformer architecture search,”arXiv preprint arXiv:2306.00807, 2023

    work page arXiv 2023

  18. [18]

    Brain-inspired neural circuit evolution for spiking neural networks,

    G. Shen, D. Zhao, Y. Dong, and Y. Zeng, “Brain-inspired neural circuit evolution for spiking neural networks,”Proceedings of the National Academy of Sciences, vol. 120, no. 39, p. e2218173120, 2023

    work page 2023

  19. [19]

    Efficient spiking neural archi- tecture search with mixed neuron models and variable thresholds,

    Z. Xie, Z. Liu, P. Chen, and J. Zhang, “Efficient spiking neural archi- tecture search with mixed neuron models and variable thresholds,” in International Conference on Neural Information Processing, pp. 466– 481, Springer, 2023

    work page 2023

  20. [20]

    Unleashing energy-efficiency: Neural architecture search without training for spiking neural networks on loihi chip,

    S. Liu and Y. Yi, “Unleashing energy-efficiency: Neural architecture search without training for spiking neural networks on loihi chip,” in2024 25th International Symposium on Quality Electronic Design (ISQED), pp. 1–7, IEEE, 2024

    work page 2024

  21. [21]

    A methodology for improving accuracy of embedded spiking neural networks through kernel size scaling,

    R. V. W. Putra and M. Shafique, “A methodology for improving accuracy of embedded spiking neural networks through kernel size scaling,”arXiv preprint arXiv:2404.01685, 2024

    work page arXiv 2024

  22. [22]

    Hasnas: A hardware- aware spiking neural architecture search framework for neuromorphic compute-in-memory systems,

    R. Vidya Wicaksana Putra and M. Shafique, “Hasnas: A hardware- aware spiking neural architecture search framework for neuromorphic compute-in-memory systems,”arXiv e-prints, pp. arXiv–2407, 2024

    work page 2024

  23. [23]

    Brain-inspired evolution- ary architectures for spiking neural networks,

    W. Pan, F. Zhao, Z. Zhao, and Y. Zeng, “Brain-inspired evolution- ary architectures for spiking neural networks,”IEEE Transactions on Artificial Intelligence, 2024

    work page 2024

  24. [24]

    Efficient spiking neural network design via neural architecture search,

    J. Yan, Q. Liu, M. Zhang, L. Feng, D. Ma, H. Li, and G. Pan, “Efficient spiking neural network design via neural architecture search,”Neural Networks, p. 106172, 2024

    work page 2024

  25. [25]

    Few-shot class incremental learning with attention-aware self-adaptive prompt

    Q. Liu, J. Yan, M. Zhang, G. Pan, and H. Li, “Lite-snn: Design- ing lightweight and efficient spiking neural network through spatial- temporal compressive network search and joint optimization,”arXiv preprint arXiv:2401.14652, 2024

    work page arXiv 2024

  26. [26]

    Spikeex- plorer: Hardware-oriented design space exploration for spiking neural networks on fpga,

    D. Padovano, A. Carpegna, A. Savino, and S. Di Carlo, “Spikeex- plorer: Hardware-oriented design space exploration for spiking neural networks on fpga,”Electronics, vol. 13, no. 9, p. 1744, 2024

    work page 2024

  27. [27]

    Despine: Nas generated deep evolutionary adaptive spiking network for low power edge computing applications,

    B. Ajay, M. Rao,et al., “Despine: Nas generated deep evolutionary adaptive spiking network for low power edge computing applications,” in2024 25th International Symposium on Quality Electronic Design (ISQED), pp. 1–8, IEEE, 2024

    work page 2024

  28. [28]

    Autost: Training-free neural architecture search for spiking transformers,

    Z. Wang, Q. Zhao, J. Cui, X. Liu, and D. Xu, “Autost: Training-free neural architecture search for spiking transformers,” inICASSP 2024- 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3455–3459, IEEE, 2024

    work page 2024

  29. [29]

    Hardware/software codesign: The past, the present, and predicting the future,

    J. Teich, “Hardware/software codesign: The past, the present, and predicting the future,”Proceedings of the IEEE, vol. 100, no. Special Centennial Issue, pp. 1411–1430, 2012

    work page 2012

  30. [30]

    Gerstner and W

    W. Gerstner and W. M. Kistler,Spiking neuron models: Single neurons, populations, plasticity. Cambridge university press, 2002

    work page 2002

  31. [31]

    Spiking neural networks,

    S. Ghosh-Dastidar and H. Adeli, “Spiking neural networks,”Interna- tional journal of neural systems, vol. 19 4, pp. 295–308, 2009

    work page 2009

  32. [32]

    Koch and I

    C. Koch and I. Segev,Methods in neuronal modeling: from ions to networks. MIT press, 1998

    work page 1998

  33. [33]

    A quantitative description of membrane current and its application to conduction and excitation in nerve,

    A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,”The Journal of physiology, vol. 117, no. 4, p. 500, 1952

    work page 1952

  34. [34]

    Simple model of spiking neurons,

    E. M. Izhikevich, “Simple model of spiking neurons,”IEEE Transac- tions on neural networks, vol. 14, no. 6, pp. 1569–1572, 2003

    work page 2003

  35. [35]

    A review of learning in biologically plausible spiking neural networks,

    A. Taherkhani, A. Belatreche, Y. Li, G. Cosma, L. P. Maguire, and T. M. McGinnity, “A review of learning in biologically plausible spiking neural networks,”Neural Networks, vol. 122, pp. 253–272, 2020

    work page 2020

  36. [36]

    A comparative study on spiking neural network encoding schema: implemented with cloud computing,

    A. Almomani, M. Alauthman, M. Alweshah, O. Dorgham, and F. Al- balas, “A comparative study on spiking neural network encoding schema: implemented with cloud computing,”Cluster Computing, vol. 22, pp. 419–433, 2019

    work page 2019

  37. [37]

    Sparsity- aware hardware-software co-design of spiking neural networks: An overview,

    I. Aliyev, K. Svoboda, T. Adegbija, and J.-M. Fellous, “Sparsity- aware hardware-software co-design of spiking neural networks: An overview,” in2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pp. 413–420, IEEE, 2024

    work page 2024

  38. [38]

    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,”Frontiers in Neuroscience, vol. 15, p. 638474, 2021

    work page 2021

  39. [39]

    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,” in2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1–6, IEEE, 2020

    work page 2020

  40. [40]

    Deep neural networks with weighted spikes,

    J. Kim, H. Kim, S. Huh, J. Lee, and K. Choi, “Deep neural networks with weighted spikes,”Neurocomputing, vol. 311, pp. 373–386, 2018

    work page 2018

  41. [41]

    Bursts as a unit of neural information: selective communication via resonance,

    E. M. Izhikevich, N. S. Desai, E. C. Walcott, and F. C. Hoppensteadt, “Bursts as a unit of neural information: selective communication via resonance,”Trends in neurosciences, vol. 26, no. 3, pp. 161–167, 2003

    work page 2003

  42. [42]

    Spike based information processing in spiking neural networks,

    S. Sheik, “Spike based information processing in spiking neural networks,” inProceedings of the 4th International Conference on Applications in Nonlinear Dynamics (ICAND 2016) 5, pp. 177–188, Springer, 2017

    work page 2016

  43. [43]

    Temporal pattern coding in deep spiking neural networks,

    B. Rueckauer and S.-C. Liu, “Temporal pattern coding in deep spiking neural networks,” in2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, IEEE, 2021

    work page 2021

  44. [44]

    Synaptic plasticity: taming the beast,

    L. F. Abbott and S. B. Nelson, “Synaptic plasticity: taming the beast,” Nature neuroscience, vol. 3, no. 11, pp. 1178–1183, 2000

    work page 2000

  45. [45]

    Review of deep learning algorithms and architectures,

    A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,”IEEE access, vol. 7, pp. 53040–53065, 2019

    work page 2019

  46. [46]

    Unsupervised learning of visual fea- tures through spike timing dependent plasticity,

    T. Masquelier and S. J. Thorpe, “Unsupervised learning of visual fea- tures through spike timing dependent plasticity,”PLoS computational biology, vol. 3, no. 2, p. e31, 2007

    work page 2007

  47. [47]

    Categorization and decision-making in a neurobiologically plausible spiking network using a stdp-like learning rule,

    M. Beyeler, N. D. Dutt, and J. L. Krichmar, “Categorization and decision-making in a neurobiologically plausible spiking network using a stdp-like learning rule,”Neural Networks, vol. 48, pp. 109–124, 2013

    work page 2013

  48. [48]

    Acquisition of visual features through probabilistic spike-timing-dependent plasticity,

    A. Tavanaei, T. Masquelier, and A. S. Maida, “Acquisition of visual features through probabilistic spike-timing-dependent plasticity,” in 2016 International Joint Conference on Neural Networks (IJCNN), pp. 307–314, IEEE, 2016

    work page 2016

  49. [49]

    To spike, or when to spike?,

    R. Gütig, “To spike, or when to spike?,”Current opinion in neurobi- ology, vol. 25, pp. 134–139, 2014

    work page 2014

  50. [50]

    Learning precisely timed spikes,

    R.-M. Memmesheimer, R. Rubin, B. P. Ölveczky, and H. Sompolinsky, “Learning precisely timed spikes,”Neuron, vol. 82, no. 4, pp. 925–938, 2014

    work page 2014

  51. [51]

    Normad-normalized approximate de- scent based supervised learning rule for spiking neurons,

    N. Anwani and B. Rajendran, “Normad-normalized approximate de- scent based supervised learning rule for spiking neurons,” in2015 international joint conference on neural networks (IJCNN), pp. 1–8, IEEE, 2015

    work page 2015

  52. [52]

    Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network,

    A. Gilra and W. Gerstner, “Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network,”Elife, vol. 6, p. e28295, 2017

    work page 2017

  53. [53]

    Supervised learning in spiking neural networks with force training,

    W. Nicola and C. Clopath, “Supervised learning in spiking neural networks with force training,”Nature communications, vol. 8, no. 1, p. 2208, 2017

    work page 2017

  54. [54]

    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, no. 6, pp. 51–63, 2019

    work page 2019

  55. [55]

    Gradient descent for spiking neural networks,

    D. Huh and T. J. Sejnowski, “Gradient descent for spiking neural networks,”Advances in neural information processing systems, vol. 31, 2018

    work page 2018

  56. [56]

    Training deep spiking neural networks using backpropagation,

    J. H. Lee, T. Delbruck, and M. Pfeiffer, “Training deep spiking neural networks using backpropagation,”Frontiers in neuroscience, vol. 10, p. 508, 2016

    work page 2016

  57. [57]

    Spikeprop: backpropaga- tion for networks of spiking neurons.,

    S. M. Bohte, J. N. Kok, and J. A. La Poutré, “Spikeprop: backpropaga- tion for networks of spiking neurons.,” inESANN, vol. 48, pp. 419–424, Bruges, 2000

    work page 2000

  58. [58]

    Spatio-temporal back- propagation for training high-performance spiking neural networks,

    Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, “Spatio-temporal back- propagation for training high-performance spiking neural networks,” Frontiers in neuroscience, vol. 12, p. 331, 2018

    work page 2018

  59. [59]

    Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization,

    N. Rathi and K. Roy, “Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization,” IEEE Transactions on Neural Networks and Learning Systems, 2021

    work page 2021

  60. [60]

    Fine-tuning surrogate gradient learning for optimal hardware performance in spiking neural networks,

    I. Aliyev and T. Adegbija, “Fine-tuning surrogate gradient learning for optimal hardware performance in spiking neural networks,” in 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1–2, IEEE, 2024

    work page 2024

  61. [61]

    Signedneuronwithmemory: Towards simple, accurate and high-efficient ann-snn conversion.,

    Y.Wang,M.Zhang,Y.Chen,andH.Qu,“Signedneuronwithmemory: Towards simple, accurate and high-efficient ann-snn conversion.,” in IJCAI, pp. 2501–2508, 2022

    work page 2022

  62. [62]

    Con- version of continuous-valued deep networks to efficient event-driven 14 networks for image classification,

    B. Rueckauer, I.-A. Lungu, Y. Hu, M. Pfeiffer, and S.-C. Liu, “Con- version of continuous-valued deep networks to efficient event-driven 14 networks for image classification,”Frontiers in neuroscience, vol. 11, p. 682, 2017

    work page 2017

  63. [63]

    Optimal conversion of conventional artifi- cial neural networks to spiking neural networks,

    S. Deng and S. Gu, “Optimal conversion of conventional artifi- cial neural networks to spiking neural networks,”arXiv preprint arXiv:2103.00476, 2021

    work page arXiv 2021

  64. [64]

    Tcl: an ann-to-snn conversion with trainable clipping layers,

    N.-D. Ho and I.-J. Chang, “Tcl: an ann-to-snn conversion with trainable clipping layers,” in2021 58th ACM/IEEE Design Automation Confer- ence (DAC), pp. 793–798, 2021

    work page 2021

  65. [65]

    Synaptic modifications in cultured hip- pocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type,

    G.-q. Bi and M.-m. Poo, “Synaptic modifications in cultured hip- pocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type,”Journal of neuroscience, vol. 18, no. 24, pp. 10464–10472, 1998

    work page 1998

  66. [66]

    A critical survey of stdp in spiking neural networks for pattern recognition,

    A. Vigneron and J. Martinet, “A critical survey of stdp in spiking neural networks for pattern recognition,” in2020 international joint conference on neural networks (ijcnn), pp. 1–9, IEEE, 2020

    work page 2020

  67. [67]

    Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connec- tions,stochasticsoftmax,andhybridization,

    P. Panda, S. A. Aketi, and K. Roy, “Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connec- tions,stochasticsoftmax,andhybridization,”FrontiersinNeuroscience, vol. 14, p. 653, 2020

    work page 2020

  68. [68]

    Sstdp: Supervised spike timing dependent plasticity for efficient spiking neural network training,

    F. Liu, W. Zhao, Y. Chen, Z. Wang, T. Yang, and L. Jiang, “Sstdp: Supervised spike timing dependent plasticity for efficient spiking neural network training,”Frontiers in Neuroscience, vol. 15, p. 756876, 2021

    work page 2021

  69. [69]

    The step size impact on the computational cost of spiking neuron simulation,

    S. Valadez-Godínez, H. Sossa, and R. Santiago-Montero, “The step size impact on the computational cost of spiking neuron simulation,” in2017 Computing Conference, pp. 722–728, IEEE, 2017

    work page 2017

  70. [70]

    The heidelberg spiking data sets for the systematic evaluation of spiking neural net- works,

    B. Cramer, Y. Stradmann, J. Schemmel, and F. Zenke, “The heidelberg spiking data sets for the systematic evaluation of spiking neural net- works,”IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2744–2757, 2020

    work page 2020

  71. [71]

    Spiking neural networks hardware im- plementations and challenges: A survey,

    M. Bouvier, A. Valentian, T. Mesquida, F. Rummens, M. Reyboz, E. Vianello, and E. Beigne, “Spiking neural networks hardware im- plementations and challenges: A survey,”ACM Journal on Emerging Technologies in Computing Systems (JETC), vol. 15, no. 2, pp. 1–35, 2019

    work page 2019

  72. [72]

    Evolving neural networks through augmenting topologies,

    K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,”Evolutionary computation, vol. 10, no. 2, pp. 99–127, 2002

    work page 2002

  73. [73]

    Neural architecture search: A survey,

    T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,”The Journal of Machine Learning Research, vol. 20, no. 1, pp. 1997–2017, 2019

    work page 1997

  74. [74]

    Surrogate module learning: Reduce the gradient error accumulation in training spiking neural networks,

    S. Deng, H. Lin, Y. Li, and S. Gu, “Surrogate module learning: Reduce the gradient error accumulation in training spiking neural networks,” inInternational Conference on Machine Learning, pp. 7645–7657, PMLR, 2023

    work page 2023

  75. [75]

    Advancements in algorithms and neuromorphic hardware for spiking neural networks,

    A. Javanshir, T. T. Nguyen, M. P. Mahmud, and A. Z. Kouzani, “Advancements in algorithms and neuromorphic hardware for spiking neural networks,”Neural Computation, vol. 34, no. 6, pp. 1289–1328, 2022

    work page 2022

  76. [76]

    Efficient neural architecture search via parameters sharing,

    H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” inInternational conference on machine learning, pp. 4095–4104, PMLR, 2018

    work page 2018

  77. [77]

    Anas: Asynchronous neuromorphic hardware architecture search based on a system-level simulator,

    J. Zhang, J. Zhang, D. Huo, and H. Chen, “Anas: Asynchronous neuromorphic hardware architecture search based on a system-level simulator,” in2023 60th ACM/IEEE Design Automation Conference (DAC), pp. 1–6, IEEE, 2023

    work page 2023

  78. [78]

    Neural Architecture Search with Reinforcement Learning

    B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,”arXiv preprint arXiv:1611.01578, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  79. [79]

    Designing Neural Network Architectures using Reinforcement Learning

    B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing neural network architectures using reinforcement learning,”arXiv preprint arXiv:1611.02167, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  80. [80]

    Nas-bench-201: Extending the scope of repro- ducible neural architecture search,

    X. Dong and Y. Yang, “Nas-bench-201: Extending the scope of repro- ducible neural architecture search,”arXiv preprint arXiv:2001.00326, 2020

    work page arXiv 2001

Showing first 80 references.