{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:47:41Z","timestamp":1792601261966,"version":"4.5.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-77779-8","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T18:24:01Z","timestamp":1735323841000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Exploring spiking neural networks for deep reinforcement learning in robotic tasks"],"prefix":"10.1038","volume":"14","author":[{"given":"Luca","family":"Zanatta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Barchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Manoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Tolu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Bartolini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Acquaviva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"77779_CR1","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659\u20131671 (1997).","journal-title":"Neural Netw."},{"key":"77779_CR2","unstructured":"Keng, W.\u00a0L. & Graesser, L. Slm lab. https:\/\/github.com\/kengz\/SLM-Lab (2017)."},{"key":"77779_CR3","doi-asserted-by":"crossref","unstructured":"Hwangbo, J. et al. Learning agile and dynamic motor skills for legged robots. Sci. Robot. 4, eaau5872 (2019).","DOI":"10.1126\/scirobotics.aau5872"},{"key":"77779_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17236-y","volume":"11","author":"G Bellec","year":"2020","unstructured":"Bellec, G. et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11, 1\u201315 (2020).","journal-title":"Nat. Commun."},{"key":"77779_CR5","unstructured":"Tang, G., Kumar, N., Yoo, R. & Michmizos, K. Deep reinforcement learning with population-coded spiking neural network for continuous control. In Conference on Robot Learning, 2016\u20132029 (PMLR, 2021)."},{"key":"77779_CR6","doi-asserted-by":"crossref","unstructured":"Chen, D., Peng, P., Huang, T. & Tian, Y. Fully spiking actor network with intralayer connections for reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. (2024).","DOI":"10.1109\/TNNLS.2024.3352653"},{"key":"77779_CR7","doi-asserted-by":"crossref","unstructured":"Shah, S., Dey, D., Lovett, C. & Kapoor, A. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and Service Robotics (2017). arXiv:1705.05065.","DOI":"10.1007\/978-3-319-67361-5_40"},{"key":"77779_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126885","volume":"562","author":"L Zanatta","year":"2023","unstructured":"Zanatta, L. et al. Directly-trained spiking neural networks for deep reinforcement learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator. Neurocomputing 562, 126885 (2023).","journal-title":"Neurocomputing"},{"key":"77779_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, Q., Zhang, T. & Xu, B. Biologically-plausible topology improved spiking actor network for efficient deep reinforcement learning. arXiv preprint[SPACE]arXiv:2403.20163 (2024).","DOI":"10.3389\/fnins.2024.1325062"},{"key":"77779_CR10","doi-asserted-by":"crossref","unstructured":"Salvatore, N., Mian, S., Abidi, C. & George, A.\u00a0D. A neuro-inspired approach to intelligent collision avoidance and navigation. In 2020 AIAA\/IEEE 39th Digital Avionics Systems Conference (DASC), 1\u20139 (IEEE, 2020).","DOI":"10.1109\/DASC50938.2020.9256492"},{"key":"77779_CR11","first-page":"59","volume":"36","author":"D Zhang","year":"2022","unstructured":"Zhang, D., Zhang, T., Jia, S. & Xu, B. Multi-sacle dynamic coding improved spiking actor network for reinforcement learning. Proc. AAAI Conf. Artif. Intell. 36, 59\u201367 (2022).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"77779_CR12","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.neunet.2019.08.009","volume":"120","author":"D Patel","year":"2019","unstructured":"Patel, D., Hazan, H., Saunders, D. J., Siegelmann, H. T. & Kozma, R. Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari breakout game. Neural Netw. 120, 108\u2013115 (2019).","journal-title":"Neural Netw."},{"key":"77779_CR13","doi-asserted-by":"publisher","first-page":"150345","DOI":"10.1109\/ACCESS.2021.3126311","volume":"9","author":"K Naya","year":"2021","unstructured":"Naya, K., Kutsuzawa, K., Owaki, D. & Hayashibe, M. Spiking neural network discovers energy-efficient hexapod motion in deep reinforcement learning. IEEE Access 9, 150345\u2013150354 (2021).","journal-title":"IEEE Access"},{"key":"77779_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3390\/machines11020162","volume":"11","author":"KM Oikonomou","year":"2023","unstructured":"Oikonomou, K. M., Kansizoglou, I. & Gasteratos, A. A hybrid spiking neural network reinforcement learning agent for energy-efficient object manipulation. Machines 11, 162 (2023).","journal-title":"Machines"},{"key":"77779_CR15","unstructured":"Chen, D., Peng, P., Huang, T. & Tian, Y. Deep reinforcement learning with spiking q-learning. arXiv preprint[SPACE]arXiv:2201.09754 (2022)."},{"key":"77779_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.953368","volume":"16","author":"Y Sun","year":"2022","unstructured":"Sun, Y., Zeng, Y. & Li, Y. Solving the spike feature information vanishing problem in spiking deep q network with potential based normalization. Front. Neurosci. 16, 953368 (2022).","journal-title":"Front. Neurosci."},{"key":"77779_CR17","doi-asserted-by":"publisher","unstructured":"Tang, G., Kumar, N. & Michmizos, K.\u00a0P. Reinforcement co-learning of deep and spiking neural networks for energy-efficient mapless navigation with neuromorphic hardware. In 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 6090\u20136097. https:\/\/doi.org\/10.1109\/IROS45743.2020.9340948 (2020).","DOI":"10.1109\/IROS45743.2020.9340948"},{"key":"77779_CR18","doi-asserted-by":"publisher","unstructured":"Mahadevuni, A. & Li, P. Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN), 2243\u20132250, https:\/\/doi.org\/10.1109\/IJCNN.2017.7966127 (2017).","DOI":"10.1109\/IJCNN.2017.7966127"},{"key":"77779_CR19","doi-asserted-by":"crossref","unstructured":"Jiang, J. et al. Neuro-planner: A 3d visual navigation method for mav with depth camera based on neuromorphic reinforcement learning. IEEE Trans. Veh. Technol. (2023).","DOI":"10.1109\/TVT.2023.3278097"},{"key":"77779_CR20","doi-asserted-by":"crossref","unstructured":"Walravens, M., Verreyken, E. & Steckel, J. Spiking neural network implementation on fpga for robotic behaviour. In Advances on P2P, Parallel, Grid, Cloud and Internet Computing: Proceedings of the 14th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2019) 14, 694\u2013703 (Springer, 2020).","DOI":"10.1007\/978-3-030-33509-0_65"},{"key":"77779_CR21","unstructured":"Hu, Y., Tang, H. & Pan, G. Spiking deep residual networks. IEEE Trans. Neural Netw. Learn. Syst. (2021)."},{"key":"77779_CR22","doi-asserted-by":"crossref","unstructured":"Kundu, S., Datta, G., Pedram, M. & Beerel, P.\u00a0A. Spike-thrift: Towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 3953\u20133962 (2021).","DOI":"10.1109\/WACV48630.2021.00400"},{"key":"77779_CR23","unstructured":"Wang, Z. et al. Towards lossless ANN-SNN conversion under ultra-low latency with dual-phase optimization. arXiv preprint[SPACE]arXiv:2205.07473 (2022)."},{"key":"77779_CR24","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3389\/fnins.2018.00331","volume":"12","author":"Y Wu","year":"2018","unstructured":"Wu, Y., Deng, L., Li, G., Zhu, J. & Shi, L. Spatio-temporal backpropagation for training high-performance spiking neural networks. Front. Neurosci. 12, 331 (2018).","journal-title":"Front. Neurosci."},{"key":"77779_CR25","first-page":"21056","volume":"34","author":"W Fang","year":"2021","unstructured":"Fang, W. et al. Deep residual learning in spiking neural networks. Adv. Neural. Inf. Process. Syst. 34, 21056\u201321069 (2021).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"77779_CR26","unstructured":"Deng, S., Li, Y., Zhang, S. & Gu, S. Temporal efficient training of spiking neural network via gradient re-weighting. arXiv preprint[SPACE]arXiv:2202.11946 (2022)."},{"key":"77779_CR27","doi-asserted-by":"crossref","unstructured":"Feng, L., Liu, Q., Tang, H., Ma, D. & Pan, G. Multi-level firing with spiking ds-resnet: Enabling better and deeper directly-trained spiking neural networks. arXiv preprint[SPACE]arXiv:2210.06386 (2022).","DOI":"10.24963\/ijcai.2022\/343"},{"key":"77779_CR28","doi-asserted-by":"crossref","unstructured":"Guo, Y. et al. Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 326\u2013335 (2022).","DOI":"10.1109\/CVPR52688.2022.00042"},{"key":"77779_CR29","first-page":"11062","volume":"35","author":"H Zheng","year":"2021","unstructured":"Zheng, H., Wu, Y., Deng, L., Hu, Y. & Li, G. Going deeper with directly-trained larger spiking neural networks. Proc. AAAI Conf. Artif. Intell. 35, 11062\u201311070 (2021).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"77779_CR30","first-page":"11795","volume":"34","author":"N Perez-Nieves","year":"2021","unstructured":"Perez-Nieves, N. & Goodman, D. Sparse spiking gradient descent. Adv. Neural. Inf. Process. Syst. 34, 11795\u201311808 (2021).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"77779_CR31","unstructured":"Musa, A., Zanatta, L., Barchi, F., Andrea, B. & Andrea, A. A method for accelerated simulations of reinforcement learning tasks of uavs in airsim. In SIMUL 22 (2022)."},{"key":"77779_CR32","unstructured":"Makoviychuk, V. et al. Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint[SPACE]arXiv:2108.10470 (2021)."},{"key":"77779_CR33","unstructured":"Zanatta, L. Spikegym. https:\/\/gitlab.com\/ecs-lab\/spikegym (2024)."},{"key":"77779_CR34","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. arXiv preprint[SPACE]arXiv:1707.06347 (2017)."},{"key":"77779_CR35","first-page":"1","volume":"24","author":"A Serrano-Mu\u00f1oz","year":"2023","unstructured":"Serrano-Mu\u00f1oz, A., Chrysostomou, D., B\u00f8gh, S. & Arana-Arexolaleiba, N. skrl: Modular and flexible library for reinforcement learning. J. Mach. Learn. Res. 24, 1\u20139 (2023).","journal-title":"J. Mach. Learn. Res."},{"key":"77779_CR36","doi-asserted-by":"crossref","unstructured":"Lian, S. et al. Learnable surrogate gradient for direct training spiking neural networks. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, 3002\u20133010 (2023).","DOI":"10.24963\/ijcai.2023\/335"},{"key":"77779_CR37","doi-asserted-by":"publisher","unstructured":"Todorov, E., Erez, T. & Tassa, Y. Mujoco: A physics engine for model-based control. In 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, 5026\u20135033. https:\/\/doi.org\/10.1109\/IROS.2012.6386109 (IEEE, 2012).","DOI":"10.1109\/IROS.2012.6386109"},{"key":"77779_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003024","volume":"9","author":"N Fr\u00e9maux","year":"2013","unstructured":"Fr\u00e9maux, N., Sprekeler, H. & Gerstner, W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput. Biol. 9, e1003024 (2013).","journal-title":"PLoS Comput. Biol."},{"key":"77779_CR39","unstructured":"Tieck, J. C.\u00a0V. et al. Learning target reaching motions with a robotic arm using dopamine modulated stdp. In 18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (2019)."},{"key":"77779_CR40","doi-asserted-by":"crossref","unstructured":"Bing, Z. et al. End to end learning of spiking neural network based on r-stdp for a lane keeping vehicle. In 2018 IEEE International Conference on Robotics and Automation (ICRA), 4725\u20134732 (IEEE, 2018).","DOI":"10.1109\/ICRA.2018.8460482"},{"key":"77779_CR41","unstructured":"Brockman, G. et al. Openai gym (2016). arXiv:1606.01540."},{"key":"77779_CR42","unstructured":"Lu, J., Hagenaars, J.\u00a0J. & de Croon, G.\u00a0C. Evolving-to-learn reinforcement learning tasks with spiking neural networks. arXiv preprint[SPACE]arXiv:2202.12322 (2022)."},{"key":"77779_CR43","doi-asserted-by":"publisher","first-page":"310","DOI":"10.3390\/electronics12020310","volume":"12","author":"Y Liu","year":"2023","unstructured":"Liu, Y. & Pan, W. Spiking neural-networks-based data-driven control. Electronics 12, 310 (2023).","journal-title":"Electronics"},{"key":"77779_CR44","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3389\/fninf.2018.00089","volume":"12","author":"H Hazan","year":"2018","unstructured":"Hazan, H. et al. Bindsnet: A machine learning-oriented spiking neural networks library in python. Front. Neuroinform. 12, 89 (2018).","journal-title":"Front. Neuroinform."},{"key":"77779_CR45","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1162\/neco_a_01238","volume":"31","author":"M Yuan","year":"2019","unstructured":"Yuan, M., Wu, X., Yan, R. & Tang, H. Reinforcement learning in spiking neural networks with stochastic and deterministic synapses. Neural Comput. 31, 2368\u20132389 (2019).","journal-title":"Neural Comput."},{"key":"77779_CR46","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1016\/j.neunet.2021.09.010","volume":"144","author":"SF Chevtchenko","year":"2021","unstructured":"Chevtchenko, S. F. & Ludermir, T. B. Combining stdp and binary networks for reinforcement learning from images and sparse rewards. Neural Netw. 144, 496\u2013506 (2021).","journal-title":"Neural Netw."},{"key":"77779_CR47","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3389\/fnins.2020.00088","volume":"14","author":"SA Lobov","year":"2020","unstructured":"Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A. & Kazantsev, V. B. Spatial properties of stdp in a self-learning spiking neural network enable controlling a mobile robot. Front. Neurosci. 14, 88 (2020).","journal-title":"Front. Neurosci."},{"key":"77779_CR48","doi-asserted-by":"crossref","unstructured":"Shim, M.\u00a0S. & Li, P. Biologically inspired reinforcement learning for mobile robot collision avoidance. In 2017 International Joint Conference on Neural Networks (IJCNN), 3098\u20133105 (IEEE, 2017).","DOI":"10.1109\/IJCNN.2017.7966242"},{"key":"77779_CR49","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fncom.2018.00042","volume":"12","author":"L Paulun","year":"2018","unstructured":"Paulun, L., Wendt, A. & Kasabov, N. A retinotopic spiking neural network system for accurate recognition of moving objects using neucube and dynamic vision sensors. Front. Comput. Neurosci. 12, 42 (2018).","journal-title":"Front. Comput. Neurosci."},{"key":"77779_CR50","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"PU Diehl","year":"2015","unstructured":"Diehl, P. U. & Cook, M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015).","journal-title":"Front. Comput. Neurosci."},{"key":"77779_CR51","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3389\/fnins.2017.00682","volume":"11","author":"B Rueckauer","year":"2017","unstructured":"Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M. & Liu, S.-C. Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017).","journal-title":"Front. Neurosci."},{"key":"77779_CR52","doi-asserted-by":"crossref","unstructured":"Tan, W., Patel, D. & Kozma, R. Strategy and benchmark for converting deep q-networks to event-driven spiking neural networks. arXiv preprint[SPACE]arXiv:2009.14456 (2020).","DOI":"10.1609\/aaai.v35i11.17180"},{"key":"77779_CR53","doi-asserted-by":"publisher","DOI":"10.1088\/1748-3190\/ac290c","volume":"16","author":"A Shalumov","year":"2021","unstructured":"Shalumov, A., Halaly, R. & Tsur, E. E. Lidar-driven spiking neural network for collision avoidance in autonomous driving. Bioinspir. Biomimet. 16, 066016 (2021).","journal-title":"Bioinspir. Biomimet."},{"key":"77779_CR54","doi-asserted-by":"crossref","unstructured":"Salvatore, N., Mian, S., Abidi, C. & George, A.\u00a0D. A neuro-inspired approach to intelligent collision avoidance and navigation. In 2020 AIAA\/IEEE 39th Digital Avionics Systems Conference (DASC), 1\u20139 (IEEE, 2020).","DOI":"10.1109\/DASC50938.2020.9256492"},{"key":"77779_CR55","first-page":"1","volume":"2021","author":"M Akl","year":"2021","unstructured":"Akl, M., Sandamirskaya, Y., Walter, F. & Knoll, A. Porting deep spiking q-networks to neuromorphic chip loihi. Int. Conf. Neuromorphic Syst. 2021, 1\u20137 (2021).","journal-title":"Int. Conf. Neuromorphic Syst."},{"key":"77779_CR56","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci, E. O., Mostafa, H. & Zenke, F. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36, 51\u201363 (2019).","journal-title":"IEEE Signal Process. Mag."},{"key":"77779_CR57","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies, M. et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82\u201399 (2018).","journal-title":"IEEE Micro"},{"key":"77779_CR58","doi-asserted-by":"crossref","unstructured":"Yang, S. & Chen, B. Snib: improving spike-based machine learning using nonlinear information bottleneck. IEEE Trans. Syst. Man Cybern. Syst. (2023).","DOI":"10.1109\/TSMC.2023.3300318"},{"key":"77779_CR59","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, H. & Chen, B. Sibols: robust and energy-efficient learning for spike-based machine intelligence in information bottleneck framework. IEEE Trans. Cogn. Dev. Syst. (2023).","DOI":"10.1109\/TCDS.2023.3329532"},{"key":"77779_CR60","doi-asserted-by":"crossref","unstructured":"Yang, S. & Chen, B. Effective surrogate gradient learning with high-order information bottleneck for spike-based machine intelligence. IEEE Trans. Neural Netw. Learn. Syst. (2023).","DOI":"10.1109\/TNNLS.2023.3329525"},{"key":"77779_CR61","unstructured":"Lapan, M. Deep Reinforcement Learning Hands-on (Packt Publishing, 2020)."},{"key":"77779_CR62","unstructured":"Graesser, L. & Keng, W.\u00a0L. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Professional, 2019)."},{"key":"77779_CR63","doi-asserted-by":"crossref","unstructured":"Eshraghian, J.\u00a0K. et al. Training spiking neural networks using lessons from deep learning. In Proceedings of the IEEE (2023).","DOI":"10.1109\/JPROC.2023.3308088"},{"key":"77779_CR64","unstructured":"Rasmussen, D. NengoDL: Combining deep learning and neuromorphic modelling methods. arXiv1805.11144, 1\u201322 (2018)."},{"key":"77779_CR65","unstructured":"Bohte, S.\u00a0M., Kok, J.\u00a0N. & La Poutr\u00e9, J.\u00a0A. Spikeprop: backpropagation for networks of spiking neurons. In ESANN, vol.\u00a048, 419\u2013424 (Bruges, 2000)."},{"key":"77779_CR66","unstructured":"Shrestha, S.\u00a0B. & Orchard, G. Slayer: Spike layer error reassignment in time. Adv. Neural Inf. Process. Syst. 31 (2018)."},{"key":"77779_CR67","doi-asserted-by":"publisher","first-page":"350","DOI":"10.3389\/fnins.2017.00350","volume":"11","author":"E Stromatias","year":"2017","unstructured":"Stromatias, E., Soto, M., Serrano-Gotarredona, T. & Linares-Barranco, B. An event-driven classifier for spiking neural networks fed with synthetic or dynamic vision sensor data. Front. Neurosci. 11, 350 (2017).","journal-title":"Front. Neurosci."},{"key":"77779_CR68","doi-asserted-by":"crossref","unstructured":"Zanatta, L. et al. Damage detection in structural health monitoring with spiking neural networks. In 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 &IoT), 105\u2013110 (IEEE, 2021).","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488476"},{"key":"77779_CR69","doi-asserted-by":"crossref","unstructured":"Henderson, P. et al. Deep reinforcement learning that matters. In Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018).","DOI":"10.1609\/aaai.v32i1.11694"},{"key":"77779_CR70","unstructured":"Andrychowicz, M. et al. What matters in on-policy reinforcement learning? a large-scale empirical study. arXiv preprint[SPACE]arXiv:2006.05990 (2020)."},{"key":"77779_CR71","unstructured":"Islam, R., Henderson, P., Gomrokchi, M. & Precup, D. Reproducibility of benchmarked deep reinforcement learning tasks for continuous control. arXiv preprint[SPACE]arXiv:1708.04133 (2017)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77779-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77779-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77779-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T19:03:42Z","timestamp":1735326222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-77779-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["77779"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-77779-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4470688\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]},"assertion":[{"value":"24 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30648"}}