Auswahl der wissenschaftlichen Literatur zum Thema „Spiking neural works“
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Zeitschriftenartikel zum Thema "Spiking neural works"
Ponghiran, Wachirawit, und Kaushik Roy. „Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 7 (28.06.2022): 8001–8. http://dx.doi.org/10.1609/aaai.v36i7.20771.
Der volle Inhalt der QuelleChunduri, Raghavendra K., und Darshika G. Perera. „Neuromorphic Sentiment Analysis Using Spiking Neural Networks“. Sensors 23, Nr. 18 (06.09.2023): 7701. http://dx.doi.org/10.3390/s23187701.
Der volle Inhalt der QuelleSzczęsny, Szymon, Damian Huderek und Łukasz Przyborowski. „Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry“. Sensors 21, Nr. 9 (10.05.2021): 3276. http://dx.doi.org/10.3390/s21093276.
Der volle Inhalt der QuelleNgu, Huynh Cong Viet, und Keon Myung Lee. „Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks“. Applied Sciences 12, Nr. 11 (06.06.2022): 5749. http://dx.doi.org/10.3390/app12115749.
Der volle Inhalt der QuelleNgu, Huynh Cong Viet, und Keon Myung Lee. „Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks“. Applied Sciences 12, Nr. 11 (06.06.2022): 5749. http://dx.doi.org/10.3390/app12115749.
Der volle Inhalt der QuelleYan, Zhanglu, Jun Zhou und Weng-Fai Wong. „Near Lossless Transfer Learning for Spiking Neural Networks“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 12 (18.05.2021): 10577–84. http://dx.doi.org/10.1609/aaai.v35i12.17265.
Der volle Inhalt der QuelleKim, Youngeun, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer und Priyadarshini Panda. „Exploring Temporal Information Dynamics in Spiking Neural Networks“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 7 (26.06.2023): 8308–16. http://dx.doi.org/10.1609/aaai.v37i7.26002.
Der volle Inhalt der QuelleMárquez-Vera, Carlos Antonio, Zaineb Yakoub, Marco Antonio Márquez Vera und Alfian Ma'arif. „Spiking PID Control Applied in the Van de Vusse Reaction“. International Journal of Robotics and Control Systems 1, Nr. 4 (25.11.2021): 488–500. http://dx.doi.org/10.31763/ijrcs.v1i4.490.
Der volle Inhalt der QuelleWu, Yujie, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie und Luping Shi. „Direct Training for Spiking Neural Networks: Faster, Larger, Better“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 1311–18. http://dx.doi.org/10.1609/aaai.v33i01.33011311.
Der volle Inhalt der QuelleLourenço, J., Q. R. Al-Taai, A. Al-Khalidi, E. Wasige und J. Figueiredo. „Resonant Tunnelling Diode – Photodetectors for spiking neural networks“. Journal of Physics: Conference Series 2407, Nr. 1 (01.12.2022): 012047. http://dx.doi.org/10.1088/1742-6596/2407/1/012047.
Der volle Inhalt der QuelleDissertationen zum Thema "Spiking neural works"
Ali, Elsayed Sarah. „Fault Tolerance in Hardware Spiking Neural Networks“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS310.
Der volle Inhalt der QuelleArtificial Intelligence (AI) and machine learning algorithms are taking up the lion's share of the technology market nowadays, and hardware AI accelerators are foreseen to play an increasing role in numerous applications, many of which are mission-critical and safety-critical. This requires assessing their reliability and developing cost-effective fault tolerance techniques; an issue that remains largely unexplored for neuromorphic chips and Spiking Neural Networks (SNNs). A tacit assumption is often made that reliability and error-resiliency in Artificial Neural Networks (ANNs) are inherently achieved thanks to the high parallelism, structural redundancy, and the resemblance to their biological counterparts. However, prior work in the literature unraveled the falsity of this assumption and exposed the vulnerability of ANNs to faults. This requires assessing their reliability and developing cost-effective fault tolerance techniques; an issue that remains largely unexplored for neuromorphic chips and Spiking Neural Networks (SNNs). In this thesis, we tackle the subject of testing and fault tolerance in hardware SNNs. We start by addressing the issue of post-manufacturing test and behavior-oriented self-test of hardware neurons. Then we move on towards a global solution for the acceleration of testing and resiliency analysis of SNNs against hardware-level faults. We also propose a neuron fault tolerance strategy for SNNs, optimized for low area and power overhead. Finally, we present a hardware case-study which would be used as a platform for demonstrating fault-injection experiments and fault-tolerance capabilities
Buchteile zum Thema "Spiking neural works"
Antonietti, Alberto, Claudia Casellato, Egidio D’Angelo und Alessandra Pedrocchi. „Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout“. In Lecture Notes in Computer Science, 35–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_3.
Der volle Inhalt der Quellevan Albada, Sacha J., Jari Pronold, Alexander van Meegen und Markus Diesmann. „Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex“. In Lecture Notes in Computer Science, 47–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_4.
Der volle Inhalt der QuelleZheng, Honghao, und Yang Cindy Yi. „Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing“. In Neuromorphic Computing [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.113050.
Der volle Inhalt der QuelleFrick, Nikolay. „Neuromorphic Computing with Resistive Memory and Bayesian Machines“. In Memristors - the Fourth Fundamental Circuit Element - Theory, Device, and Applications [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003254.
Der volle Inhalt der QuelleGamez, David. „The Simulation of Spiking Neural Networks“. In Handbook of Research on Discrete Event Simulation Environments, 337–58. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-774-4.ch015.
Der volle Inhalt der QuelleDumesnil, Etienne, Philippe-Olivier Beaulieu und Mounir Boukadoum. „Single SNN Architecture for Classical and Operant Conditioning Using Reinforcement Learning“. In Robotic Systems, 786–810. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch041.
Der volle Inhalt der QuelleCabarle, F., H. Adorna und M. A. Martínez-del-Amor. „Simulating Spiking Neural P Systems Without Delays Using GPUs“. In Natural Computing for Simulation and Knowledge Discovery, 109–21. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4253-9.ch006.
Der volle Inhalt der QuelleTang, Tiong Yew, Simon Egerton und János Botzheim. „Spiking Reflective Processing Model for Stress-Inspired Adaptive Robot Partner Applications“. In Rapid Automation, 1047–66. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8060-7.ch049.
Der volle Inhalt der QuelleAhmed, L. Jubair, S. Dhanasekar, K. Martin Sagayam, Surbhi Vijh, Vipin Tyagi, Mayank Singh und Alex Norta. „Introduction to Neuromorphic Computing Systems“. In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–29. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6596-7.ch001.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Spiking neural works"
Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, Qingyu Wang und Bo Xu. „Recent Advances and New Frontiers in Spiking Neural Networks“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/790.
Der volle Inhalt der QuelleWang, Yuchen, Kexin Shi, Chengzhuo Lu, Yuguo Liu, Malu Zhang und Hong Qu. „Spatial-Temporal Self-Attention for Asynchronous Spiking Neural Networks“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/344.
Der volle Inhalt der QuelleLiu, Qianhui, Dong Xing, Huajin Tang, De Ma und Gang Pan. „Event-based Action Recognition Using Motion Information and Spiking Neural Networks“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/240.
Der volle Inhalt der QuelleWang, Yuchen, Malu Zhang, Yi Chen und Hong Qu. „Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/347.
Der volle Inhalt der QuelleCheng, Xiang, Yunzhe Hao, Jiaming Xu und Bo Xu. „LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/211.
Der volle Inhalt der QuelleZhu, Zulun, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu und Siqiang Luo. „Spiking Graph Convolutional Networks“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/338.
Der volle Inhalt der QuelleMorozov, Alexander, Karine Abgaryan und Dmitry Reviznikov. „SIMULATION OF A NEUROMORPHIC NETWORK ON MEMRISTIVE ELEMENTS WITH 1T1R KROSSBAR ARCHITECTURE“. In International Forum “Microelectronics – 2020”. Joung Scientists Scholarship “Microelectronics – 2020”. XIII International conference «Silicon – 2020». XII young scientists scholarship for silicon nanostructures and devices physics, material science, process and analysis. LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1638.silicon-2020/322-325.
Der volle Inhalt der QuelleLiu, Xiyu, und Hongyan Zhang. „Spiking DNA neural trees with applications to conceptual design“. In 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2011. http://dx.doi.org/10.1109/cscwd.2011.5960085.
Der volle Inhalt der QuelleHong, Shen, Liu Ning, Li Xiaoping und Wang Qian. „A cooperative method for supervised learning in Spiking neural networks“. In 2010 14th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2010. http://dx.doi.org/10.1109/cscwd.2010.5472007.
Der volle Inhalt der QuelleJimeno Yepes, Antonio, Jianbin Tang und Benjamin Scott Mashford. „Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips“. In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/274.
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