Literatura científica selecionada sobre o tema "Spiking neural works"
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Artigos de revistas sobre o assunto "Spiking neural works"
Ponghiran, Wachirawit, e Kaushik Roy. "Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junho de 2022): 8001–8. http://dx.doi.org/10.1609/aaai.v36i7.20771.
Texto completo da fonteChunduri, Raghavendra K., e Darshika G. Perera. "Neuromorphic Sentiment Analysis Using Spiking Neural Networks". Sensors 23, n.º 18 (6 de setembro de 2023): 7701. http://dx.doi.org/10.3390/s23187701.
Texto completo da fonteSzczęsny, Szymon, Damian Huderek e Łukasz Przyborowski. "Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry". Sensors 21, n.º 9 (10 de maio de 2021): 3276. http://dx.doi.org/10.3390/s21093276.
Texto completo da fonteNgu, Huynh Cong Viet, e Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks". Applied Sciences 12, n.º 11 (6 de junho de 2022): 5749. http://dx.doi.org/10.3390/app12115749.
Texto completo da fonteNgu, Huynh Cong Viet, e Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks". Applied Sciences 12, n.º 11 (6 de junho de 2022): 5749. http://dx.doi.org/10.3390/app12115749.
Texto completo da fonteYan, Zhanglu, Jun Zhou e Weng-Fai Wong. "Near Lossless Transfer Learning for Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de maio de 2021): 10577–84. http://dx.doi.org/10.1609/aaai.v35i12.17265.
Texto completo da fonteKim, Youngeun, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer e Priyadarshini Panda. "Exploring Temporal Information Dynamics in Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8308–16. http://dx.doi.org/10.1609/aaai.v37i7.26002.
Texto completo da fonteMárquez-Vera, Carlos Antonio, Zaineb Yakoub, Marco Antonio Márquez Vera e Alfian Ma'arif. "Spiking PID Control Applied in the Van de Vusse Reaction". International Journal of Robotics and Control Systems 1, n.º 4 (25 de novembro de 2021): 488–500. http://dx.doi.org/10.31763/ijrcs.v1i4.490.
Texto completo da fonteWu, Yujie, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie e Luping Shi. "Direct Training for Spiking Neural Networks: Faster, Larger, Better". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 1311–18. http://dx.doi.org/10.1609/aaai.v33i01.33011311.
Texto completo da fonteLourenço, J., Q. R. Al-Taai, A. Al-Khalidi, E. Wasige e J. Figueiredo. "Resonant Tunnelling Diode – Photodetectors for spiking neural networks". Journal of Physics: Conference Series 2407, n.º 1 (1 de dezembro de 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2407/1/012047.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteArtificial 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
Capítulos de livros sobre o assunto "Spiking neural works"
Antonietti, Alberto, Claudia Casellato, Egidio D’Angelo e 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.
Texto completo da fontevan Albada, Sacha J., Jari Pronold, Alexander van Meegen e 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.
Texto completo da fonteZheng, Honghao, e 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.
Texto completo da fonteFrick, 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.
Texto completo da fonteGamez, 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.
Texto completo da fonteDumesnil, Etienne, Philippe-Olivier Beaulieu e 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.
Texto completo da fonteCabarle, F., H. Adorna e 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.
Texto completo da fonteTang, Tiong Yew, Simon Egerton e 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.
Texto completo da fonteAhmed, L. Jubair, S. Dhanasekar, K. Martin Sagayam, Surbhi Vijh, Vipin Tyagi, Mayank Singh e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Spiking neural works"
Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, Qingyu Wang e 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.
Texto completo da fonteWang, Yuchen, Kexin Shi, Chengzhuo Lu, Yuguo Liu, Malu Zhang e 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.
Texto completo da fonteLiu, Qianhui, Dong Xing, Huajin Tang, De Ma e 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.
Texto completo da fonteWang, Yuchen, Malu Zhang, Yi Chen e 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.
Texto completo da fonteCheng, Xiang, Yunzhe Hao, Jiaming Xu e 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.
Texto completo da fonteZhu, Zulun, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu e 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.
Texto completo da fonteMorozov, Alexander, Karine Abgaryan e 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.
Texto completo da fonteLiu, Xiyu, e 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.
Texto completo da fonteHong, Shen, Liu Ning, Li Xiaoping e 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.
Texto completo da fonteJimeno Yepes, Antonio, Jianbin Tang e 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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