Artykuły w czasopismach na temat „Spiking neural works”
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Ponghiran, Wachirawit, i 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.
Pełny tekst źródłaChunduri, Raghavendra K., i Darshika G. Perera. "Neuromorphic Sentiment Analysis Using Spiking Neural Networks". Sensors 23, nr 18 (6.09.2023): 7701. http://dx.doi.org/10.3390/s23187701.
Pełny tekst źródłaSzczęsny, Szymon, Damian Huderek i 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.
Pełny tekst źródłaNgu, Huynh Cong Viet, i Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks". Applied Sciences 12, nr 11 (6.06.2022): 5749. http://dx.doi.org/10.3390/app12115749.
Pełny tekst źródłaNgu, Huynh Cong Viet, i Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks". Applied Sciences 12, nr 11 (6.06.2022): 5749. http://dx.doi.org/10.3390/app12115749.
Pełny tekst źródłaYan, Zhanglu, Jun Zhou i 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.
Pełny tekst źródłaKim, Youngeun, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer i 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.
Pełny tekst źródłaMárquez-Vera, Carlos Antonio, Zaineb Yakoub, Marco Antonio Márquez Vera i 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.
Pełny tekst źródłaWu, Yujie, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie i 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.
Pełny tekst źródłaLourenço, J., Q. R. Al-Taai, A. Al-Khalidi, E. Wasige i J. Figueiredo. "Resonant Tunnelling Diode – Photodetectors for spiking neural networks". Journal of Physics: Conference Series 2407, nr 1 (1.12.2022): 012047. http://dx.doi.org/10.1088/1742-6596/2407/1/012047.
Pełny tekst źródłaFu, Si-Yao, Guo-Sheng Yang i Xin-Kai Kuai. "A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition". Computational Intelligence and Neuroscience 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/946589.
Pełny tekst źródłaXiao, Chao, Jihua Chen i Lei Wang. "Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI". Sensors 22, nr 19 (24.09.2022): 7248. http://dx.doi.org/10.3390/s22197248.
Pełny tekst źródła‘Atyka Nor Rashid, Fadilla, i Nor Surayahani Suriani. "Spiking neural network classification for spike train analysis of physiotherapy movements". Bulletin of Electrical Engineering and Informatics 9, nr 1 (1.02.2020): 319–25. http://dx.doi.org/10.11591/eei.v9i1.1868.
Pełny tekst źródłaKheradpisheh, Saeed Reza, i Timothée Masquelier. "Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron". International Journal of Neural Systems 30, nr 06 (28.05.2020): 2050027. http://dx.doi.org/10.1142/s0129065720500276.
Pełny tekst źródłaAl-Hamid, Ali A., i HyungWon Kim. "Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding". Electronics 9, nr 10 (29.09.2020): 1599. http://dx.doi.org/10.3390/electronics9101599.
Pełny tekst źródłaQin, Xing, Chaojie Li, Haitao He, Zejun Pan i Chenxiao Lai. "Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network". Electronics 12, nr 11 (23.05.2023): 2351. http://dx.doi.org/10.3390/electronics12112351.
Pełny tekst źródłaKorsakov, Anton, Lyubov Astapova i Aleksandr Bakhshiev. "Application of a Compartmental Spiking Neuron Model with Structural Adaptation for Solving Classification Problems". Informatics and Automation 21, nr 3 (13.05.2022): 493–520. http://dx.doi.org/10.15622/ia.21.3.2.
Pełny tekst źródłaQiu, Xuerui, Rui-Jie Zhu, Yuhong Chou, Zhaorui Wang, Liang-Jian Deng i Guoqi Li. "Gated Attention Coding for Training High-Performance and Efficient Spiking Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 1 (24.03.2024): 601–10. http://dx.doi.org/10.1609/aaai.v38i1.27816.
Pełny tekst źródłaSakthivadivel, Dalton A. R. "Formalizing the Use of the Activation Function in Neural Inference". Complex Systems 31, nr 4 (15.12.2022): 433–49. http://dx.doi.org/10.25088/complexsystems.31.4.433.
Pełny tekst źródłaLiu, Jing, Xu Yang, Yimeng Zhu, Yunlin Lei, Jian Cai, Miao Wang, Ziyi Huan i Xialv Lin. "How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study". Brain Sciences 11, nr 2 (25.01.2021): 153. http://dx.doi.org/10.3390/brainsci11020153.
Pełny tekst źródłaKanazawa, Yusuke, Tetsuya Asai i Yoshihito Amemiya. "Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses". Journal of Robotics and Mechatronics 15, nr 2 (20.04.2003): 208–18. http://dx.doi.org/10.20965/jrm.2003.p0208.
Pełny tekst źródłaHandy, Gregory, i Alla Borisyuk. "Investigating the ability of astrocytes to drive neural network synchrony". PLOS Computational Biology 19, nr 8 (9.08.2023): e1011290. http://dx.doi.org/10.1371/journal.pcbi.1011290.
Pełny tekst źródłaSaemaldahr, Raghdah, i Mohammad Ilyas. "Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning". Sensors 23, nr 14 (21.07.2023): 6578. http://dx.doi.org/10.3390/s23146578.
Pełny tekst źródłaO’Donnell, Cian, J. Tiago Gonçalves, Nick Whiteley, Carlos Portera-Cailliau i Terrence J. Sejnowski. "The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data". Neural Computation 29, nr 1 (styczeń 2017): 50–93. http://dx.doi.org/10.1162/neco_a_00910.
Pełny tekst źródłaWang, Jiang, Ruixue Han, Xilei Wei, Yingmei Qin, Haitao Yu i Bin Deng. "Weak signal detection and propagation in diluted feed-forward neural network with recurrent excitation and inhibition". International Journal of Modern Physics B 30, nr 02 (20.01.2016): 1550253. http://dx.doi.org/10.1142/s0217979215502537.
Pełny tekst źródłaLi, Duowei, Jianping Wu i Depin Peng. "Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks". Journal of Advanced Transportation 2021 (2.12.2021): 1–20. http://dx.doi.org/10.1155/2021/9290921.
Pełny tekst źródłaHarel, Yuval, i Ron Meir. "Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints". Neural Computation 32, nr 4 (kwiecień 2020): 794–828. http://dx.doi.org/10.1162/neco_a_01267.
Pełny tekst źródłaKleijnen, Robert, Markus Robens, Michael Schiek i Stefan van Waasen. "A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks". Journal of Low Power Electronics and Applications 12, nr 2 (21.04.2022): 23. http://dx.doi.org/10.3390/jlpea12020023.
Pełny tekst źródłaWang, Yihao, Danqing Wu, Yu Wang, Xianwu Hu, Zizhao Ma, Jiayun Feng i Yufeng Xie. "A Low-Cost Hardware-Friendly Spiking Neural Network Based on Binary MRAM Synapses, Accelerated Using In-Memory Computing". Electronics 10, nr 19 (8.10.2021): 2441. http://dx.doi.org/10.3390/electronics10192441.
Pełny tekst źródłaChen, Ruizhi, i Ling Li. "Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation". Neural Computation 32, nr 12 (grudzień 2020): 2557–600. http://dx.doi.org/10.1162/neco_a_01319.
Pełny tekst źródłaQiu, Xiaorong, Ye Xu, Yingzhong Shi, S. Kannadhasan Deepa i S. Balakumar. "Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method". Journal of Computer Networks and Communications 2023 (31.12.2023): 1–13. http://dx.doi.org/10.1155/2023/8840168.
Pełny tekst źródłaMorita, Kenta, Haruhiko Takase, Naoki Morita, Hiroharu Kawanak i Hidehiko Kita. "Spiking Neural Network to Extract Frequent Words from Japanese Speech Data". Procedia Computer Science 159 (2019): 363–71. http://dx.doi.org/10.1016/j.procs.2019.09.191.
Pełny tekst źródłaFadhil, Muthna Jasim, Maitham Ali Naji i Ghalib Ahmed Salman. "Transceiver error reduction by design prototype system based on neural network analysis method". Indonesian Journal of Electrical Engineering and Computer Science 18, nr 3 (1.06.2020): 1244. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1244-1251.
Pełny tekst źródłaNaudin, Loïs. "Biological emergent properties in non-spiking neural networks". AIMS Mathematics 7, nr 10 (2022): 19415–39. http://dx.doi.org/10.3934/math.20221066.
Pełny tekst źródłaGrimaldi, Antoine, Amélie Gruel, Camille Besnainou, Jean-Nicolas Jérémie, Jean Martinet i Laurent U. Perrinet. "Precise Spiking Motifs in Neurobiological and Neuromorphic Data". Brain Sciences 13, nr 1 (29.12.2022): 68. http://dx.doi.org/10.3390/brainsci13010068.
Pełny tekst źródłaLong, Yun. "Design and Evaluation of English Vocabulary Learning Aids Based on Word Vector Modelling". Journal of Electrical Systems 20, nr 6s (29.04.2024): 1763–74. http://dx.doi.org/10.52783/jes.3094.
Pełny tekst źródłaHayat, Hanna, Amit Marmelshtein, Aaron J. Krom, Yaniv Sela, Ariel Tankus, Ido Strauss, Firas Fahoum, Itzhak Fried i Yuval Nir. "Reduced neural feedback signaling despite robust neuron and gamma auditory responses during human sleep". Nature Neuroscience 25, nr 7 (lipiec 2022): 935–43. http://dx.doi.org/10.1038/s41593-022-01107-4.
Pełny tekst źródłaPattusamy, Murugan, i Lakshmi Kanth. "Classification of Tweets Into Facts and Opinions Using Recurrent Neural Networks". International Journal of Technology and Human Interaction 19, nr 1 (10.03.2023): 1–14. http://dx.doi.org/10.4018/ijthi.319358.
Pełny tekst źródłaHatsopoulos, N. G., M. Burrows i G. Laurent. "Hysteresis reduction in proprioception using presynaptic shunting inhibition". Journal of Neurophysiology 73, nr 3 (1.03.1995): 1031–42. http://dx.doi.org/10.1152/jn.1995.73.3.1031.
Pełny tekst źródłaLarsson, J. P., Fátima Vera Constán, Núria Sebastián-Gallés i Gustavo Deco. "Lexical Plasticity in Early Bilinguals Does Not Alter Phoneme Categories: I. Neurodynamical Modeling". Journal of Cognitive Neuroscience 20, nr 1 (styczeń 2008): 76–94. http://dx.doi.org/10.1162/jocn.2008.20004.
Pełny tekst źródłaSkinner, T. L., i B. Peretz. "Age sensitivity of osmoregulation and of its neural correlates in Aplysia". American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 256, nr 4 (1.04.1989): R989—R996. http://dx.doi.org/10.1152/ajpregu.1989.256.4.r989.
Pełny tekst źródłaWoo, Dae-Seong, Hyun-Do Choi, Hong-Uk Jin, Jae-Kyeong Kim, Tae-Hun Shim i Jea-Gun Park. "Multi-Bit Self-Rectifying Synaptic Memristor Having Tri-Layer Structure for Quantization Aware Training of Quantized Neural Network". ECS Meeting Abstracts MA2023-02, nr 30 (22.12.2023): 1560. http://dx.doi.org/10.1149/ma2023-02301560mtgabs.
Pełny tekst źródłaLee, Albert K., i Matthew A. Wilson. "A Combinatorial Method for Analyzing Sequential Firing Patterns Involving an Arbitrary Number of Neurons Based on Relative Time Order". Journal of Neurophysiology 92, nr 4 (październik 2004): 2555–73. http://dx.doi.org/10.1152/jn.01030.2003.
Pełny tekst źródłaUusitalo, R. O., M. Juusola i M. Weckstrom. "Graded responses and spiking properties of identified first-order visual interneurons of the fly compound eye". Journal of Neurophysiology 73, nr 5 (1.05.1995): 1782–92. http://dx.doi.org/10.1152/jn.1995.73.5.1782.
Pełny tekst źródłaBarrio, L. C., A. Araque i W. Buno. "Participation of voltage-gated conductances on the response succeeding inhibitory synaptic potentials in the crayfish slowly adapting stretch receptor neuron". Journal of Neurophysiology 72, nr 3 (1.09.1994): 1140–51. http://dx.doi.org/10.1152/jn.1994.72.3.1140.
Pełny tekst źródłaYan, Yulong, Haoming Chu, Yi Jin, Yuxiang Huan, Zhuo Zou i Lirong Zheng. "Backpropagation With Sparsity Regularization for Spiking Neural Network Learning". Frontiers in Neuroscience 16 (14.04.2022). http://dx.doi.org/10.3389/fnins.2022.760298.
Pełny tekst źródłaGuo, Yufei, Xuhui Huang i Zhe Ma. "Direct learning-based deep spiking neural networks: a review". Frontiers in Neuroscience 17 (16.06.2023). http://dx.doi.org/10.3389/fnins.2023.1209795.
Pełny tekst źródłaPoliti, Antonio, i Alessandro Torcini. "A robust balancing mechanism for spiking neural networks". Chaos: An Interdisciplinary Journal of Nonlinear Science 34, nr 4 (1.04.2024). http://dx.doi.org/10.1063/5.0199298.
Pełny tekst źródłaWang, Jing. "Training multi-layer spiking neural networks with plastic synaptic weights and delays". Frontiers in Neuroscience 17 (24.01.2024). http://dx.doi.org/10.3389/fnins.2023.1253830.
Pełny tekst źródłaPandey, Shagun. "Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, nr 07 (22.07.2023). http://dx.doi.org/10.55041/ijsrem24791.
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