Zobrazeno 1 - 10
of 6 985
pro vyhledávání: '"Park, il"'
Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. Ho
Externí odkaz:
http://arxiv.org/abs/2410.05454
Autor:
Sun, Tian-Rui, Geng, Jin-Jun, Yan, Jing-Zhi, Hu, You-Dong, Wu, Xue-Feng, Castro-Tirado, Alberto J., Yang, Chao, Ping, Yi-Ding, Hu, Chen-Ran, Xu, Fan, Gao, Hao-Xuan, Jiang, Ji-An, Zhu, Yan-Tian, Xue, Yongquan, Pérez-García, Ignacio, Wu, Si-Yu, Fernández-García, Emilio, Caballero-García, María D., Sánchez-Ramírez, Rubén, Guziy, Sergiy, Olivares, Ignacio, del Pulgar, Carlos Jesus Pérez, Castellón, A., Castillo, Sebastián, Xiong, Ding-Rong, Pandey, Shashi B., Hiriart, David, García-Segura, Guillermo, Lee, William H., Carrasco-García, I. M., Park, Il H., Meintjes, Petrus J., van Heerden, Hendrik J., Martín-Carrillo, Antonio, Hanlon, Lorraine, Zhang, Bin-Bin, Maury, Alain, Hernández-García, L., Gritsevich, Maria, Rossi, Andrea, Maiorano, Elisabetta, Cusano, Felice, D'Avanzo, Paolo, Ferro, Matteo, Melandri, Andrea, De Pasquale, Massimiliano, Brivio, Riccardo, Fang, Min, Fan, Lu-Lu, Hu, Wei-Da, Wan, Zhen, Hu, Lei, Zuo, Ying-Xi, Tang, Jin-Long, Zhang, Xiao-Ling, Zheng, Xian-Zhong, Li, Bin, Luo, Wen-Tao, Liu, Wei, Wang, Jian, Zhang, Hong-Fei, Liu, Hao, Gao, Jie, Liang, Ming, Wang, Hai-Ren, Yao, Da-Zhi, Cheng, Jing-Quan, Zhao, Wen, Dai, Zi-Gao
Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, th
Externí odkaz:
http://arxiv.org/abs/2409.17983
Autor:
Vermani, Ayesha, Dowling, Matthew, Jeon, Hyungju, Jordan, Ian, Nassar, Josue, Bernaerts, Yves, Zhao, Yuan, Van Vaerenbergh, Steven, Park, Il Memming
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. T
Externí odkaz:
http://arxiv.org/abs/2409.01280
Autor:
Jeon, Hyungju, Park, Il Memming
Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for bui
Externí odkaz:
http://arxiv.org/abs/2408.08752
Publikováno v:
In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2024)
Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general
Externí odkaz:
http://arxiv.org/abs/2408.00109
State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibi
Externí odkaz:
http://arxiv.org/abs/2403.01371
Autor:
Kim, Cheol-Hwan, Ryu, Seon-Young, Yoon, Ji-Young, Lee, Hyoung-Kwon, Choi, Nak-Gu, Park, Il-Ho, Choi, Hae-Young
Publikováno v:
JMIR mHealth and uHealth, Vol 7, Iss 3, p e11251 (2019)
BackgroundThe surgical microscope is used primarily for microsurgeries, which are more complicated than other surgical procedures and require delicate tasks for a long time. Therefore, during these surgical procedures, surgeons experience back and ne
Externí odkaz:
https://doaj.org/article/305232760d7346c49a15f039aee47727
Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful lear
Externí odkaz:
http://arxiv.org/abs/2308.12585
Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While latent GP models provide a principled and powerful solution in theory, th
Externí odkaz:
http://arxiv.org/abs/2306.01802