Zobrazeno 1 - 10
of 503
pro vyhledávání: '"Fukai, Tomoki"'
Autor:
Koshkin, Roman, Fukai, Tomoki
Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a
Externí odkaz:
http://arxiv.org/abs/2402.01130
Autor:
Alsolami, Ibrahim, Fukai, Tomoki
Rank-order coding, a form of temporal coding, has emerged as a promising scheme to explain the rapid ability of the mammalian brain. Owing to its speed as well as efficiency, rank-order coding is increasingly gaining interest in diverse research area
Externí odkaz:
http://arxiv.org/abs/2308.07034
Autor:
Burns, Thomas F, Fukai, Tomoki
Publikováno v:
International Conference on Learning Representations 2023
Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories.
Externí odkaz:
http://arxiv.org/abs/2305.05179
The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that
Externí odkaz:
http://arxiv.org/abs/2305.04432
Autor:
Alsolami, Ibrahim, Fukai, Tomoki
Fisher's criterion is a widely used tool in machine learning for feature selection. For large search spaces, Fisher's criterion can provide a scalable solution to select features. A challenging limitation of Fisher's criterion, however, is that it pe
Externí odkaz:
http://arxiv.org/abs/2212.09225
Publikováno v:
Frontiers in Neuroscience 2022
In natural auditory environments, acoustic signals originate from the temporal superimposition of different sound sources. The problem of inferring individual sources from ambiguous mixtures of sounds is known as blind source decomposition. Experimen
Externí odkaz:
http://arxiv.org/abs/2201.06123
Publikováno v:
In Neural Networks June 2024 174
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Haga, Tatsuya, Fukai, Tomoki
Publikováno v:
Phys. Rev. Lett. 123, 078101 (2019)
Hebbian learning of excitatory synapses plays a central role in storing activity patterns in associative memory models. Furthermore, interstimulus Hebbian learning associates multiple items in the brain by converting temporal correlation to spatial c
Externí odkaz:
http://arxiv.org/abs/1809.05254
Autor:
Fung, Chi Chung Alan, Fukai, Tomoki
Publikováno v:
Phys. Rev. Lett. 122, 018102 (2019)
Continuous attractor neural networks generate a set of smoothly connected attractor states. In memory systems of the brain, these attractor states may represent continuous pieces of information such as spatial locations and head directions of animals
Externí odkaz:
http://arxiv.org/abs/1808.06889