Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Sakemi, Yusuke"'
Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. This approach offers a model for considering how the biological brain can create variability in its behavior and learn in an expl
Externí odkaz:
http://arxiv.org/abs/2405.09086
The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the firing time
Externí odkaz:
http://arxiv.org/abs/2307.13007
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional representation of time-series data i
Externí odkaz:
http://arxiv.org/abs/2301.09235
We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that encodes information in the relative multiple spike timing of individual neurons without single-spike restrictions. The proposed algorithm inherits the advan
Externí odkaz:
http://arxiv.org/abs/2211.16113
The spiking neural network (SNN) has been attracting considerable attention not only as a mathematical model for the brain, but also as an energy-efficient information processing model for real-world applications. In particular, SNNs based on tempora
Externí odkaz:
http://arxiv.org/abs/2106.10382
Autor:
Sakemi, Yusuke1,2 (AUTHOR) yusuke.sakemi@p.chibakoudai.jp, Nobukawa, Sou1,3,4 (AUTHOR), Matsuki, Toshitaka5 (AUTHOR), Morie, Takashi6 (AUTHOR), Aihara, Kazuyuki1,2 (AUTHOR)
Publikováno v:
Communications Physics. 1/12/2024, Vol. 7 Issue 1, p1-11. 11p.
Publikováno v:
Sci Rep 10, 21794 (2020)
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edg
Externí odkaz:
http://arxiv.org/abs/2006.06218
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient computational models
Externí odkaz:
http://arxiv.org/abs/2001.05348
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.