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Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP, CCS 2018-06-08
09:45		 Kyoto Kyoto Terrsa Application of tensor decomposition to chaotic itinerancy time series
Takahiro Arai, Toshio Aoyagi (Kyoto Univ.) NLP2018-29 CCS2018-2		 Tensor decomposition is a typical method for analyzing resting-state BOLD signals. This method can decompose the observe... [more] NLP2018-29 CCS2018-2
pp.7-12
NC, MBE 2015-03-16
14:45		 Tokyo Tamagawa University Reinforcement Learning based on Internal-Dynamics-Derived Exploration Using a Chaotic Neural Network
Katsunari Shibata, Yuta Sakashita (Oita Univ.) MBE2014-166 NC2014-117		 As a basic concept for emergence of intelligence through autonomous learning, exploration that is es- sential in reinfor... [more] MBE2014-166 NC2014-117
pp.277-282
NLP 2009-11-11
14:30		 Kagoshima   Parallel Simulation of a Large-Scale Chaotic Neural Network
Makito Oku, Koji Iwayama, Keita Tokuda, Hideyuki Suzuki, Kazuyuki Aihara (Univ. of Tokyo.) NLP2009-92		 Recently large-scale numerical simulations of artificial neural networks are getting popular.
The number of units deal... [more]		 NLP2009-92
pp.55-59
NC 2009-01-20
10:00		 Hokkaido Hokkaido Univ. [Invited Talk] Chaotic itinerancy in the hippocampal CA3 and contractive affine transformations in CA1 provide a dynamical interpretation of complex memory
Ichiro Tsuda (Hokkaido Univ.) NC2008-96		 Since David Marr's pioneering paper entitled “Simple memory: A model for the archecortex” published in 1969, the functio... [more] NC2008-96
pp.85-86
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