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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2022-11-25 13:00 |
Shiga | (Primary: On-site, Secondary: Online) |
A biobjective optimization problem in hysteresis associative memories Shinya Kujirai, Toshimichi Saito (HU)  NLP2022-73 |
This paper studies a biobjective optimization problem in hysteresis neural networks as associative memories. The network... [more] | NLP2022-73 pp.73-76 |
| CCS | 2022-11-17 13:00 |
Mie | (Primary: On-site, Secondary: Online) |
A clustering system based on binary associative memories Kazuma Kiyohara, Kento Saka, Toshimichi Saito (HU) CCS2022-43 |
This paper studies application of binary associative memories to clustering systems of binary data. The binary associati... [more] | CCS2022-43 pp.1-4 |
| NC, MBE (Joint) |
2022-09-30 10:05 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
On evolutionary bi-objective optimization in binary associative memories Kazuma Morishita, Toshimichi Saito (HU) NC2022-39 |
This paper studies a bi-objective optimization problem in a binary associative memories(BAMs). The BAMs are characteri... [more] |
NC2022-39 pp.28-31 |
| CCS | 2021-11-18 13:50 |
Osaka | Osaka Univ. (Primary: On-site, Secondary: Online) |
On a biobjective optimization problem in binary associative memories Kento Saka, Toshimichi Saito (HU) CCS2021-20 |
This paper considers a biobjective optimization problem in binary associative memories characterized by ternary connecti... [more] | CCS2021-20 pp.16-19 |
| MBE, NC | 2019-10-12 10:00 |
Miyagi | Stability of Periodic Orbits in 3-Layer Dynamic Binary Neural Networks Seitaro Koyama, Toshimichi Saito (HU) MBE2019-39 NC2019-30 |
The 3-layer dynamic binary neural networks (3-DBNNs) are characterized by ternary connection parameters and signum activ... [more] | MBE2019-39 NC2019-30 p.51 |
|
| MBE, NC (Joint) |
2018-11-22 14:55 |
Kyoto | Analysis of the recalling process of associative memory model with astrocyte function Masaru Kondo, Yoshimasa Tawatsuji, Tatsunori Matsui (Waseda Univ.) NC2018-26 |
While it may take some time for humans to recall specific memories, the associative memory model constructed by intercon... [more] | NC2018-26 pp.17-21 |
|
| MBE, NC (Joint) |
2018-05-19 15:20 |
Toyama | Univ. of Toyama | On Stability of Sparse Binary Neural Networks Seitaro Koyama, Shunsuke Aoki, Toshimichi Saito (HU) NC2018-3 |
The dynamic binary neural network is characterized by ternary connection parameters and the signum activaion function. ... [more] |
NC2018-3 pp.9-13 |
| MBE, NC, NLP (Joint) |
2018-01-27 11:20 |
Fukuoka | Kyushu Institute of Technology | Stability and periodic orbits in dynamic binary neural newtorks Seitaro Koyama, Shunsuke Aoki, Toshimichi Saito (HU) NLP2017-97 |
The dynamic binary neural network is characterized by the signum activation function and ternary connection parameters. ... [more] | NLP2017-97 pp.59-62 |
| MRIS, ITE-MMS | 2017-10-19 13:45 |
Niigata | Kashiwazaki energy hall, Niigata | [Invited Talk]
Analog spintronics devices and its application to artificial neural networks Hisanao Akima, William Borders, Shunsuke Fukami, Satoshi Moriya, Shouta Kurihara, Aleksandr Kurenkov, Yoshihiko Horio, Shigeo Sato, Hideo Ohno (Tohoku Univ.) MR2017-18 |
Developing dedicated integrated circuits operating with low power consumption is indispensable to realize a large scale ... [more] | MR2017-18 pp.7-12 |
| NC, NLP (Joint) |
2016-01-29 14:00 |
Fukuoka | Kyushu Institute of Technology | Analysis of spurious memories in hysteresis associative memories Takahiro Ishikawa, Kei Yamaoka, Toshimichi Saito (HU) NC2015-62 |
The hysteresis neural network is a continuous-time network characterized by binary hysteresis activation function. Th... [more] |
NC2015-62 pp.29-32 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-25 16:10 |
Okinawa | Okinawa Institute of Science and Technology | Evolutionary learning of hysteresis neural networks Kei Yamaoka, Toshimichi Saito (HU) NC2015-10 |
This paper studies evolutionary leaning algorithm for hysteresis associative memories (HAM). The HAM is characterized ... [more] |
NC2015-10 pp.89-92 |
| NC | 2015-01-29 14:35 |
Fukuoka | Kyushu Institute of Technology | Associative memory and amplitude death of hysteresis neural networks Kei Yamaoka, Toshimichi Saito (HU) NC2014-58 |
This paper studies effects of the amplitude death on the storage function of the hysteresis neural network. Applying... [more] |
NC2014-58 pp.7-10 |
| NLP | 2014-06-30 16:25 |
Miyagi | Tohoku Univ. | Study on the hardware of the Bidirectional Associative Memories by using the Inverse Function Delayless model Chunyu Bao, Takeshi Onomi, Yoshihiro Hayakawa, Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2014-26 |
In conventional macro models such as the Hopfield model, the problems that are caused by the solution of the network not... [more] | NLP2014-26 pp.31-36 |
| RECONF | 2013-09-19 14:50 |
Ishikawa | Japan Advanced Institute of Science and Technology | Considerations of Constantize for Entries in Associative Memories Using Dynamic Partial Reconfiguration Tomoaki Ukezono, Koichi Araki (JAIST) RECONF2013-36 |
In general, memories which can be referenced by associative search will enlarge hardware size and extend delay for refer... [more] | RECONF2013-36 pp.97-102 |
| NC, MBE (Joint) |
2012-12-12 11:05 |
Aichi | Toyohashi University of Technology | Instabilities of spurious state with synaptic depression Shin Murata, Yosuke Otsubo, Kenji Nagata (Univ. of Tokyo), Masato Okada (Univ. of Tokyo/BSI RIKEN) NC2012-79 |
The associative memory model is one of typical neural network model and has equilibrium state called spurious state in w... [more] | NC2012-79 pp.31-36 |
| MBE, NC (Joint) |
2012-03-14 16:50 |
Tokyo | Tamagawa University | Bayesian Network Associative Memories Hiroaki Hasegawa, Masafumi Hagiwara (Keio Univ.) NC2011-146 |
In this paper, we propose Bayesian Network Associative Memories (BNAMs) for modeling associative memories with Bayesian ... [more] | NC2011-146 pp.147-152 |
| NC | 2012-01-26 09:00 |
Hokkaido | Future University Hakodate | Storage capacity of the associative memory model with the zero-order synaptic decay Ryota Miyata (Tokyo Tech.), Jun Tsuzurugi (Okayama Univ. Sci.), Toru Aonishi (Tokyo Tech.), Koji Kurata (Univ. Ryukyu.) NC2011-97 |
It has been reported that synaptogenesis, formation of synaptic connection, continues to take place in certain regions o... [more] | NC2011-97 pp.1-6 |
| NC | 2012-01-26 11:45 |
Hokkaido | Future University Hakodate | Comparison and Evaluation of Growing Complex Network Generated by Similarities Keisuke Terayama, Yukari Yamauchi (NU) NC2011-103 |
We proposed a method to configure growing network based on similarities. Generated network by the proposed method shows ... [more] | NC2011-103 pp.37-42 |
| NC, NLP | 2011-01-24 15:35 |
Hokkaido | Hokakido Univ. | [Invited Talk]
Pattern-recalling processes in a quantum-mechanical variant of the Hopfield model Jun-ichi Inoue (Hokkaido Univ.) NLP2010-135 NC2010-99 |
As a mathematical model of associative memories, the Hopfield model was well-established and a lot of studies to ... [more] |
NLP2010-135 NC2010-99 pp.63-68 |
| MI | 2010-01-28 11:40 |
Okinawa | Naha-Bunka-Tenbusu | Brain time-shift map and communication strategy
-- Memory loop and remote copy -- Takuya Kamimura (Ritsumei Univ.), Kazuki Nakamura (Univ. of Hyogo), Kazuyo Yoneda (NBL), Yen-Wei Chen (Ritsumei Univ.), Yuko Mizuno-Matsumoto (Univ. of Hyogo), Tomomitsu Miyoshi, Hajime Sawai (Osaka Univ.), Shinichi Tamura (NBL) MI2009-100 |
We have developed a time-shift map of the brain, which shows/ visualizes signal such as EEG and MEG etc. is transmitted/... [more] | MI2009-100 pp.133-138 |
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