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Technical Committee on Neurocomputing (NC) (Searched in: 2019)
Search Results: Keywords 'from:2019-12-06 to:2019-12-06'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE | 2019-12-06 09:45 |
Aichi | Toyohashi Tech | Relation of movement intermittency and eye-movement during drawing using Wavelet analysis Yamato Nagata, Naohiro Fukumura (Toyohashi Univ. of Tech.)  MBE2019-45 NC2019-36 |
When performing a movement that moves the arm continuously, such as a manual tracking, discontinuous movement called “mo... [more] | MBE2019-45 NC2019-36 pp.1-5 |
| NC, MBE | 2019-12-06 10:10 |
Aichi | Toyohashi Tech | Implementation of Cerebellar Spiking Neural Network Model on a FPGA Yusuke Shinji (Chubu Univ.), Hirotsugu Okuno (OIT), Yutaka Hirata (Chubu Univ.) MBE2019-46 NC2019-37 |
The cerebellum is crucially involved in motor control and learning. Its neuronal network architecture and firing propert... [more] | MBE2019-46 NC2019-37 pp.7-12 |
| NC, MBE | 2019-12-06 10:35 |
Aichi | Toyohashi Tech | Circuit mechanisms of working memory for the maintenance and cognition of temporal information Hikaru Tokuhara, Yoshiki Kashimori (UEC) MBE2019-47 NC2019-38 |
[more] | MBE2019-47 NC2019-38 pp.13-18 |
| NC, MBE | 2019-12-06 11:00 |
Aichi | Toyohashi Tech | Prediction of EEG Time Series with Reservoir Computing Takayuki Koga, Yuta Takahashi, Rieko Osu (Waseda Univ) MBE2019-48 NC2019-39 |
We applied Reservoir Computing (RC) to predict and generate EEG time-series. In the prediction, 10sec EEG was used for t... [more] | MBE2019-48 NC2019-39 pp.19-24 |
| NC, MBE | 2019-12-06 11:25 |
Aichi | Toyohashi Tech | Mathematical Model for Generating Human Foot-Lifting Movements onto One-Up Stair-Step from Stair Rise Toshikazu Matsui, Shu Kitabatake (Gunma Univ) MBE2019-49 NC2019-40 |
This research formulates a mathematical model generating foot-lifting movements from only the step rise without any info... [more] | MBE2019-49 NC2019-40 pp.25-30 |
| NC, MBE | 2019-12-06 13:00 |
Aichi | Toyohashi Tech | Developing a frequency-selective piezoelectric acoustic sensor highly sensitive to the audible frequency range of rodents Takumi Kuwano, Jun Nishikawa, Takashi Tateno (Hokkaido Univ.) MBE2019-50 NC2019-41 |
In this study, we are planning to develop a piezoelectric acoustic sensor that responds to the audible frequency range o... [more] | MBE2019-50 NC2019-41 pp.31-36 |
| NC, MBE | 2019-12-06 13:25 |
Aichi | Toyohashi Tech | Numerical analysis of coil-induced electric field in micro magnetic stimulation and its evaluation on the basis of evoked neural responses Shunsuke Sugai, Jun Nishikawa, Takashi Tateno (Hokkaido Univ.) MBE2019-51 NC2019-42 |
Magnetic stimulation has widely attracted attention as a treatment for neurological diseases. In general, the size reduc... [more] | MBE2019-51 NC2019-42 pp.37-42 |
| NC, MBE | 2019-12-06 13:50 |
Aichi | Toyohashi Tech | CNN with Aperture Synthesis
-- Toward making anotation tasks simpler and easier -- Ryo Nakamura, Masaru Tanaka, Jun Fuji, Yoshiaki Ueda (Fukuoka Univ) MBE2019-52 NC2019-43 |
(To be available after the conference date) [more] | MBE2019-52 NC2019-43 pp.43-48 |
| NC, MBE | 2019-12-06 14:15 |
Aichi | Toyohashi Tech | Writing authentication model using MLP and SMOTE in Web-testing Daisuke Hayashi, Taisuke Kawamata, Takako Akakura (TUS) MBE2019-53 NC2019-44 |
Since the common examinee authentication method in Web-testing is based only on the ID and password at the beginning of ... [more] | MBE2019-53 NC2019-44 pp.49-54 |
| NC, MBE | 2019-12-06 14:40 |
Aichi | Toyohashi Tech | Implementation of an FPGA-based energy-efficient MCMC method for 2D Lenz-Ising model Patrick Tchicali, Hayaru Shouno (UEC) MBE2019-54 NC2019-45 |
MCMC methods are arguably one of the most useful sampling methods. MCMC while being very useful and practical remains a ... [more] | MBE2019-54 NC2019-45 pp.55-60 |
| NC, MBE | 2019-12-06 15:05 |
Aichi | Toyohashi Tech | Hierarchical prediction error model with echo state network for the auditory local-global oddball paradigm Kosuke Miyoshi (NN), Hiroshi Yamakawa (UTokyo), Koichi Takahashi (RIKEN) MBE2019-55 NC2019-46 |
For the animals, it is imporant to capture important changes in the environmental with smaller energy using hierarchical... [more] | MBE2019-55 NC2019-46 pp.61-65 |
| NC, MBE | 2019-12-06 15:40 |
Aichi | Toyohashi Tech | Prevention of redundant representations and of the black box in stacked autoencoders Masumi Ishikawa (Kyutech) MBE2019-56 NC2019-47 |
Recent progress in deep learning (DL) is remarkable and its recognition capability is said to surpass that of humans. Th... [more] | MBE2019-56 NC2019-47 pp.67-72 |
| NC, MBE | 2019-12-06 16:05 |
Aichi | Toyohashi Tech | Neural Networks for Constructing Logical Operations Yuma Saito, Masafumi Hagiwara (Keio Univ.) MBE2019-57 NC2019-48 |
In this research, we aim to integrate both conventional statistical processing and exact logic processing into the same ... [more] | MBE2019-57 NC2019-48 pp.73-78 |
| NC, MBE | 2019-12-06 16:30 |
Aichi | Toyohashi Tech | Explaining Neural Networks by using a multiple tree Shunya Sasaki, Masafumi Hagiwara (Keio Univ) MBE2019-58 NC2019-49 |
The existing Neural Networks (NNs) have a problem that it is difficult to explain the reasoning process and the grounds ... [more] | MBE2019-58 NC2019-49 pp.79-84 |
| NC, MBE | 2019-12-06 16:55 |
Aichi | Toyohashi Tech | Evaluation of the visualization techniques providing explanations for decisions of convolutional neural networks Mizuki Mori, Hiroki Tanaka (Kyoto-Sangyo Univ) MBE2019-59 NC2019-50 |
Recent work has proposed a variety of techniques to visualize what a convolutional neural networks (CNN) utilizes to cla... [more] | MBE2019-59 NC2019-50 pp.85-88 |
| NC, MBE | 2019-12-06 17:20 |
Aichi | Toyohashi Tech | Regularization Term of WRH Type Used with Moore-Penrose Inverse for Optimizing Neural Networks Yoshifusa Ito (FHU), Hiroyuki Izumi (AGU), Cidambi Srinivasan (UK) MBE2019-60 NC2019-51 |
Weigend et al. proposed an algorithm for optimizing neural networks, which suppressed the notorious over- tting. They at... [more] | MBE2019-60 NC2019-51 pp.89-94 |
| NC, MBE | 2019-12-06 13:00 |
Aichi | Toyohashi Tech | Effects of View Angle and Amount of Information on Transition While Viewing Video Makoto Sudo, Kiyoko Yokoyama (Nagoya City Univ.) MBE2019-61 NC2019-52 |
The purpose of this study was to investigate the effect of view angle and amount of information on the naturalness of tr... [more] | MBE2019-61 NC2019-52 pp.95-100 |
| NC, MBE | 2019-12-06 13:25 |
Aichi | Toyohashi Tech | Proposal of Region Segmentation Algorithm for Facial Thermal Image Using Eigenfaces Yuki Hasumi, Kosuke Oiwa, Akio Nozawa (Aoyama Gakuin Univ.) MBE2019-62 NC2019-53 |
In late years, facial skin temperature acquired non-catalytically by using thermal cameras is suggested as one of the no... [more] | MBE2019-62 NC2019-53 pp.101-105 |
| NC, MBE | 2019-12-06 13:50 |
Aichi | Toyohashi Tech | Kazuhiko Sato, Yuko Hayashi, Hisae O. Shimizu, Kazuyuki Kimura, Masaji Yamashita (Hokkaido University of Science) MBE2019-63 NC2019-54 |
(To be available after the conference date) [more] | MBE2019-63 NC2019-54 pp.107-110 |
| NC, MBE | 2019-12-06 14:30 |
Aichi | Toyohashi Tech | Ueno Kyosei, Hashimoto Yasunari (Kitami Inst. of. Tech) MBE2019-64 NC2019-55 |
(To be available after the conference date) [more] | MBE2019-64 NC2019-55 pp.111-114 |
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