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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 11:50 |
Tokushima | Naruto University of Education | Optimization of synaptic scaling rule, its implementation on modular spiking neural networks and analysis of its affects Takumi Shinkawa, Hideyuki Kato (Oita Univ.), Yoshitaka Ishikawa (FUN), Takuma Sumi, Hideaki Yamamoto (Tohoku Univ.), Yuichi Katori (FUN)  NLP2023-107 MICT2023-62 MBE2023-53 |
In this study, to theoretically investigate the information processing mechanisms in the brain, we optimized synaptic sc... [more] | NLP2023-107 MICT2023-62 MBE2023-53 pp.110-113 |
| CCS | 2023-11-12 10:25 |
Toyama | Toyama Prefectural University | Analysis of a simple network topology for optimizer based on spiking-neural oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) CCS2023-34 |
Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are one of the deterministic PSO methods, which are based ... [more] | CCS2023-34 pp.53-57 |
| NC, MBE (Joint) |
2023-10-28 10:45 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
A design of ultra-low power reservoir computing system with analog CMOS spiking neural network circuits Satoshi Ono, Satoshi Moriya, Hideaki Yamamoto (Tohoku Univ.), Yasushi Yuminaka (Gunma Univ.), Yoshihiko Horio, Shigeo Sato (Tohoku Univ.) NC2023-29 |
Spiking neural network (SNN) is expected to be applied to edge computing due to its low power consumption when implement... [more] | NC2023-29 p.23 |
| SeMI, RCS, RCC, NS, SR (Joint) |
2023-07-12 15:50 |
Osaka | Osaka University Nakanoshima Center + Online (Primary: On-site, Secondary: Online) |
[Invited Talk]
Neural computing in wireless IoT network Naoki Wakamiya (Osaka Univ.) RCC2023-15 NS2023-33 RCS2023-85 SR2023-32 SeMI2023-26 |
Collection, management, and processing data at the edge of an IoT system is effective in distribution of communication a... [more] | RCC2023-15 NS2023-33 RCS2023-85 SR2023-32 SeMI2023-26 p.8(RCC), p.8(NS), p.32(RCS), p.32(SR), p.26(SeMI) |
| NLP | 2023-05-13 10:00 |
Fukushima | Kenshin Koriyama Cultural Center (Koriyama, Fukushima) | A Study of Ergodic Sequential Circuit Neuronal Networks for Use in Neuroprosthetic Devices Yuta Shiomi, Hiroyuki Torikai (Hosei Univ.) NLP2023-1 |
In this study, we propose a network based on an ergodic ordered circuit neuron model. We show that the proposed model c... [more] |
NLP2023-1 pp.1-4 |
| CCS | 2023-03-26 16:05 |
Hokkaido | RUSUTSU RESORT | Simple Applications of WiBIC with Asynchronous Pulse Code Multiple Access Jiaying Lin, Ryuji Nagazawa, Kien Nguyen (Chiba Univ.), Hiroyuki Torikai (Hosei Univ.), Mikio Hasegawa (Tokyo Univ.), Won-Joo Hwang (Pusan National Univ.), Hiroo Sekiya (Chiba Univ.) CCS2022-78 |
In this study, we propose to combine Spiking Neural Network and IoT network to construct a distributed information proce... [more] | CCS2022-78 pp.85-90 |
| NLP, MSS | 2023-03-15 15:15 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Hippocampal CA3 spiking neural network model for constructing never-experienced novel path sequences on a maze task Kensuke Takada, Katsumi Tateno (Kyushu Inst. Tech.) MSS2022-74 NLP2022-119 |
Hippocampal neurons that represent the animal's self-location are called "place cells." In the maze task in rodents, hip... [more] | MSS2022-74 NLP2022-119 p.64 |
| NC, NLP | 2023-01-29 10:15 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Low complexity of neural activity caused by weak inhibition in spiking neural networks Jihoon Park (NICT/Osaka Univ.), Yuji Kawai (Osaka Univ.), Minoru Asada (IPUT Univ./Osaka Univ./Chubu Univ./NICT) NLP2022-96 NC2022-80 |
The balance between excitatory and inhibitory neuronal activities (E/I) is an essential factor to perform normal functio... [more] | NLP2022-96 NC2022-80 pp.81-86 |
| MBE, NC | 2022-12-03 15:50 |
Osaka | Osaka Electro-Communication University | A RISC-V Soft-core Processor with Custom VLIW Extension for Spiking Neural Network Accelerator Mingyang Li, Yuki Hayashida (Mie Univ.) MBE2022-40 NC2022-62 |
We aim to develop an embedded accelerator for spiking neural networks (SNN). In order to develop prototypes of various S... [more] | MBE2022-40 NC2022-62 pp.86-91 |
| NLP | 2022-11-24 15:50 |
Shiga | (Primary: On-site, Secondary: Online) |
Investigation of the range in application of a neural network with spike timing in quantitative analysis of two gas mixtures Taiga Manabe, Katsumi Tateno (KIT), Osamu Nakamura (UT) NLP2022-65 |
Volatile organic compounds (VOCs) are useful substances in industry, but the effects of exposure to VOCs through inhalat... [more] | NLP2022-65 pp.36-41 |
| CCS | 2022-11-18 16:00 |
Mie | (Primary: On-site, Secondary: Online) |
Investigation for the coupling interactions in swarm intelligence algorithm based on spiking neural-oscillator networks Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) CCS2022-60 |
Optimizer based on Spiking Neural-oscillator Networks (OSNNs) are deterministic swarm intelligence algorithms which intr... [more] | CCS2022-60 pp.85-90 |
| SANE | 2022-11-10 15:40 |
Chiba | Chiba Univ. (Nishi-Chiba Campus) (Primary: On-site, Secondary: Online) |
Application of Spiking Neural Network with Event Camera to Terrain Relative Navigation for Spacecraft Landings Yudai Azuma (Tokyo Univ.), Seisuke Fukuda (JAXA) SANE2022-60 |
In recent years, lunar exploration missions have been required to land at a specific location for the purpose of observi... [more] | SANE2022-60 pp.55-60 |
| NC, MBE (Joint) |
2022-09-29 10:25 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Analog circuit implementation of spiking neural networks and its application to time-series information processing Satoshi Moriya, Hideaki Yamamoto (Tohoku Univ), Yasushi Yuminaka (Gunma Univ.), Shigeo Sato, Yoshihiko Horio (Tohoku Univ) NC2022-33 |
Edge computing in which low-dimensional signals such as sensor output are processed nearby sensors have become increasin... [more] | NC2022-33 p.5 |
| NC, MBE (Joint) |
2022-09-29 10:50 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Improvement of AdaBoost algorithm for spiking neural networks Masaya Kawaguchi, Jun Ohkubo (Saitama Univ.) NC2022-34 |
Unlike artificial neural networks (ANNs), which have been widely used recently, spiking neural networks (SNNs) have attr... [more] | NC2022-34 pp.6-10 |
| CCS, NLP | 2022-06-09 15:45 |
Osaka | (Primary: On-site, Secondary: Online) |
Reservoir computing with spiking neural networks and reward-modulated STDP Takayuki Tsurumi, Gouhei Tanaka (UTokyo) NLP2022-7 CCS2022-7 |
In a previous study, it was verified that tasks requiring nonlinearity and working memory can be performed using reward-... [more] | NLP2022-7 CCS2022-7 pp.31-35 |
| CCS, NLP | 2022-06-10 15:55 |
Osaka | (Primary: On-site, Secondary: Online) |
Swarm intelligence algorithm based on spiking neural-oscillator networks, coupling interactions and solving performances Tomoyuki Sasaki (SIT), Hidehiro Nakano (TCU) NLP2022-22 CCS2022-22 |
Optimizer based on spiking neural-oscillator networks (OSNN) are one of the deterministic swarm intelligence algorithms... [more] |
NLP2022-22 CCS2022-22 pp.111-116 |
| SR | 2022-05-13 13:30 |
Tokyo | NICT Koganei (Primary: On-site, Secondary: Online) |
[Invited Talk]
Computation with optical parametric oscillator networks Hiroki Takesue, Takahiro Inagaki, Kensuke Inaba, Takuya Ikuta, Yasuhiro Yamada, Yuya Yonezu, Toshimori Honjo (NTT) SR2022-15 |
We present our recent effort to realize computations that efficiently solve difficult problems such as combinatorial opt... [more] | SR2022-15 pp.67-69 |
| MSS, NLP | 2022-03-29 09:40 |
Online | Online | Effects of sparse connections in spiking neural networks for unsupervised pattern recognition Hiroki Shinagawa, Kantaro Fujiwara, Gouhei Tanaka (Univ. of Tokyo) MSS2021-69 NLP2021-140 |
Recently, the spiking neural network (SNN) models, which compute using spatio-temporal information representation by neu... [more] | MSS2021-69 NLP2021-140 pp.71-76 |
| VLD, HWS [detail] | 2022-03-07 15:05 |
Online | Online | Low-Energy and Fast Inference Method for Spiking Neural Networks Using Dynamic Threshold Adjustment Takehiro Habara, Hiromitsu Awano (Kyoto Univ.) VLD2021-87 HWS2021-64 |
Conventional SNNs have fixed thresholds that determine the possibility of neuron firing, resulting in degradation of inf... [more] | VLD2021-87 HWS2021-64 pp.57-62 |
| MBE, NC (Joint) |
2022-03-03 11:10 |
Online | Online | Basic characteristics of SAM spiking neuron model with rate coding Minoru Motoki (Kumamoto KOSEN) NC2021-63 |
he SAM neuron model is one of spiking neural networks that have high computational efficiency and familiarity for digita... [more] | NC2021-63 pp.88-93 |
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