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All Technical Committee Conferences  (Searched in: Recent 10 Years)

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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 32  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, NLP
(Joint)
2025-01-29
14:30
Osaka (Osaka) Effects of Introducing Gap Junctions into Spiking Neural Networks on Handwritten Digit Classification
Shinnosuke Touda, Akito Morita, Hirotsugu Okuno (OIT) NC2024-56
We investigated the effect of introducing gap junctions on the classification of handwritten digits in a winner-take-all... [more] NC2024-56
pp.79-83
VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] 2024-11-12
14:30
Oita COMPAL HALL (Oita, Online)
(Primary: On-site, Secondary: Online)
Efficient inference method using adaptive variable time steps in SNN
Naoya Watanabe, Yoshinori Takeuchi (Kindai) VLD2024-29 ICD2024-47 DC2024-51 RECONF2024-59
Artificial intelligence is now widely used in private life and business, and its application requires learning that invo... [more] VLD2024-29 ICD2024-47 DC2024-51 RECONF2024-59
pp.14-19
MRIS, CPM, ITE-MMS [detail] 2024-11-01
12:10
Nagano Nagano Camp. Shinshu Univ. + Online (Nagano, Online)
(Primary: On-site, Secondary: Online)
[Invited Talk] Development of oxide-based leaky-integrating transistor for spiking neural networks
Hisashi Inoue (AIST), Hiroto Tamura (Univ. Tokyo), Ai Kitoh (AIST), Xiangyu Chen, Zolboo Byambadorj (Univ. Tokyo), Takeaki Yajima (Kyushu Univ.), Yasushi Hotta (Univ. Hyogo), Tetsuya Iizuka (Univ. Tokyo), Gouhei Tanaka (Univ. Tokyo/Nagoya Inst. Tech.), Isao Inoue (AIST) MRIS2024-26 CPM2024-55
Biomimetic computing aims to realize energy-efficient information processing by mimicking the behavior of biological neu... [more] MRIS2024-26 CPM2024-55
pp.77-80
NC, MBE
(Joint)
2024-09-27
15:40
Miyagi Tohoku Univ. (Miyagi, Online)
(Primary: On-site, Secondary: Online)
Analog CMOS circuit implementation of STDP and its application to classification tasks
Yosuke Iida, Satoshi Moriya, Hideaki Yamamoto, Shigeo Sato (Tohoku Univ) NC2024-37
STDP, a learning rule suitable for spiking neural networks, learns from local spike timing differences between neurons. ... [more] NC2024-37
pp.29-32
NC, MBE, NLP, MICT
(Joint) [detail]
2024-01-25
11:50
Tokushima Naruto University of Education (Tokushima) 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
NLP 2023-05-13
10:00
Fukushima Kenshin Koriyama Cultural Center (Koriyama, Fukushima) (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
NLP, MSS 2023-03-15
15:15
Nagasaki (Nagasaki, Online)
(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 (Hokkaido, Online)
(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
NC, MBE
(Joint)
2022-09-29
10:25
Miyagi Tohoku Univ. (Miyagi, Online)
(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
MSS, NLP 2022-03-29
09:40
Online 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 (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 (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
RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] 2022-01-24
17:10
Online Online (Online) Ternarizing Deep Spiking Neural Network
Man Wu, Yirong Kan, Van_Tinh Nguyen, Renyuan Zhang, Yasuhiko Nakashima (NAIST) VLD2021-61 CPSY2021-30 RECONF2021-69
The feasibility of ternarizing spiking neural networks (SNNs) is studied in this work toward trading a slight accuracy f... [more] VLD2021-61 CPSY2021-30 RECONF2021-69
pp.67-72
MBE, NC
(Joint)
2021-10-28
15:05
Online Online (Online) Enhancement of spatio-temporal coding performance in spiking neural network and its application to hazard detection for landing of spacecrafts
Hideaki Kinoshita, Shinichi Kimura (TUS), Seisuke Fukuda (JAXA) NC2021-21
Spiking neural networks (SNNs) are a neuromimetic computational architecture that has attracted much attention in recent... [more] NC2021-21
pp.16-21
CCS 2020-11-26
15:25
Online Online (Online) Synthesis and implementation of digital spiking neurons
Tomoki Harada, Toshimichi Saito (HU) CCS2020-18
This paper studies implementation of desired digital spike-trains based on simple evolutionary algorithm.
First, the dy... [more]
CCS2020-18
pp.6-10
MBE, NC, NLP, CAS
(Joint) [detail]
2020-10-29
15:20
Online Online (Online) Unsupervised learning based on local interactions between reservoir and readout neurons
Tstuki Kato, Satoshi Moriya, Hideaki Yamamoto, Masao Sakuraba, Shigeo Sato (Tohoku Univ.) NC2020-12
Reservoir computing is suitable for implementations in edge computing devices thanks to its low computational cost and e... [more] NC2020-12
pp.21-23
IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] 2020-01-23
14:45
Kanagawa Raiosha, Hiyoshi Campus, Keio University (Kanagawa) Study of a Simplified Digital Spiking Neuron and Its FPGA Implementation
Tomohiro Yoneda (NII) VLD2019-75 CPSY2019-73 RECONF2019-65
A simplified digital spiking neural network implementable on FPGAs is proposed in order to reduce necessary resources an... [more] VLD2019-75 CPSY2019-73 RECONF2019-65
pp.135-140
NC, MBE 2019-12-06
10:10
Aichi Toyohashi Tech (Aichi) 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, IBISML, IPSJ-MPS, IPSJ-BIO [detail] 2019-06-17
14:15
Okinawa Okinawa Institute of Science and Technology (Okinawa) Effects of excitatory/inhibitory balance of a spiking neuron model on the organization of neural network
Jihoon Park, Motohiro Ogura, Yuji Kawai, Minoru Asada (Osaka Univ.) NC2019-4
In this study, a spiking neural network model is examined to study how the balance between excitatory and inhibitory neu... [more] NC2019-4
pp.15-20
OME, SDM 2019-04-26
13:10
Kagoshima Yakushima Environmental and Culture Village Center (Kagoshima) Information processing using molecular network system
Takuya Matsumoto (Osaka Univ.) SDM2019-4 OME2019-4
In recent decades, studies on the electronic properties and functions of single molecules have made significant advances... [more] SDM2019-4 OME2019-4
pp.13-17
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