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All Technical Committee Conferences (All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] | 2022-01-24 17:10 |
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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 |
| CCS | 2019-03-26 13:30 |
Tokyo | NICT, Koganei, Tokyo | Various synchronization phenomena in digital spiking neural networks Yuya Oishi, Hiroaki Uchida, Tosimichi Saito (Hosei Univ.) CCS2018-47 |
This paper studies dynamics of a network of digital spiking neurons. Repeating integrate-and-fire behavior between a p... [more] |
CCS2018-47 pp.7-10 |
| CCS | 2018-11-23 14:30 |
Hyogo | Kobe Univ. | Digital Spiking Neural Net and approximation of spike-train Hiroaki Uchida, Toshimichi Saito (HU) CCS2018-44 |
This paper considers a ring-coupled digital spiking neural network and its hardware implementation. Depending on paramet... [more] | CCS2018-44 pp.61-65 |
| MBE, NC, NLP (Joint) |
2018-01-27 14:25 |
Fukuoka | Kyushu Institute of Technology | A study on FPGA implementation of SAM spiking neural network Minoru Motoki, Kazunori Matsuo, Hirohito Shintani (NIT, Kumamoto Col.) NC2017-65 |
his paper describes a design for FPGA implementation of SAM spiking neural network and experimental results of circuit r... [more] | NC2017-65 pp.89-94 |
| MBE, NC (Joint) |
2017-10-07 11:45 |
Osaka | Osaka Electro-Communication University | How the Balance between Global-local Connections Affects the Complexity of Neural Activity
-- Constructive Understanding of Atypical Neural Activity in Autism Spectrum Disorder -- Koki Ichinose, Jihoon Park, Yuji Kawai, Junichi Suzuki (Osaka Univ.), Hiroki Mori (Waseda Univ.), Minoru Asada (Osaka Univ.) NC2017-22 |
The purpose of this study is to understand how atypical neural activity in autism spectrum disorder (ASD) occurs, by con... [more] | NC2017-22 pp.13-18 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-24 10:20 |
Okinawa | Okinawa Institute of Science and Technology | Synchronization and stability of digital spiking neurons Hiroaki Uchida, Tomoki Hamaguchi, Toshimichi Saito (Hosei Univ.) NC2017-15 |
The digital spiking neuron(DSN) is constructed by two shift-resistors connected by a wiring. Depending on the wiring pat... [more] | NC2017-15 pp.105-108 |
| CCS | 2016-11-04 11:15 |
Kyoto | Kyoto Sangyo Univ. (Musubiwaza Bldg.) | Unsupervised Learning with Spike-Timing Dependent Delay Learning Model Takashi Matsubara (Kobe Univ.) CCS2016-32 |
Precious timing of neuronal spikes is considered to play an important role in signal transmission and processing in cent... [more] | CCS2016-32 pp.13-16 |
| NLP | 2016-09-15 13:25 |
Hyogo | Konan Univ. | Implementation circuit of a spiking neuron having two slopes of the state and triangular base signal Yusuke Matsuoka (NIT Yonago College) NLP2016-59 |
This report considers a spiking neuron having two slopes of the state and triangular base signal. The state has two kind... [more] | NLP2016-59 pp.79-82 |
| NLP | 2016-09-15 13:50 |
Hyogo | Konan Univ. | Analysis of Super-stable Periodic Orbits in Piecewise Linear Bifurcating Neuron Circuits Risa Takahashi, Toshimichi Saito (Hosei Univ.) NLP2016-60 |
This paper studies dynamics of the bifurcating neuron circuit. Repeating integrate-and-fire behavior between a constan... [more] |
NLP2016-60 pp.83-87 |
| MBE, NC (Joint) |
2015-11-21 13:25 |
Miyagi | Tohoku University | Steady State Analysis of Digital Spike Maps Hiroki Yamaoka, Toshimichi Saito (Hosei Univ.) NC2015-41 |
This paper studies dynamic of digital spike maps: simple digital dynamical systems that can generate various periodic s... [more] |
NC2015-41 pp.25-29 |
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