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 |
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 |
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 |
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 |
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, 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 |
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 |
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 |
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, CAS |
2015-10-05 13:55 |
Hiroshima |
Aster Plaza |
Generation of disital spike-trains based on growing greedy search Kei Yamaoka, Toshimichi Saito (HU) CAS2015-27 NLP2015-88 |
This paper studies a simple evolutionary algorithm for synthesis of digital spiking neurons(DSNs).
The DSN is based on... [more] |
CAS2015-27 NLP2015-88 pp.37-41 |
CCS |
2015-08-06 13:45 |
Hokkaido |
Dai-ichi Takimotokan (Noboribetsu, Hokkaido) |
Parameter estimation of the spiking neuron model Hiroaki Kurokawa, Shunichi Komatsuzaki, Hideyuki Kato (Tokyo University of Technology) CCS2015-29 |
The complex network structure realizes various functions in biological neuronal systems. The investigation of the signal... [more] |
CCS2015-29 pp.1-6 |
NLP |
2014-01-22 11:00 |
Hokkaido |
Niseko Park Hotel |
Analysis of Bifurcation of Simple Spiking Neuron Model with Filters Shota Kirikawa, Toshimichi Saito (Hosei Univ.) NLP2013-149 |
This paper studies a simple spiking neuron with filtered base signal.
The dynamics depends crucially on the shape of t... [more] |
NLP2013-149 pp.107-110 |
NLP |
2014-01-22 11:40 |
Hokkaido |
Niseko Park Hotel |
Analysis of a Dendritic Network Based on an Asynchronous Cellular Automaton Naoki Shimada (Osaka Univ.), Hiroyuki Torikai (Kyouto Sangyo Univ.) NLP2013-151 |
The neuron is roughly divided into three parts: soma, dendrite, and axon.
In this paper, a multi-compartment neuron mod... [more] |
NLP2013-151 pp.117-122 |
NC, MBE (Joint) |
2013-12-21 14:20 |
Gifu |
Gifu University |
Response of Simple Bifurcating neurons to plural inputs Yusaku Yanase, Shota Kirikawa, Toshimichi Saito (HU) NC2013-63 |
This paper studies a simple spiking neuron model with double inputs. The double inputs are applied as the base signal fo... [more] |
NC2013-63 pp.59-62 |