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 Results 1 - 20 of 81  /  [Next]  
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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