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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2024-05-09 13:40 |
Kagawa | Kagawa Prefecture Social Welfare Center | Bifurcation analysis of the Hindmarsh-Rose neuron model with electromagnetic induction Masato Saito, Tetsushi Ueta (Tokushima Univ.) |
(To be available after the conference date) [more] | |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 11:30 |
Tokushima | Naruto University of Education | Bifurcation of three Wilson-Cowan neuron models Masaki Yoshikawa, Seiya Amoh, Tetsushi Ueta (Tokushima Univ.)  NLP2023-89 MICT2023-44 MBE2023-35 |
[more] | NLP2023-89 MICT2023-44 MBE2023-35 pp.30-33 |
| 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, CAS | 2023-10-07 11:30 |
Gifu | Work plaza Gifu | Reproduction of Firing Phenomena in a Piecewise Constant Neuron Model by Automatic Cellular Differentiation Method Kengo Hosoi, Hiroyuki Torikai (Hosei Univ) CAS2023-53 NLP2023-52 |
In this study, the cellular differentiation method for a piecewise-constant neuron model is designed using hardware. The... [more] | CAS2023-53 NLP2023-52 pp.104-105 |
| NLP, MSS | 2023-03-17 15:25 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Chaotic Response of Hardware Small World Neural Network with STDP Takuto Yamaguchi, Katsutoshi Saeki, Yoshiki Sasaki (Nihon Univ.) MSS2022-106 NLP2022-151 |
The role of chaotic activity in the brain function is still unclarified. However, it is possible to estimate the role by... [more] | MSS2022-106 NLP2022-151 pp.210-213 |
| 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 |
| EST | 2023-01-26 11:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Analysis of Scattered Light for Biological Imaging Using Optical Pulses Kaito Kobayashi, Seiya Kishimoto, Shuichiro Inoue, Shinichiro Ohnuki (Nihon Univ.) EST2022-78 |
We aim to develop a superior biological imaging method using optical pulses and study time-resolved measurements of mous... [more] | EST2022-78 pp.26-30 |
| DC, SS | 2022-10-25 14:15 |
Fukushima | (Primary: On-site, Secondary: Online) |
Relationship between the Defects in Learning Programs and the Model Distortion on the Convolutional Neural Networks Takumi Tsuchiya, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakajima (NII) SS2022-26 DC2022-32 |
In recent years, the quality issue of machine learning software has become an important concern. When considering the qu... [more] | SS2022-26 DC2022-32 pp.23-28 |
| DC, SS | 2022-10-25 14:40 |
Fukushima | (Primary: On-site, Secondary: Online) |
Comparison of the Coverage Indicators of Evaluation Data for the Convolutional Neural Networks Yuto Yokoyama, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakazima (NII) SS2022-27 DC2022-33 |
Neuron Coverage (NC) was proposed as a measure to quantify the usefulness of evaluation data against Deep Neural Network... [more] | SS2022-27 DC2022-33 pp.29-34 |
| CAS, NLP | 2022-10-20 13:50 |
Niigata | (Primary: On-site, Secondary: Online) |
A Central Pattern Generator Hardware Network Model Reproducing Two Types of Gait Sets Yoshinobu Maeda (Niigata Univ.) CAS2022-21 NLP2022-41 |
Bursting discharges repeating neuronal firings between quiescent states under constant stimulation are thought to be use... [more] | CAS2022-21 NLP2022-41 pp.11-16 |
| CAS, NLP | 2022-10-20 16:10 |
Niigata | (Primary: On-site, Secondary: Online) |
Learning Method for Echo State Networks Constructed by Chaotic Neuron Models by Innate Training Yudai Ebato, Sou Nobukawa, Yusuke Sakemi (CIT), Takashi Kanamaru (kougakuin univ), Nina Sviridova (Tokyo Univ. of Science), Kazuyuki Aihara (UTokyo) CAS2022-26 NLP2022-46 |
Echo State Network (ESN) is a machine learning method that consists of an input layer, a layer of recurrent neural netwo... [more] | CAS2022-26 NLP2022-46 pp.35-40 |
| NLP | 2022-08-02 15:30 |
Online | Online | The energy-accuracy trade-off in thresholding Nobumasa Ishida, Yoshihiko Hasegawa (Univ. Tokyo) NLP2022-36 |
In the last decade, stochastic thermodynamics revealed that the accuracy of various information processing, from biologi... [more] | NLP2022-36 pp.39-42 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2022-06-28 09:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
Inside the Ventricle System Immune Cell Network Hypothesis for Language Processing and Intelligence
-- B lymphocytes in the CSF Control Spinal Reflexes and Speech Processing -- Kumon Tokumaru (Researcher) NC2022-10 IBISML2022-10 |
Some patients with idiopathic normal-pressure hydrocephalus develop aphasia or amnesia after shunt operation. This may b... [more] | NC2022-10 IBISML2022-10 pp.80-85 |
| 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 |
| MBE, NC (Joint) |
2021-10-28 11:40 |
Online | Online | Reservoir computing properties of micropatterned neuronal networks in culture Takuma Sumi, Hideaki Yamamoto, Satoshi Moriya, Taiki Takemuro, Tomohiro Konno, Shigeo Sato, Ayumi Hirano-Iwata (Tohoku Univ) NC2021-19 |
In this experiment, we used the reservoir computing model to analyze the information processing properties of micropatte... [more] | NC2021-19 pp.7-10 |
| NLP, MSS (Joint) |
2021-03-15 10:05 |
Online | Online | Analysis of spike time series characteristics of spiking neuron model with rectangular threshold signal Hiroyuki Kawasaki, Yusuke Matsuoka (NIYT) NLP2020-57 |
This Paper considers the spiking neuron with a rectangular threshold signal. The state of this neuron repeats integrate-... [more] | NLP2020-57 pp.11-16 |
| NC, MBE (Joint) |
2021-03-03 15:35 |
Online | Online | A Study on Feature Extraction of signal arrival order using unsupervised learning of the pulsed neuron model Kaya Teramoto, Susumu Kuroyanagi (NIT) NC2020-51 |
For time series information processing using pulsed neuron models, a supervised learning rule is proposed that enables c... [more] | NC2020-51 pp.47-52 |
| NC, NLP (Joint) |
2021-01-21 09:40 |
Online | Online | Reconstruction of Input Signal Using Common Interspike Interval Time Series Ei Miura, Tohru Ikeguchi (TUS) NLP2020-40 |
It is not easy to observe the input signals of neurons compared to the output signals of neurons. For this reason, sev... [more] |
NLP2020-40 pp.1-6 |
| MBE, NC (Joint) |
2020-12-18 15:15 |
Online | Online | A Study on Recall of Temporal Pattern Using Hopfield Network with Pulse-type Hardware Neuron Model Yoshiki Sasaki, Katsutoshi Saeki (Nihon Univ.) NC2020-29 |
Previously, we proposed a pulse-type hardware chaotic neuron model.In this paper, we report on a pulsed neural network t... [more] | NC2020-29 pp.7-12 |
| NLP | 2020-09-10 10:30 |
Online | Online | On Investigation of Invariant Characteristics of Deterministic Nonlinear Dynamical Systems and Marked Point Processes Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2020-22 |
In recent years, various kinds of time series data have been observed. One of the kinds is a marked point process. For e... [more] | NLP2020-22 pp.1-6 |
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