21 citations · 33 across the 10 of their papers we have counts for
11 papers
Learning Reservoir Dynamics with Temporal Self-Modulation
Yusuke Sakemi, Sou Nobukawa, Toshitaka Matsuki +2
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as…
Hibikino-Musashi@Home 2022 Team Description Paper
Tomoya Shiba, Tomohiro Ono, Shoshi Tokuno +27
Our team, Hibikino-Musashi@Home (HMA), was founded in 2010. It is based in Japan in the Kitakyushu Science and Research Park. Since 2010, we have annually participated in the RoboC…
Hibikino-Musashi@Home 2018 Team Description Paper
Yutaro Ishida, Sansei Hori, Yuichiro Tanaka +14
Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCu…
A Neural Network Model of the Entorhinal Cortex and Hippocampus for Event-order Memory Processing
Hiroki Nakagawa, Katsumi Tateno, Kensuke Takada +1
To solve a navigation task based on experiences, we need a mechanism to associate places with objects and to recall them along the course of action. In a reward-oriented task, if t…
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks
Yusuke Sakemi, Takashi Morie, Takeo Hosomi +1
The spiking neural network (SNN) has been attracting considerable attention not only as a mathematical model for the brain, but also as an energy-efficient information processing m…
Hibikino-Musashi@Home 2019 Team Description Paper
Yuichiro Tanaka, Yutaro Ishida, Yushi Abe +12
Our team, Hibikino-Musashi@Home (HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. Since 2010, we have participated in the RoboCup@Home Jap…
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
Yusuke Sakemi, Kai Morino, Takashi Morie +1
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machi…
An Efficient Hardware-Oriented Dropout Algorithm
Yoeng Jye Yeoh, Takashi Morie, Hakaru Tamukoh
This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting o…
An Energy-efficient Time-domain Analog VLSI Neural Network Processor Based on a Pulse-width Modulation Approach
Masatoshi Yamaguchi, Goki Iwamoto, Hakaru Tamukoh +1
A time-domain analog-weighted-sum calculation model based on a pulse-width modulation (PWM) approach is proposed. The proposed calculation model can be applied to any types of netw…
A Time-domain Analog Weighted-sum Calculation Model for Extremely Low Power VLSI Implementation of Multi-layer Neural Networks
Quan Wang, Hakaru Tamukoh, Takashi Morie
A time-domain analog weighted-sum calculation model is proposed based on an integrate-and-fire-type spiking neuron model. The proposed calculation model is applied to multi-layer f…
Depth-Based Visual Servoing Using Low-Accurate Arm
Ludovic Hofer, Michio Tanaka, Hakaru Tamukoh +2
This paper proposes a visual-servoing method dedicated to grasping of daily-life objects. In order to obtain an affordable solution, we use a low-accurate robotic arm. Our method c…