420 citations · 1.5k across the 35 of their papers we have counts for
35 papers
Input-driven chaotic dynamics in vortex spin-torque oscillator
Yusuke Imai, Kohei Nakajima, Sumito Tsunegi +1
A new research topic in spintronics relating to the operation principles of brain-inspired computing is input-driven magnetization dynamics in nanomagnet. In this paper, the magnet…
Embedding bifurcations into pneumatic artificial muscle
Nozomi Akashi, Yasuo Kuniyoshi, Taketomo Jo +4
Harnessing complex body dynamics has been a long-standing challenge in robotics. Soft body dynamics is a typical example of high complexity in interacting with the environment. An…
Computational capability for physical reservoir computing using a spin-torque oscillator with two free layers
Terufumi Yamaguchi, Sumito Tsunegi, Kohei Nakajima +1
A numerical analysis on the computational capability of physical reservoir computing utilizing a spin-torque oscillator with two free layers is reported. Conventional spintronics d…
Self-Organized Criticality Explains Readiness Potential
Katsushi Kagaya, Tomoyuki Kubota, Kohei Nakajima
Readiness potential is a widely observed brain activity in several species including crayfish before the spontaneous behavioral initiation. However, it is poorly understood how thi…
Quantum-Classical Hybrid Information Processing via a Single Quantum System
Quoc Hoan Tran, Sanjib Ghosh, Kohei Nakajima
Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologie…
Quantum Noise-Induced Reservoir Computing
Tomoyuki Kubota, Yudai Suzuki, Shumpei Kobayashi +3
Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the…
Composite FORCE learning of chaotic echo state networks for time-series prediction
Yansong Li, Kai Hu, Kohei Nakajima +1
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output…
Physical Deep Learning with Biologically Plausible Training Method
Mitsumasa Nakajima, Katsuma Inoue, Kenji Tanaka +3
The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation de…
Transient Chaos in BERT
Katsuma Inoue, Soh Ohara, Yasuo Kuniyoshi +1
Language is an outcome of our complex and dynamic human-interactions and the technique of natural language processing (NLP) is hence built on human linguistic activities. Bidirecti…
Learning Temporal Quantum Tomography
Quoc Hoan Tran, Kohei Nakajima
Quantifying and verifying the control level in preparing a quantum state are central challenges in building quantum devices. The quantum state is characterized from experimental me…
Step-like dependence of memory function on pulse width in spintronics reservoir computing
Terufumi Yamaguchi, Nozomi Akashi, Kohei Nakajima +3
Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation betwee…
Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
Keisuke Fujii, Kohei Nakajima
Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Qu…
Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
Takahiro Goto, Quoc Hoan Tran, Kohei Nakajima
Encoding classical data into quantum states is considered a quantum feature map to map classical data into a quantum Hilbert space. This feature map provides opportunities to incor…
Periodic structure of memory function in spintronics reservoir with feedback current
Terufumi Yamaguchi, Nozomi Akashi, Sumito Tsunegi +3
The role of the feedback effect on physical reservoir computing is studied theoretically by solving the vortex-core dynamics in a nanostructured ferromagnet. Although the spin-tran…
Higher-Order Quantum Reservoir Computing
Quoc Hoan Tran, Kohei Nakajima
Quantum reservoir computing (QRC) is an emerging paradigm for harnessing the natural dynamics of quantum systems as computational resources that can be used for temporal machine le…
Physical reservoir computing -- An introductory perspective
Kohei Nakajima
Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing…
Designing spontaneous behavioral switching via chaotic itinerancy
Katsuma Inoue, Kohei Nakajima, Yasuo Kuniyoshi
Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-a…
Computing with vortices: Bridging fluid dynamics and its information-processing capability
Ken Goto, Kohei Nakajima, Hirofumi Notsu
Herein, the Karman vortex system is considered to be a large recurrent neural network, and the computational capability is numerically evaluated by emulating nonlinear dynamical sy…
Optimal short-term memory before the edge of chaos in driven random recurrent networks
Taichi Haruna, Kohei Nakajima
The ability of discrete-time nonlinear recurrent neural networks to store time-varying small input signals is investigated by mean-field theory. The combination of a small input st…
Chaos in nanomagnet via feedback current
Tomohiro Taniguchi, Nozomi Akashi, Hirofumi Notsu +3
Nonlinear magnetization dynamics excited by spin-transfer effect with feedback current is studied both numerically and analytically. The numerical simulation of the Landau-Lifshitz…
A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems
Tomoyuki Kubota, Hirokazu Takahashi, Kohei Nakajima
A dynamical system can be regarded as an information processing apparatus that encodes input streams from the external environment to its state and processes them through state tra…
Information Processing Capability of Soft Continuum Arms
Estefany A. Torres, Kohei Nakajima, Isuru S. Godage
Soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compl…
Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid
Makoto Negoro, Kosuke Mitarai, Keisuke Fujii +2
We experimentally demonstrate quantum machine learning using NMR based on a framework of quantum reservoir computing. Reservoir computing is for exploiting natural nonlinear dynami…
Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions
Taishi Furuta, Keisuke Fujii, Kohei Nakajima +4
The figures-of-merit for reservoir computing (RC), using spintronics devices called magnetic tunnel junctions (MTJs), are evaluated. RC is a type of recurrent neural network. The i…