arXiv subjects

Yuichiro Terasaki

Publications and source records attributed to Yuichiro Terasaki.

2 recordsLinked to original sources

arXiv2026

Programming Spintronic Reservoir Computing

We present a programming framework for a spintronic reservoir computer (RC) that maps prescribed input-output relationships directly onto the readout layer, bypassing conventional data-driven black-box approaches. Our spintronic RC is based on magnetoresistive random-access memory and exploits magnetization dynamics for computation. We introduce a general metric that quantifies the system's programmability and reveals how the governing equations and system parameters constrain the class of realizable functions. We then construct externally controllable readout layers by exploiting the explicit parameter dependence of the prescribed equations. This metric and construction enable programming explicit functions on the spintronic RC, indicating a potential route to in-memory computing. Our demonstrations include neural-network emulation, bifurcation embedding, and a Newton solver for fifth-order algebraic equations. In addition, we prove the universal approximation property of the spintronic RC in the limit of infinite system size and input duration, and show its consistency with programmability.

cs.ET↗

arXiv2025

Thermodynamic limit in learning period three

A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? In this paper, we report the answer to be yes. Considering a random neural network in its thermodynamic limit, we first show that almost all learned periods are unstable, and each network has its own characteristic attractors (which can even be untrained ones). The latently acquired dynamics, which are unstable within the trained network, serve as a foundation for the diversity of characteristic attractors and may even lead to the emergence of attractors of all periods after learning. When the neural network interpolation is quadratic, a universal post-learning bifurcation scenario appears, which is consistent with a topological conjugacy between the trained network and the classical logistic map. In addition to universality, we explore specific properties of certain networks, including the singular behavior of the scale of weight at the infinite limit, the finite-size effects, and the symmetry in learning period three.

stat.ML↗