9 papers
A Conservation Law for Equilibrium Propagation and Coupled Learning
Joshua A. McGinnis, Adam G. Kline, Yoichiro Mori
In this paper we show that the physical learning methods known as coupled learning (CL) and equilibrium propagation (EP) conserve a mass-like quantity in the trainable parameters i…
Coercivity and Local Convergence of Physical Learning in Linear Circuits
Joshua A. McGinnis, Xinbo Li, Yoichiro Mori
Physical learning methods train physical networks to perform computational tasks using only local update rules, exploiting the physics of the system to handle the global transfer o…
Radiating Solitary Waves in an FPUT Lattice with Random Coefficients
Hermen Jan Hupkes, Joshua A. McGinnis, Rik W. S. Westdorp +1
We study the propagation of solitary waves in a Fermi-Pasta-Ulam-Tsingou (FPUT) lattice with small random heterogeneity in the linear spring force. Perturbed by the random environm…
Isochronal Phase Reduction and Speed Correction of a Pulse in a Stochastic Kinematic Model
Joshua A. McGinnis, Xinbo Li, Toshiyuki Ogawa +1
We develop a method for computing the stochastic wave speed of pulse solutions in kinematic equations subject to small stochastic forcing based on the isochronal phase reduction. T…
Strong convergence with error estimates for a stochastic compartmental model of electrophysiology
Wai-Tong Louis Fan, Joshua A. McGinnis, Yoichiro Mori
This paper presents a rigorous mathematical analysis, alongside simulation studies, of a spatially extended stochastic electrophysiology model, the Hodgkin-Huxley model of the squi…
Approximation of (some) FPUT lattices by KdV Equations
Joshua A. McGinnis, J. Douglas Wright
We consider a Fermi-Pasta-Ulam-Tsingou lattice with randomly varying coefficients. We discover a relatively simple condition which when placed on the nature of the randomness allow…