Prof. Robert Stamps
Department of Physics and Astronomy
University of Manitoba
Host: Lance DeLong and Todd Hastings
Title: Spin Vision: Using Artificial Spin Ice to Study Complex Systems
Abstract: Artificial Spin Ice is the name given to a class of metamaterials that are used as models for frustrated systems. In these models, frustration is introduced through competing interactions in a mesoscopic-scale array of interacting particles. Their experimental realization using nanomagnets as the interacting particles was first demonstrated in 2006 and opened a new field of study for frustrated systems. [1] As models in experiment, Artificial Spin Ice facilitate detailed observation in real time with high spatial resolution of complex out of equilibrium dynamics and thermodynamic processes. This flexibility underwrites their use in fundamental studies of a variety of nonlinear dynamics and ordering phenomena in low dimensions.
Recent advances in fabrication now enable creation of complex two- and three-dimensional structures that have opened new possibilities for structural design and exciting new potentials for application in practical devices. [2] In this talk, a new cross-discipline direction for research that can benefit from Artificial Spin Ice models is explored. Motivated by studies of neural network models used in the analysis of experiments on primate vision, [3,4] a new type of three-dimensional Artificial Spin Ice geometry is proposed for implementation of a biologically plausible neuroscientific model called ‘active inference’. [5] Cast in the form of nanomagnetic spin geometries that can be studied experimentally, the approach can be used to facilitate a physics-based understanding of how complex systems might spontaneously generate a type of Bayesian filtering.
To achieve this, novel mechanisms for the control of magnetic states and ordering processes using three-dimensional geometries are required and different strategies are discussed. [6,7] A rudimentary ‘smart ASI’ is described whose design is based on state optimization principles assumed in some models used to describe general neurological processes.
- RF Wang et al., Nature 439, 303 (2006).
- SH Skjærvø, CH Marrows, RL Stamps, LJ Heyderman, Nature Reviews Physics 2, 13 (2020).
- M Falconbridge, RL Stamps, DR Badcock, Neural Computation 18, 415 (2006).
- M Falconbridge, RL Stamps, M Edwards, DR Badcock, i-Perception (in press).
- RA Adams, E Aponte, L Marshall, KJ Friston, J. Neuroscience Methods 242, 1 (2015).
- VM Parakkat, GM Macauley, RL Stamps, KM Krishnan, Physical Review Letters 126, 017203 (2021).
- RB Popy, J Frank, RL Stamps, Journal of Applied Physics 132, 133902 (2022).