Multi-dimensional memory in low-friction granular materials

To explore what features of multi-dimensional training can be remembered in granular materials, the response of a small, two-dimensional packing of hydrogel spheres to two independent types of shear is measured. Packings are trained <i>via</i> the application of several identical shear cycles, either of a single shear type or combinations of the two types. The memory is then read out using a standard protocol capable of revealing memories as a cusp at the point where readout reaches the training strain. The ability to read out a memory is sensitive not only to the type of deformation applied but also to the order in which different types of training are performed, underscoring the importance of thinking of memories not as single remembered amplitude but as a learned path through phase space. Moreover, while memory capacity (one or multiple) and type of memory (direction, amplitude, duration) have become standard ways of classifying memories, the non-abelian nature of the readout process suggests an additional axis to consider.

Authors

Institutions

Publication Details

Journal
Soft Matter
Published
2025-01-01
DOI
https://doi.org/10.1039/d5sm00470e
Primary Topic
Adhesion, Friction, and Surface Interactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-dimensional memory in low-friction granular materials

Chloe W. Lindeman
Soft Matter
Adhesion, Friction, and Surface Interactions
article

Multi-dimensional memory in low-friction granular materials

Chloe W. Lindeman
article en

Abstract

To explore what features of multi-dimensional training can be remembered in granular materials, the response of a small, two-dimensional packing of hydrogel spheres to two independent types of shear is measured. Packings are trained <i>via</i> the application of several identical shear cycles, either of a single shear type or combinations of the two types. The memory is then read out using a standard protocol capable of revealing memories as a cusp at the point where readout reaches the training strain. The ability to read out a memory is sensitive not only to the type of deformation applied but also to the order in which different types of training are performed, underscoring the importance of thinking of memories not as single remembered amplitude but as a learned path through phase space. Moreover, while memory capacity (one or multiple) and type of memory (direction, amplitude, duration) have become standard ways of classifying memories, the non-abelian nature of the readout process suggests an additional axis to consider.

Soft MatterVol. 21(24)
Fermi National Accelerator Laboratory (US), University of Chicago (US)
National Science Foundation, Materials Research Science and Engineering Center, Harvard University, Division of Materials Research
Sustainable cities and communities
Openalex Percentile: Top 87%
Adhesion, Friction, and Surface Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Multi-dimensional memory in low-friction granular materials — Chloe W. Lindeman · Soft Matter (2025) | TGRS Research Map | TGRS