A grapeful discovery: Reservoir computing with wine beads

In this work, we introduce encapsulated wine beads as a novel, edible material for unconventional and neuromorphic computing. When encapsulated in alginate beads, wine, a complex mixture of proteins, organic acids, sugars, metal ions, and volatiles, exhibits nonlinear electrical behavior and memory effects governed by ox-redox processes. These responses show plasticity-like features, allowing programmable resistance states. The wine beads can be leveraged for computing, and to demonstrate this potential, the wine bead was used as a single-node reservoir for classification tasks. Our findings indicate that the resistance is programmable, exhibits a high degree of repeatability, and can be used for reservoir computing scenarios.

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Publication Details

Journal
PNAS Nexus
Published
2025-10-22
DOI
https://doi.org/10.1093/pnasnexus/pgaf335
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.48
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article

A grapeful discovery: Reservoir computing with wine beads

Raphael Fortulan, Andrew Adamatzky, Noushin Raeisi Kheirabadi, Alessandro Chiolerio
1 citations
PNAS Nexus
Neural Networks and Reservoir Computing
2.48
article

A grapeful discovery: Reservoir computing with wine beads

Raphael Fortulan, Andrew Adamatzky, Noushin Raeisi Kheirabadi, Alessandro Chiolerio
article en
1 citations

Abstract

In this work, we introduce encapsulated wine beads as a novel, edible material for unconventional and neuromorphic computing. When encapsulated in alginate beads, wine, a complex mixture of proteins, organic acids, sugars, metal ions, and volatiles, exhibits nonlinear electrical behavior and memory effects governed by ox-redox processes. These responses show plasticity-like features, allowing programmable resistance states. The wine beads can be leveraged for computing, and to demonstrate this potential, the wine bead was used as a single-node reservoir for classification tasks. Our findings indicate that the resistance is programmable, exhibits a high degree of repeatability, and can be used for reservoir computing scenarios.

PNAS NexusVol. 4(11)
University of Huddersfield (GB), University of the West of England (GB), Italian Institute of Technology (IT)
Openalex Percentile: Top 8%
Neural Networks and Reservoir Computing
2.48
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