---
title: "Activator–Inhibitor Cellular Automata as Reservoirs: Nonlinear Regime Transitions and Computation"
id: "biorxiv-14-nonlinear-regime-transitions-enable-reservoir-computation-in-activator"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-14-nonlinear-regime-transitions-enable-reservoir-computation-in-activator"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.10.750595v1?rss=1"
published_at: "2026-09-13T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Activator–Inhibitor Cellular Automata as Reservoirs: Nonlinear Regime Transitions and Computation
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-14-nonlinear-regime-transitions-enable-reservoir-computation-in-activator
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.10.750595v1?rss=1)
- **Published At:** 2026-09-13T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors introduce a reaction-diffusion–inspired **activator-inhibitor cellular automaton** that functions as a tunable nonlinear medium for information processing rather than only pattern formation. - Two families of local nonlinear activation/relaxation functions are implemented: a continuous sigmoid and a logistic-step relaxer. Each family is controlled by a single family-specific parameter that modulates nonlinear response or relaxation dynamics while keeping neighborhood wiring fixed. - Varying that parameter moves the system through distinct dynamical regimes described as collapsed, structured, saturated, and overshooting. These regimes relate to state-level diversity and pattern compressibility. - The authors treat **reservoir computing** as a diagnostic of the untrained medium’s computational properties, testing for fading memory, input separability, and downstream readout learning in each regime. - Across parameter sweeps, stronger memory and separability cluster near regime transition regions rather than being uniformly distributed across parameter space. - The work identifies activator-inhibitor cellular automata as interpretable unconventional reservoir substrates where effective gain, threshold, and relaxation tune the balance between **pattern formation**, **memory**, and **separability**. - The results support the broader idea that tissue-like or physical pattern-forming media can, in principle, switch between patterning and information-processing behaviors by modulating local nonlinear dynamics. - No competing interests were declared. Funding was provided by the Medical Research Council (MRC) as reported. Data and code availability are referenced in the source.
## Clinical Analysis & Structured Key Points
Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata | bioRxiv Skip to main content New Results Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata View ORCID Profile Jurgen Riedel , View ORCID Profile Chris P. Barnes , View ORCID Profile Alexey Zaikin doi: https://doi.org/10.64898/2026.09.10.750595 Jurgen Riedel University College London Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jurgen Riedel For correspondence: jurgen.riedel{at}gmail.com Chris P. Barnes University College London Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chris P. Barnes Alexey Zaikin University College London Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexey Zaikin Abstract Info/History Metrics Data/Code Preview PDF Abstract Activator-inhibitor systems are usually studied as pattern-forming media, but the same local nonlinear interactions can also shape how information is stored and separated over time. Here we first introduce a reaction-diffusion-inspired cellular automaton as a tunable nonlinear medium with two activation families, a continuous sigmoid and a logistic-step relaxer. In each case, a single family-specific parameter changes the nonlinear response or relaxation dynamics while leaving the neighbourhood wiring fixed. This provides a controlled way to move the activator-inhibitor system between collapsed, structured, saturated, and overshooting regimes, and to relate these regimes to state-level diversity and pattern compressibility. We then use reservoir computing to test whether these tuned pattern-forming regimes support fading memory, input separability, and downstream readout learning. In this role, reservoir computing is not treated as an architecture to be optimised, but as a diagnostic of the computational properties of the untrained medium. Across parameter sweeps, stronger memory and separability are concentrated near transition regions rather than distributed uniformly across parameter space. These results identify activator-inhibitor cellular automata as interpretable unconventional reservoir substrates in which effective gain, threshold, and relaxation parameters tune the balance between pattern formation, memory, and separability. More broadly, they support the view that tissue-like or physical pattern-forming media may, in principle, shift between patterning and information-processing regimes by modulating local nonlinear dynamics. Competing Interest Statement The authors have declared no competing interest. Footnotes https://doi.org/10.5281/zenodo.19458106 Funder Information Declared Medical Research Council (MRC) , MR/R02524X/1 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted September 13, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata Jurgen Riedel , Chris P. Barnes , Alexey Zaikin bioRxiv 2026.09.10.750595; doi: https://doi.org/10.64898/2026.09.10.750595 Share This Article: Copy Citation Tools Nonlinear regime transitions enable reservoir computation in activator-inhibitor cellular automata Jurgen Riedel , Chris P. 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