A Note on the Conceptual Continuity of Hormonally Modulated Memory Architectures in Artificial Intelligence

AbstractIn recent years, the field of artificial intelligence—particularly in the context of modular andagentic architectures—has seen a surge of interest in biologically inspired mechanisms ofregulation, memory, and adaptation. Several recent works propose architectures based onthe hormonal modulation of agent behavior, multi-level memory systems, and globalregulatory signals.The objective of this brief note is to clearly and correctly clarify the conceptual connectionsbetween these approaches and a previously published functional framework thatsystematically integrated and formally derived these elements. This text does not seek toquestion the originality of newer proposals, but rather to refine their theoretical groundingand to support transparent citation practices.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-09
DOI
https://doi.org/10.5281/zenodo.18196036
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
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article

A Note on the Conceptual Continuity of Hormonally Modulated Memory Architectures in Artificial Intelligence

Michal Seidl
Zenodo (CERN European Organization for Nuclear Research)
Evolutionary Algorithms and Applications
article

A Note on the Conceptual Continuity of Hormonally Modulated Memory Architectures in Artificial Intelligence

Michal Seidl
article en

Abstract

AbstractIn recent years, the field of artificial intelligence—particularly in the context of modular andagentic architectures—has seen a surge of interest in biologically inspired mechanisms ofregulation, memory, and adaptation. Several recent works propose architectures based onthe hormonal modulation of agent behavior, multi-level memory systems, and globalregulatory signals.The objective of this brief note is to clearly and correctly clarify the conceptual connectionsbetween these approaches and a previously published functional framework thatsystematically integrated and formally derived these elements. This text does not seek toquestion the originality of newer proposals, but rather to refine their theoretical groundingand to support transparent citation practices.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 92%
Evolutionary Algorithms and Applications
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