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LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS

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Bibliographic Details
Main Author:	Researcher
Format:	Recurso digital
Language:	English
Published:	Zenodo 2025
Subjects:	Liquid-brain networks, lifelong learning, recurrent attention, non-stationary environments, neural plasticity, biological AI
Online Access:	Acceder al recurso
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_version_	1866901529610420224
author	Researcher
author_facet	Researcher
contents	<p><span lang="EN-US">The Liquid-Brain Network (LBN) paradigm draws from biological principles of neural plasticity and real-time adaptation to address challenges posed by non-stationary learning environments. Inspired by neurodynamic plasticity and recurrent attention mechanisms, this architecture facilitates lifelong learning, efficient memory representation, and adaptive reasoning. The architecture dynamically encodes temporal patterns through variable neural states, enabling continuous learning and generalization without catastrophic forgetting.</span></p> <p><span lang="EN-US">This paper proposes a comprehensive framework that integrates time-varying neuron states with recurrent attention modules, enhancing the model’s ability to react, adapt, and store evolving patterns. Benchmarking results highlight LBN's superior performance in energy efficiency, memory retention, and adaptability compared to classical recurrent and spiking models. Furthermore, visual illustrations, graphs, and comparative tables demonstrate its operational advantage for smart autonomous systems.</span></p>
format	Recurso digital
id	zenodo_https___doi_org_10_5281_zenodo_15647125
institution	Zenodo
language	eng
publishDate	2025
publisher	Zenodo
record_format	zenodo
spellingShingle	LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS Researcher Liquid-brain networks, lifelong learning, recurrent attention, non-stationary environments, neural plasticity, biological AI <p><span lang="EN-US">The Liquid-Brain Network (LBN) paradigm draws from biological principles of neural plasticity and real-time adaptation to address challenges posed by non-stationary learning environments. Inspired by neurodynamic plasticity and recurrent attention mechanisms, this architecture facilitates lifelong learning, efficient memory representation, and adaptive reasoning. The architecture dynamically encodes temporal patterns through variable neural states, enabling continuous learning and generalization without catastrophic forgetting.</span></p> <p><span lang="EN-US">This paper proposes a comprehensive framework that integrates time-varying neuron states with recurrent attention modules, enhancing the model’s ability to react, adapt, and store evolving patterns. Benchmarking results highlight LBN's superior performance in energy efficiency, memory retention, and adaptability compared to classical recurrent and spiking models. Furthermore, visual illustrations, graphs, and comparative tables demonstrate its operational advantage for smart autonomous systems.</span></p>
title	LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS
topic	Liquid-brain networks, lifelong learning, recurrent attention, non-stationary environments, neural plasticity, biological AI
url	https://doi.org/10.5281/zenodo.15647125