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We consider self-organization and memory formation in a mesoscopic model of an amorphous solid subject to a protocol of random shear confined to a strain range ±ϵ_{max}. We develop proper readout protocols to show that the response of the driven system self-organizes to retain a memory of the strain range, which can be subsequently retrieved. Our findings generalize previous results obtained upon oscillatory driving and suggest that self-organization and memory formation of disordered materials can emerge under more general conditions, such as a disordered system interacting with its fluctuating environment. Self-organization results in a correlation between the dynamics of the system and its environment, providing thereby an elementary mechanism for sensing. We conclude by discussing our results and their potential relevance for the adaptation of simple organisms lacking a brain to changing environments.
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http://dx.doi.org/10.1103/PhysRevLett.134.178203 | DOI Listing |
Front Neural Circuits
July 2025
International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
Dissociated neuronal cultures provide a powerful, simplified model for investigating self-organized prediction and information processing in neural networks. This review synthesizes and critically examines research demonstrating their fundamental computational abilities, including predictive coding, adaptive learning, goal-directed behavior, and deviance detection. A unique contribution of this work is the integration of findings on network self-organization, such as the development of critical dynamics optimized for information processing, with emergent predictive capabilities, the mechanisms of learning and memory, and the relevance of the free energy principle within these systems.
View Article and Find Full Text PDFRecent Pat Nanotechnol
July 2025
Chair for Multicomponent Materials, Department of Materials Science, Kiel University, Kaiserstraße 2, D-24143 Kiel, Germany.
The recent rapid progress in artificial intelligence (AI) and the processing of big data imposes a strong demand to explore novel approaches for robust and efficient hardware solutions. Neuromorphic engineering and brain-inspired electronics take inspiration from biological information pathways in neural assemblies, particularly their fundamental building blocks and organizational principles. In contrast, resistive switching in memristive devices is widely considered an electronic synapse with potential applications in in-memory computing and vector-matrix multiplication.
View Article and Find Full Text PDFInt J Mol Sci
May 2025
Environment and Health Department, Istituto Superiore di Sanitá (Italian NIH), 00161 Rome, Italy.
Dynamic criticality-the balance between order and chaos-is fundamental to genome regulation and cellular transitions. In this study, we investigate the distinct behaviors of gene expression dynamics in MCF-7 breast cancer cells under two stimuli: heregulin (HRG), which promotes cell fate transitions, and epidermal growth factor (EGF), which binds to the same receptor but fails to induce cell-fate changes. We model the system as an open, nonequilibrium thermodynamic system and introduce a convergence-based approach for the robust estimation of information-thermodynamic metrics.
View Article and Find Full Text PDFCereb Cortex
May 2025
The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London WC1N 3AR, United Kingdom.
Cognitive self-organization rests on activity-dependent plasticity to extend the ontogenetic process of neural differentiation and integration of the cerebral cortex in each act of cognition. This account of neurocognitive growth can be formulated in terms of active inference and learning. The organism regulates synaptic connectivity as it seeks its goals actively, through excitatory, feedforward expectancies that manifest its species-specific affordances.
View Article and Find Full Text PDFPhys Rev Lett
May 2025
Sorbonne Université, PMMH, CNRS, ESPCI Paris, Université PSL, Université Paris Cité, France.
We consider self-organization and memory formation in a mesoscopic model of an amorphous solid subject to a protocol of random shear confined to a strain range ±ϵ_{max}. We develop proper readout protocols to show that the response of the driven system self-organizes to retain a memory of the strain range, which can be subsequently retrieved. Our findings generalize previous results obtained upon oscillatory driving and suggest that self-organization and memory formation of disordered materials can emerge under more general conditions, such as a disordered system interacting with its fluctuating environment.
View Article and Find Full Text PDF