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Capturing dynamical correlations using implicit neural representations.

ABSTRACT: Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La₂NiO₄, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

SUBMITTER: Chitturi SR

PROVIDER: S-EPMC10511537 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Capturing dynamical correlations using implicit neural representations.

Chitturi Sathya R SR Ji Zhurun Z Petsch Alexander N AN Peng Cheng C Chen Zhantao Z Plumley Rajan R Dunne Mike M Mardanya Sougata S Chowdhury Sugata S Chen Hongwei H Bansil Arun A Feiguin Adrian A Kolesnikov Alexander I AI Prabhakaran Dharmalingam D Hayden Stephen M SM Ratner Daniel D Jia Chunjing C Nashed Youssef Y Turner Joshua J JJ

Nature communications 20230920 1

Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are ...[more]

PMID: 37730824

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Project description:BackgroundThere is evidence that slow wave sleep (SWS) promotes the consolidation of memories that are subserved by mediotemporal- and hippocampo-cortical neural networks. In contrast to implicit memories, explicit memories are accompanied by conscious (attentive and controlled) processing. Awareness at pre-sleep encoding has been recognized as critical for the off-line memory consolidation. The present study elucidated the role of task-dependent cortical activation guided by attentional control at pre-sleep encoding for the consolidation of hippocampus-dependent memories during sleep.MethodologyA task with a hidden regularity was used (Number Reduction Task, NRT), in which the responses that can be implicitly predicted by the hidden regularity activate hippocampo-cortical networks more strongly than responses that cannot be predicted. Task performance was evaluated before and after early-night sleep, rich in SWS, and late-night sleep, rich in rapid eye movement (REM) sleep. In implicit conditions, slow cortical potentials (SPs) were analyzed to reflect the amount of controlled processing and the localization of activated neural task representations.Principal findingsDuring implicit learning before sleep, the amount of controlled processing did not differ between unpredictable and predictable responses, nor between early- and late-night sleep groups. A topographic re-distribution of SPs indicating a spatial reorganization occurred only after early, not after late sleep, and only for predictable responses. These SP changes correlated with the amount of SWS and were covert because off-line RT decrease did not differentiate response types or sleep groups.ConclusionsIt is concluded that SWS promotes the neural reorganization of task representations that rely on the hippocampal system despite absence of conscious access to these representations.SignificanceOriginal neurophysiologic evidence is provided for the role of SWS in the consolidation of memories encoded with hippocampo-cortical interaction before sleep. It is demonstrated that this SWS-mediated mechanism does not depend critically on explicitness at learning nor on the amount of controlled executive processing during pre-sleep encoding.

Dataset Information

Capturing dynamical correlations using implicit neural representations.

Publications

Capturing dynamical correlations using implicit neural representations.

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