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Reshaping computational neuropsychiatry beyond synaptopathy.
Brain
January 2025
Department of Child and Adolescent Psychopathology, CHU de Lyon, F-69000 Lyon, France; Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, F-69000 Lyon, France.
Computational neuropsychiatry is a leading discipline to explain psychopathology in terms of neuronal message passing, distributed processing, and belief propagation in neuronal networks. Active Inference (AI) has been one of the ways of representing this dysfunctional signal processing. It involves that all neuronal processing and action selection can be explained by maximizing Bayesian model evidence, or minimizing variational free energy.
View Article and Find Full Text PDFIntroducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models.
Entropy (Basel)
January 2025
Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark.
We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python.
View Article and Find Full Text PDFUnified Analysis of Viscoelasticity and Viscoplasticity Using the Onsager Variational Principle.
Entropy (Basel)
January 2025
Department of Polymer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
This study is the application of the Onsager variational principle to viscoelasticity and viscoplasticity with the minimization of the assumptions which are popularly used in conventional approaches. The conventional approaches assume Kröner-Lee decomposition, incompressible plastic deformation, flowing rule, stress equation and so on. These assumptions have been accumulated by many researchers for a long time and have shown many successful cases.
View Article and Find Full Text PDFExperiment-Guided Refinement of Milestoning Network.
J Chem Theory Comput
January 2025
Qingdao Institute for Theoretical and Computational Sciences, School of Chemistry and Chemical Engineering, Shandong University, Qingdao, Shandong 266237, P.R. China.
Milestoning is an efficient method for calculating rare event kinetics by constructing a continuous-time kinetic network that connects the reactant and product states. Its accuracy depends on both the quality of the underlying force fields and the trajectory sampling. The sampling error can be effectively controlled through various methods.
View Article and Find Full Text PDFHighly accurate real-space electron densities with neural networks.
J Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
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