Results 61 to 70 of about 1,625,671 (139)

On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

Entropy, 2021
Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents).
Magnus T. Koudahl, Wouter M. Kouw, Bert de Vries   +2 more
doaj   +1 more source

Statistical mechanics of scale-free networks at a critical point: Complexity without irreversibility?

, 2005
Based on a rigorous extension of classical statistical mechanics to networks, we study a specific microscopic network Hamiltonian. The form of this Hamiltonian is derived from the assumption that individual nodes increase/decrease their utility by ...
Christoly Biely, I. Farkas, J. E. Ingersoll, J. von Neumann, P. Erdős, S. Dorogovtsev, Stefan Thurner   +6 more
core   +1 more source

The electrochemical-step asymmetry index

MethodsX, 2021
The development of oxygen-evolution reaction (OER) electrocatalysts has been spurred by thermodynamic considerations on the free-energy landscape. Most commonly, electrocatalytic activity is approximated by the analysis of the free-energy changes among ...
Kai S. Exner
doaj   +1 more source

Experiential fantasies, prediction, and enactive minds [PDF]

, 2015
A recent surge of work on prediction-driven processing models--based on Bayesian inference and representation-heavy models--suggests that the material basis of conscious experience is inferentially secluded and neurocentrically ...
Kirchhoff, Michael David
core  

A Factor Graph Description of Deep Temporal Active Inference

Frontiers in Computational Neuroscience, 2017
Active inference is a corollary of the Free Energy Principle that prescribes how self-organizing biological agents interact with their environment. The study of active inference processes relies on the definition of a generative probabilistic model and a
Bert de Vries, Bert de Vries, Karl J. Friston   +2 more
doaj   +1 more source

Quenched Point-to-Point Free Energy for Random Walks in Random Potentials [PDF]

, 2013
We consider a random walk in a random potential on a square lattice of arbitrary dimension. The potential is a function of an ergodic environment and some steps of the walk.
Rassoul-Agha, Firas, Seppäläinen, Timo
core  

Information heat engine: converting information to energy by feedback control

, 2010
In 1929, Leo Szilard invented a feedback protocol in which a hypothetical intelligence called Maxwell's demon pumps heat from an isothermal environment and transduces it to work.
BJ Lopez, C Bustamante, C Jarzynski, C. E. Shannon, CH Bennett, DA Leigh, DJ Evans, DM Carberry, Eiro Muneyuki, G Gallavotti, G Jourdan, GE Crooks, GM Wang, HS Leff, J Liphardt, JC Doyle, JJ Thorn, K Maruyama, K Sekimoto, L Szilárd, Masahito Ueda, Masaki Sano, P Hänggi, R Kawai, R Landauer, RA van Delden, S Toyabe, Shoichi Toyabe, T Sagawa, T Sagawa, T Sagawa, T Watanabe-Nakayama, Takahiro Sagawa, TM Cover, V Serreli   +34 more
core   +1 more source

The Universal Optimism of the Self-Evidencing Mind

Entropy
Karl Friston’s free-energy principle casts agents as self-evidencing through active inference. This implies that decision-making, planning and information-seeking are, in a generic sense, ‘wishful’.
Elizabeth L. Fisher, Jakob Hohwy
doaj   +1 more source

Variational Methods for Biomolecular Modeling

, 2016
Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs).
A Ivankin, A Jaramillo, A Warshel, AB Camley, B Lee, B Li, B Li, B Li, B Lu, B Lu, B Wang, BJ Reynwar, BR Brooks, C Mim, D Marsh, D Spoel Van Der, DG Thomas, DL Parton, E Gallicchio, E Gallicchio, EA Evans, F Campelo, FH Stillinger, FM Richards, FS Cohen, G Archontis, G-W Wei, G-W Wei, G-W Wei, GP Leser, GW Wei, HT McMahon, HY Lin, I Massova, I Park, IR Cooke, J Dzubiella, J Faraudo, J Gumbart, J Strain, J Wagoner, J Zimmerberg, JA Grant, JA Grant, JA Grant, JA Wagoner, JC Phillips, JC Stachowiak, JD Madura, JE Rothman, JMJ Swanson, K Farsad, K Lum, K Simons, KA Sharp, KM Callenberg, KS Kim, L-T Gao, LJ Pike, M Daily, M Feig, MD Huang, ME Davis, N Choudhury, N Gavish, NA Baker, NW Schmidt, O Sten-Knudsen, P Concus, P Grochowski, PB Canham, PW Bates, PW Bates, Q Du, Q Du, Q Du, Q Zheng, Q Zheng, R Brewster, R Brewster, R Parthasarathy, RA Pierotti, RE Skyner, RJ Ryham, S Choe, S Dai, S Li, S Osher, S Xu, S Zhao, S Zhao, SC Brenner, T Baumgart, T Kirchhausen, T Simonson, T Sollner, T Takahashi, T Witkowski, T-L Horng, V Luzhkov, VB Chu, W Geng, X Chen, X Chen, Y Tang, Y Wang, Y Wang, Y Wang, Y Zhao, YC Zhou, YC Zhou, Z Chen, Z Chen, Z Chen, ZY Yu   +114 more
core   +1 more source

Second gradient viscoelastic fluids: dissipation principle and free energies [PDF]

Meccanica, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Amendola, G., Fabrizio, M., Golden, John Murrough   +2 more
openaire   +4 more sources