Entropy, free energy, symmetry and dynamics in the brain - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Physics: Complexity Année : 2022

Entropy, free energy, symmetry and dynamics in the brain

Viktor Jirsa
Hiba Sheheitli

Résumé

Neuroscience is home to concepts and theories with roots in a variety of domains including information theory, dynamical systems theory, and cognitive psychology. Not all of those can be coherently linked, some concepts are incommensurable, and domain-specific language poses an obstacle to integration. Still, conceptual integration is a form of understanding that provides intuition and consolidation, without which progress remains unguided. This paper is concerned with the integration of deterministic and stochastic processes within an information theoretic framework, linking information entropy and free energy to mechanisms of emergent dynamics and self-organization in brain networks. We identify basic properties of neuronal populations leading to an equivariant matrix in a network, in which complex behaviors can naturally be represented through structured flows on manifolds establishing the internal model relevant to theories of brain function. We propose a neural mechanism for the generation of internal models from symmetry breaking in the connectivity of brain networks. The emergent perspective illustrates how free energy can be linked to internal models and how they arise from the neural substrate
Fichier principal
Vignette du fichier
Jirsa_2022_J._Phys._Complex._3_015007.pdf (2.73 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03572631 , version 1 (14-02-2022)

Identifiants

Citer

Viktor Jirsa, Hiba Sheheitli. Entropy, free energy, symmetry and dynamics in the brain. Journal of Physics: Complexity, 2022, 3 (1), pp.015007. ⟨10.1088/2632-072x/ac4bec⟩. ⟨hal-03572631⟩
35 Consultations
41 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More