Abstract
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.
Highlights
Predictive coding is one of the most influential contemporary theories of brain function [1,2,3]
Internal models in predictive coding theories use simple models, which are difficult to generalize to more complex behaviors
In the context of the large scale brain network models, we demonstrated that the symmetry breaking via the connectome qualifies as a candidate mechanism underlying the emergence of these features, thereby establishing the link to biophysical processes such as Hebbian learning and other plasticity mechanisms
Summary
Aix Marseille Universite, Institut National de la Sante et de la Recherche Medicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
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