BUILDING A MINIMUM FRUSTRATION FRAMEWORK FOR BRAIN FUNCTIONS IN LONG TIMESCALES

Arturo Tozzi 1, Tor Flå2, James F. Peters3

 

1 Center for Nonlinear Science, University of North Texas

1155 Union Circle, #311427 Denton, TX 76203-5017 USA

 

2 Department of Mathematics and Statistics, Centre for Theoretical and Computational Chemistry, UiT, The Arctic University of Norway, N-9037 Tromsø, Norway

 

3 Department of Electrical and Computer Engineering, University of Manitoba

75A Chancellor’s Circle Winnipeg, MB R3T 5V6 CANADA and

Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey

 

 

 

 

 

The minimum frustration principle is a computational approach which states that, in the long timescales of evolution, proteins’ free-energy decreases more than expected by thermodynamical contraints as their aminoacids assume conformations progressively closer to the lowest energetic state.  Here we show that this general principle, borrowed from protein folding dynamics, can be fruitfully applied to nervous function too.  Highligting the foremost role of energetic requirements, macromolecular dynamics, and, above all, intertwined timescales in brain activity, the minimum frustration principle elucidates a wide range of mental processes, from sensations to memory retrieval.  Brain functions are compared to trajectories which, in long nervous timescales, are attracted towards the low-energy bottom of funnel-like structures characterized both by robustness and plasticity.  We discuss how the principle, as derived explicitly from evolution and selection of a funneling structure from microdynamics of contacts, is different from other brain models equipped with energy landscapes, such as the Bayesian and free-energy principle and the Hopfield networks. In sum, we make available a novel approach to brain function cast in a biologically informed fashion, with the potential to be operationalized and assessed empirically.

 

 

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