Hello, Does this concept of "Free energy principle ( https://en.wikipedia.org/wiki/Bayesian_approaches_to_brain_function#Free_energy )", developed by Karl J. Friston, showed its relevance ? ie is it a fruitful concept in the field of neuroscience and in biology ? Patrick
I can't comment on how valid the model is because I don't understand it but here is an article which may explain it in better detail: The free-energy principle: a rough guide to the brain? http://www.fil.ion.ucl.ac.uk/~karl/The free-energy principle - a rough guide to the brain.pdf
indeed, it is not easy to understand this approach, which seem to derive from the concept of thermodynamics : https://en.wikipedia.org/wiki/Thermodynamic_free_energy Thank for the article Patrick
In classical physics, dynamical equations can be derived from a principle of least action. We can ask the inverse question, which dynamical equations mininimize an action? This question has been addressed by Tonti (and others). http://www.dic.univ.trieste.it/perspage/tonti/DEPOSITO/Nonlinear.pdf http://www.dic.univ.trieste.it/perspage/tonti/DEPOSITO/Rassias.pdf http://www.dic.univ.trieste.it/perspage/tonti/DEPOSITO/Tonti-russi.pdf Here is some related work on energy functions for "self-organizing maps". http://www.ncbi.nlm.nih.gov/pubmed/1606243
Perhaps closer to the technicalities of the article, many forms of approximate inference can be stated using variational language. http://www.merl.com/publications/docs/TR2001-22.pdf Understanding Belief Propagation and its Generalizations Jonathan S. Yedidia, William T. Freeman, and Yair Weiss http://www.cs.princeton.edu/courses/archive/spr06/cos598C/papers/YedidaFreemanWeiss2004.pdf Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms Jonathan S. Yedidia, William T. Freeman, and Yair Weiss https://www.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf Graphical Models, Exponential Families, and Variational Inference Martin J. Wainwright and Michael I. Jordan http://www.cs.berkeley.edu/~jordan/papers/variational-intro.pdf An Introduction to Variational Methods for Graphical Models Michael Jordan, Zoubin Ghahramani, Tomi Jaakkola, Lawrence Saul