Relevance of Karl J. Friston's Free Energy Principle

AI Thread Summary
The discussion centers on the relevance of the Free Energy Principle, developed by Karl J. Friston, in neuroscience and biology. Participants express uncertainty about the model's validity due to its complexity, which is rooted in thermodynamics. Resources are shared to provide clearer explanations, including a rough guide to the brain and various academic papers that explore related concepts such as dynamical equations and self-organizing maps. The conversation highlights the principle's potential implications for understanding brain function and the challenges in grasping its intricacies. Overall, the Free Energy Principle is positioned as a significant yet complex framework in the study of brain dynamics and biological systems.
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Ryan_m_b said:
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:
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
 
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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
 
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