Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

2007 ◽  
Vol 28 (11) ◽  
pp. 1909-1923 ◽  
Author(s):  
Maykel Cruz-Monteagudo ◽  
Humberto González-Díaz ◽  
Guillermín Agüero-Chapín ◽  
Lourdes Santana ◽  
Fernanda Borges ◽  
...  
Keyword(s):  
Free Energy ◽  
Markov Model ◽  
Computational Chemistry ◽  
Biological Systems ◽  
Chemistry Development

scholarly journals PID Control as a Process of Active Inference with Linear Generative Models

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

Nanothermodynamics of metal nanoparticles

2014 ◽  
Vol 5 (7) ◽  
pp. 2605-2624 ◽  
Author(s):  
Zhen Hua Li ◽  
Donald G. Truhlar
Keyword(s):  
Free Energy ◽  
Metal Nanoparticles ◽  
Computational Chemistry ◽  
Structural Stability ◽  
Phase Changes ◽  
Aluminum Nanoparticles ◽  
Energy Functions ◽  
Thermodynamic Characterization

This article presents a perspective on thermodynamic characterization of metal nanoparticles by computational chemistry. Topics emphasized include structural stability, phases, phase changes, and free energy functions of aluminum nanoparticles.

Muscle contraction and free energy transduction in biological systems

Science ◽  
1985 ◽  
Vol 227 (4690) ◽  
pp. 999-1006 ◽  
Author(s):  
E Eisenberg ◽  
T. Hill
Keyword(s):  
Free Energy ◽  
Muscle Contraction ◽  
Biological Systems ◽  
Energy Transduction
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