A Free Energy Principle for Biological Systems
TL;DR: A free energy principle is described that tries to explain the ability of biological systems to resist a natural tendency to disorder using a principle of least action based on variational free energy (from statistical physics) and the conditions under which it is formally equivalent to the information bottleneck method.
read more
Abstract: This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Laying down a forking path: Tensions between enaction and the free energy principle
TL;DR: In this paper , a series of misreadings and misinterpretations of key enactive concepts are identified, including the conflation of processes of self-distinction in operationally closed systems and Markov blankets, which are incompatible with the time invariance of non-equilibrium steady states assumed by the FEP.
Deep learning and cognitive science.
Pietro Perconti,Alessio Plebe +1 more
TL;DR: It will be argued that it is time for cognitive science to seriously come to terms with deep learning, and the reasons why this is the case are spelled out.
71
PID control as a process of active inference with linear generative models
TL;DR: This work 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.
69
Future climates: Markov blankets and active inference in the biosphere
TL;DR: The Gaia hypothesis about the Earth climate system is formalized using advances in theoretical biology based on the minimization of variational free energy and underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.
66
Deep Active Inference
TL;DR: In this paper, the authors combine the free energy principle and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the deep active inference agent, which minimises a variational free energy bound on the average surprise of its sensations, motivated by a homeostatic argument.
63
References
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
14K
•Book
The Origins of Order: Self-Organization and Selection in Evolution
Stuart A. Kauffman
- 01 Jan 1993
TL;DR: The structure of rugged fitness landscapes and the structure of adaptive landscapes underlying protein evolution, and the architecture of genetic regulatory circuits and its evolution.
8.7K
•Posted Content
On Information and Sufficiency
TL;DR: The information deviation between any two finite measures cannot be increased by any statistical operations (Markov morphisms) and is invarient if and only if the morphism is sufficient for these two measures as mentioned in this paper.
7.3K