What does the free energy principle tell us about the brain?

The free energy principle (FEP) states, in a nutshell, that the brain seeks to minimize surprise [1]. It is arguably the most ambitious theory of the brain available today, claiming to subsume many other important ideas, such as predictive coding, efficient coding, Bayesian inference, and optimal control theory.

What is free energy karl Friston?

Friston’s free energy principle says that all life, at every scale of organization—from single cells to the human brain, with its billions of neurons—is driven by the same universal imperative, which can be reduced to a mathematical function.

What is variational free energy?

Variational free energy is a function of observations and a probability density over their hidden causes. This means that if a system acts to minimise free energy, it will implicitly place an upper bound on the entropy of the outcomes – or sensory states – it samples.

How particular is the physics of the free energy principle?

The Free Energy Principle (FEP) states that any dynamical system can be interpreted as performing Bayesian inference upon its surrounding environment. This equivalence does not hold in general even for linear systems as it requires an effective decoupling from the system’s history of interactions.

What is Gibbs free energy used for?

The Gibbs energy can be used to show whether changes occur spontaneously or if they are forced. The Gibbs energy is an extensive function of state that is defined by Equation (1.16). Equation (1.16) shows that Gibbs energy is defined by entropy, internal energy, pressure-volume work, and temperature.

Is the brain Bayesian?

The Bayesian brain exists in an external world and is endowed with an internal representation of this external world. The two are separated from each other by what is called a Markov blanket. to produce sensory information. This is the first crucial point in understanding the Bayesian brain hypothesis.

Why do variational inferences occur?

Variational Bayesian methods are primarily used for two purposes: To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables.

How is Gibbs free energy change useful in predicting feasibility of a process?

Gibbs equation helps us to predict the spontaneity of reaction on the basis of enthalpy and entropy values directly. When the reaction is exothermic, enthalpy of the system is negative making Gibbs free energy negative. Hence, we can say that all exothermic reactions are spontaneous.

Why is the brain Bayesian?

For the clearest evidence of Bayesian reasoning in the brain, we must look past the high-level cognitive processes that govern how we think and assess evidence, and consider the unconscious processes that control perception and movement. “We really are Bayesian inference machines,” he says.

What is the Bayesian approach and why is it important for visual perception?

Bayesian models of visual perception allow scientists to break these problems down into limited classes of cate- gories that lie within a theoretical framework that can be extended to deal with the ambiguities and complexities of natural images in studies of computer vision.

What does variational mean in statistics?

Variation is a way to show how data is dispersed, or spread out. Several measures of variation are used in statistics.

What type of motion does the free energy principle optimize?

This motion can be complicated and itinerant (wandering) provided that it revisits a small set of states, called a global random attractor 10, that are compatible with survival (for example, driving a car within a small margin of error). It is this motion that the free-energy principle optimizes.

Is the free-energy principle a unified brain theory?

Although this principle has been portrayed as a unified brain theory 1, its capacity to unify different perspectives on brain function has yet to be established. This Review attempts to place some key theories within the free-energy framework, in the hope of identifying common themes.

What is free energy in sensory perception?

Third, it shows that free energy rests on a generative model of the world, which is expressed in terms of the probability of a sensation and its causes occurring together. This means that an agent must have an implicit generative model of how causes conspire to produce sensory data.

What is variational free energy in machine learning and statistics?

Variational free energy has been exploited in machine learning and statistics to solve many inference and learning problems 12, 13, 14. In this setting, surprise is called the (negative) model evidence.