Despite the wealth of empirical data in neuroscience, there are relatively few global theories about how the brain works. A recently proposed free-energy principle for adaptive systems tries to provide a unified account of action, perception and learning. 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. I first review the free-energy principle and then deconstruct several global brain theories to show how they all speak to the same underlying idea.
The free-energy principleThe free-energy principle (BOX 1) says that any selforganizing system that is at equilibrium with its environment must minimize its free energy 2 . The principle is essentially a mathematical formulation of how adaptive systems (that is, biological agents, like animals or brains) resist a natural tendency to disorder [3][4][5][6] . What follows is a non-mathematical treatment of the motivation and implications of the principle. We will see that although the motivation is quite straightforward, the implications are complicated and diverse. This diversity allows the principle to account for many aspects of brain structure and function and lends it the potential to unify different perspectives on how the brain works. In subsequent sections, I discuss how the principle can be applied to neuronal systems as viewed from these perspectives. This Review starts in a rather abstract and technical way but then tries to unpack the basic idea in more familiar terms.Motivation: resisting a tendency to disorder. The defining characteristic of biological systems is that they maintain their states and form in the face of a constantly changing environment [3][4][5][6] . From the point of view of the brain, the environment includes both the external and the internal milieu. This maintenance of order is seen at many levels and distinguishes biological from other self-organizing systems; indeed, the physiology of biological systems can be reduced almost entirely to their homeostasis 7 . More precisely, the repertoire of physiological and sensory states in which an organism can be is limited, and these states define the organism's phenotype. Mathematically, this means that the probability of these (interoceptive and exteroceptive) sensory states must have low entropy; in other words, there is a high probability that a system will be in any of a small number of states, and a low probability that it will be in the remaining states. Entropy is also the average self information or 'surprise' 8 (more formally, it is the negative log-probability of an outcome). Here, 'a fish out of water' would be in a surprising state (both emotionally and mathematically). A fish that frequently forsook water would have high entropy. Note that both surprise and entropy depend on the agen
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