A free energy principle for the brain

Abstract

By formulating Helmholtz's ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses. In this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure.

Neural changes when actions change: Adaptation of strong and weak expectations

“…Otherwise, assembling and memorizing experiences would be pointless, as they would loose their capacity to guide successful behavior as soon as a one-time breach of expectation had occurred. Hence, the divergent perception should not cause instantaneous adaptation of the internal model; accordingly, a process of adaptation is revealed by diminution of the neural correlate of divergence over a large number of iterations (Friston, Kilner, & Harrison, 2006;Grill-Spector, Henson, & Martin, 2006;Majdanžić, Bekkering, van Schie, & Toni, 2009) as targeted in the adaptation contrast (ii). It has previously been established that the cortical motor network is capable of predicting the ongoing course of action (Jeannerod, 1995).…”

The Subjective Consequences of Experiencing Random Events

“…It has been suggested that we have "epistemic needs" to have order and regularity in the world around us (Kruglanski, 1980). Indeed, it has been proposed that the structure of brain networks are fundamentally non-random and modular in nature (Sporns, 2011), and at a functional level, a general organizing principle of the brain is to minimize unpredictability between the external environment and internal states (Friston, Kilner, & Harrison, 2006;Friston, 2012).…”

The Character of Consciousness

“…Although the definition of P by Equation (3) differs from the one referred to by the free-energy principle (Nicolis and Prigogine, 1977;Haken, 1983;Friston et al, 2006;Friston, 2010), the idea is the same: Evolution of any kind directs toward a free energy minimum state. The least-time principle parallels also of the principle of least effort (Zipf, 1949).…”