Monday, December 03, 2018

Our brains are prediction machines. Friston's free-energy principle

Further reading on the article noted in the previous post has made me realize that I have been seriously remiss in not paying more attention to a revolution in how we view our brains. From a Karl Friston piece in Nature Neuroscience on predictive coding:
In the 20th century we thought the brain extracted knowledge from sensations. The 21st century witnessed a ‘strange inversion’, in which the brain became an organ of inference, actively constructing explanations for what’s going on ‘out there’, beyond its sensory epithelia.
And, key points from a Friston review, "The free-energy principle: a unified brain theory?:
Adaptive agents must occupy a limited repertoire of states and therefore minimize the long-term average of surprise associated with sensory exchanges with the world. Minimizing surprise enables them to resist a natural tendency to disorder.
Surprise rests on predictions about sensations, which depend on an internal generative model of the world. Although surprise cannot be measured directly, a free-energy bound on surprise can be, suggesting that agents minimize free energy by changing their predictions (perception) or by changing the predicted sensory inputs (action).
Perception optimizes predictions by minimizing free energy with respect to synaptic activity (perceptual inference), efficacy (learning and memory) and gain (attention and salience). This furnishes Bayes-optimal (probabilistic) representations of what caused sensations (providing a link to the Bayesian brain hypothesis).
Bayes-optimal perception is mathematically equivalent to predictive coding and maximizing the mutual information between sensations and the representations of their causes. This is a probabilistic generalization of the principle of efficient coding (the infomax principle) or the minimum-redundancy principle.
Learning under the free-energy principle can be formulated in terms of optimizing the connection strengths in hierarchical models of the sensorium. This rests on associative plasticity to encode causal regularities and appeals to the same synaptic mechanisms as those underlying cell assembly formation.
Action under the free-energy principle reduces to suppressing sensory prediction errors that depend on predicted (expected or desired) movement trajectories. This provides a simple account of motor control, in which action is enslaved by perceptual (proprioceptive) predictions.
Perceptual predictions rest on prior expectations about the trajectory or movement through the agent's state space. These priors can be acquired (as empirical priors during hierarchical inference) or they can be innate (epigenetic) and therefore subject to selective pressure.
Predicted motion or state transitions realized by action correspond to policies in optimal control theory and reinforcement learning. In this context, value is inversely proportional to surprise (and implicitly free energy), and rewards correspond to innate priors that constrain policies.

1 comment:

  1. Extremely interesting! I've recently read "Worldviews: An Introduction to the History and Philosophy of Science" by Richard DeWitt. At the end of that book, he talks about the loss of analogy (as an explanatory device) in the sciences due to overwhelming complexity. In this posting, one sees analogies to Information Theory, Thermodynamics, and Kolmogorov Complexity, along with Bayesian based arguments. If there is a loss of analogy/isomorphism as an explanatory mechanism, then it may be the case that mechanized intelligence - if it is possible - truly defies explanation.