Karl Friston – 2016 CCN Workshop: Predictive Coding

Center for Cognitive Neuroscience at Dartmouth
2016 Workshop: Predictive Coding

Predictive coding, active inference and belief propagation

I will consider prediction and choice based upon the minimisation of expected free energy. Crucially, (negative) free energy can always be decomposed into pragmatic (extrinsic) and epistemic (intrinsic) value. Minimising expected free energy is therefore equivalent to maximising extrinsic value, while maximising information gain or intrinsic value, i.e., reducing uncertainty about the causes of sensory samples. This decomposition resolves the exploration-exploitation dilemma; where epistemic value is maximised until there is no further resolution of uncertainty, after which exploitation is assured through maximisation of extrinsic value. This is formally consistent with the principle of maximum mutual information, generalising formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk sensitive (KL) control – using Hamilton’s principle of least action. I will briefly review the normative theory – illustrating the minimisation of expected free energy using simulations and then turn to neuronal processes theories. In brief, the implicit (neuronally plausible) belief propagation offers a form of predictive coding, when hidden causes and outcomes are treated as discrete states.


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