Workshop on: The Free Energy Principle (Presented by the Wellcome Trust Centre for Neuroimaging) July 5 (Thursday) - 6 (Friday) 2012 Workshop on: The Free Energy Principle (Presented by the Wellcome Trust Centre for Neuroimaging) July 5 (Thursday) - 6 (Friday) 2012 Abstract How much about our interactions with – and experience of – our world can be deduced from basic principles? This talk reviews recent attempts to understand the self-organised behaviour of embodied agents, like ourselves, as satisfying basic imperatives for sustained exchanges with the environment. In brief, one simple driving force appears to explain many aspects of action and perception. This driving force is the minimisation of surprise or prediction error that – in the context of perception – corresponds to Bayes-optimal predictive coding (that suppresses exteroceptive prediction errors) and – in the context of action – reduces to classical motor reflexes (that suppress proprioceptive prediction errors). We will look at some of the implications for the anatomy of this active inference – in terms of large-scale anatomical graphs and canonical microcircuits – and then turn to some examples of active inference – such as the perceptual categorisation, action perception and visual searches.. Free energy and active inference Karl Friston University College London

Overview The free-energy principle action and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Some examples perceptual categorization omission responses action observation visual searches

“Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz Thomas Bayes Geoffrey Hinton Richard Feynman From the Helmholtz machine to the Bayesian brain and self-organization Hermann Haken Richard Gregory Hermann von Helmholtz

temperature What is the difference between a snowflake and a bird? Phase-boundary …a bird can act (to avoid surprises)

Self organisation and the principle of least action Maximum entropy principle The principle of least free energy (and minimising surprise) Minimum entropy principle Ergodic theorem

How can we minimize surprise (prediction error)? Change sensations sensations – predictions Prediction error Change predictions Action Perception …action and perception minimise free energy

Prior distribution Posterior distribution Likelihood distribution temperature Action as inference – the “Bayesian thermostat” Perception: Action:

Overview The free-energy principle action and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Some examples perceptual categorization omission responses action observation visual searches

Hidden states in the worldInternal states of the agent Sensations Action External states Fluctuations Posterior expectations

Free energy minimisationGenerative modelPredictive coding with reflexes

Langevin form Generative model Probabilistic graphical models

Expectations: Predictions: Prediction errors: Generative model Model inversion (inference) A simple hierarchy Outward prediction stream Inward error stream From models to perception

Haeusler and Maass: Cereb. Cortex 2006;17: Canonical microcircuit for predictive coding Backward predictions Forward prediction error Backward predictions Forward prediction error

occipital cortex geniculate visual cortex retinal input pons oculomotor signals Prediction error (superficial pyramidal cells) Conditional predictions (deep pyramidal cells) Top-down or backward predictions Bottom-up or forward prediction error proprioceptive input reflex arc David Mumford Predictive coding with reflexes

Biological agents resist the second law of thermodynamics They must minimize their average surprise (entropy) They minimize surprise by suppressing prediction error (free-energy) Prediction error can be reduced by changing predictions (perception) Prediction error can be reduced by changing sensations (action) Perception entails recurrent message passing in the brain to optimize predictions Action makes predictions come true (and minimizes surprise)

Overview The free-energy principle action and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Some examples perceptual categorization omission responses action observation visual searches

Generating bird songs with attractors Syrinx HVC time (sec) Frequency Sonogram causal states hidden states

prediction and error hidden states Backward predictions Forward prediction error causal states Predictive coding stimulus time (seconds)

Perceptual categorization Frequency (Hz) Song a time (seconds) Song bSong c

Sequences of sequences Syrinx Neuronal hierarchy Time (sec) Frequency (KHz) sonogram

omission and violation of predictions Stimulus but no percept Percept but no stimulus Frequency (Hz) stimulus (sonogram) Time (sec) Frequency (Hz) percept peristimulus time (ms) LFP (micro-volts) ERP (error) without last syllable Time (sec) percept peristimulus time (ms) LFP (micro-volts) with omission

Overview The free-energy principle action and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Some examples perceptual categorization omission responses action observation visual searches

Prior distribution temperature Action as inference – the “Bayesian thermostat” Perception: Action:

visual input proprioceptive input Action with point attractors Descending proprioceptive predictions Exteroceptive predictions

action position (x) position (y) observation position (x) Heteroclinic cycle (central pattern generator) Descending proprioceptive predictions

Overview The free-energy principle action and perception predictive coding with reflexes The anatomy of inference graphical models canonical microcircuits Some examples perceptual categorization omission responses action observation visual searches

Where do I expect to look?

saliencevisual inputstimulussampling Sampling the world to minimise uncertainty Perception as hypothesis testing – saccades as experiments

Hidden states in the worldInternal states of the agent Sensations Action External states Fluctuations Posterior expectations Prior expectations

Frontal eye fields Pulvinar salience map Fusiform (what) Superior colliculus Visual cortex oculomotor reflex arc Parietal (where)

Saccadic fixation and salience maps Visual samples Conditional expectations about hidden (visual) states And corresponding percept Saccadic eye movements Hidden (oculomotor) states

Thank you And thanks to collaborators: Rick Adams Andre Bastos Sven Bestmann Jean Daunizeau Harriet Brown Lee Harrison Stefan Kiebel James Kilner Jérémie Mattout Rosalyn Moran Will Penny Klaas Stephan And colleagues: Andy Clark Peter Dayan Jörn Diedrichsen Paul Fletcher Pascal Fries Geoffrey Hinton James Hopkins Jakob Hohwy Henry Kennedy Paul Verschure Florentin Wörgötter And many others

Perception and Action: The optimisation of neuronal and neuromuscular activity to suppress prediction errors (or free- energy) based on generative models of sensory data. Learning and attention: The optimisation of synaptic gain and efficacy over seconds to hours, to encode the precisions of prediction errors and causal structure in the sensorium. This entails suppression of free-energy over time. Neurodevelopment: Model optimisation through activity- dependent pruning and maintenance of neuronal connections that are specified epigenetically Evolution: Optimisation of the average free-energy (free-fitness) over time and individuals of a given class (e.g., conspecifics) by selective pressure on the epigenetic specification of their generative models. Time-scale Free-energy minimisation leading to…