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Free energy and life as we know it

Karl Friston, University College LondonExperimental Chaos and Complexity Conference 2014How much about our interaction 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. Inthe context of perception, this corresponds to Bayes-optimal predictive coding that suppresses exteroceptive prediction errors. Inthe context of action, motor reflexes can be seen as suppressing proprioceptive prediction errors. We will look at some of the phenomena that emerge from this scheme, such as hierarchical message passing in the brain and the perceptual inference that ensues. I hope to illustrate these points using simple simulations of action and perception that rest upon attractor dynamics

OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup

The anatomy of inferencegraphical models and predictive codingcanonical microcircuits

Action and perceptionsynchronization of chaos and birdsong

How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry? (Erwin Schrdinger 1943)

The Markov blanket as a statistical boundary (parents, children and parents of children)Internal states External statesSensory statesActive statesThe Markov blanket in biotic systems

Active states

External statesInternal statesSensory states

The flow of states with curl-free and divergence-free componentsTake any random dynamical system (m)

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But what about the Markov blanket?Self-organisation, cybernetics, homoeostasis and autopoiesis

Bayesian brain, predictivecoding and active inference

Entropy

Model evidence

AshbyHelmholtz

Haken

PerceptionActionGregory

OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup

The anatomy of inferencegraphical models and predictive codingcanonical microcircuits

Action and perceptionsynchronization of chaos and birdsong

Position

Simulations of a (prebiotic) primordial soupWeak electrochemical attractionStrong repulsionShort-range forces

ElementAdjacency matrix2040608010012020406080100120Markov BlanketHidden statesSensory statesActive statesInternal states

Markov Blanket = [B [eig(B) > ]]Markov blanket matrix: encoding the children, parents and parents of childrenFinding the (principal) Markov blanketADoes action maintain the structural and functional integrity of the Markov blanket (autopoiesis) ?

Do internal states appear to infer the hidden causes of sensory states (active inference) ?

Autopoiesis, oscillator death and simulated brain lesionsDecoding through the Markov blanket and simulated brain activation100200300400500-0.4-0.3-0.2-0.10TimeMotion of external stateTrue and predicted motion -505-8-6-4-20468PositionPositionPredictability2

TimeModesInternal states10020030040050051015202530

Christiaan Huygens

The existence of a Markov blanket necessarily implies a partition of states into internal states, their Markov blanket (sensory and active states) and external or hidden states.

Because active states change but are not changed by external states they minimize the entropy of internal states and their Markov blanket. This means action will appear to maintain the structural and functional integrity of the Markov blanket (autopoiesis).

Internal states appear to infer the hidden causes of sensory states (by maximizing Bayesian evidence) and influence those causes though action (active inference)

The statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup

The anatomy of inferencegraphical models and predictive codingcanonical microcircuits

Action and perceptionsynchronization of chaos and birdsong

Overview

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 BayesGeoffrey HintonRichard FeynmanThe Helmholtz machine and the Bayesian brainRichard Gregory

Hermann von Helmholtz 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 Richard Gregory

Hermann von Helmholtz Impressions on the Markov blanketPlato: The Republic (514a-520a)

Bayesian filtering, free energy and predictive codingprediction correction

prediction error

Making our own sensations

Changing sensationssensations predictionsPrediction errorChanging predictionsActionPerception

ThalamusArea XHigher vocal centreHypoglossal Nucleus

Prediction error (superficial pyramidal cells)Expectations (deep pyramidal cells)Perception

Action

David MumfordPredictive coding with reflexes

Biological agents minimize their average surprise (free energy)

They minimize surprise by suppressing prediction error

Prediction error can be reduced by changing predictions (perception)

Prediction error can be reduced by changing sensations (action)

Perception entails recurrent message passing to optimize predictions

Action makes predictions come true (and minimizes surprise)

OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup

The anatomy of inferencegraphical models and predictive codingcanonical microcircuits

Action and perceptionsynchronization of chaos and birdsong

Area X Higher local centre Syrinx

Perceptual inference and sequences of sequences

Time (sec)Frequency (KHz)sonogram0.511.5

External statesInternal statesSensory states

time (sec)Frequency (Hz)percept1234567250030003500400045005000012345678-50050100time (seconds)First level expectations (hidden states)012345678-40-20020406080time (seconds)Second level expectations (hidden states)

time (sec)Frequency (Hz)percept1234567250030003500400045005000012345678-50050100time (seconds)First level expectations (hidden states)012345678-40-20020406080time (seconds)Second level expectations (hidden states)Active inference and communication

Thank you

And thanks to collaborators:

Rick AdamsAndre BastosSven BestmannHarriet BrownJean DaunizeauMark EdwardsXiaosi GuLee HarrisonStefan KiebelJames KilnerJrmie MattoutRosalyn MoranWill PennyLisa Quattrocki Knight Klaas Stephan

And colleagues:

Andy ClarkPeter DayanJrn DiedrichsenPaul FletcherPascal FriesGeoffrey HintonAllan HobsonJames HopkinsJakob HohwyHenry KennedyPaul VerschureFlorentin Wrgtter

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-scaleFree-energy minimisation leading to