free energy and active inference karl friston, university college london
DESCRIPTION
Free energy and active inference Karl Friston, University College London. - PowerPoint PPT PresentationTRANSCRIPT
Slide 1
How 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. I hope to illustrate these points using simple simulations of perception, action and action observation..
Free energy and active inference Karl Friston, University College London
OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup
The anatomy of inferencegraphical models and predictive codingcanonical microcircuits
Action and perceptionperceptual omission responsessimulations of action observation
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 states
Active states
External statesInternal statesSensory states
External statesInternal states
The Markov blanket in biotic systems
The Fokker-Planck equation
And its solution in terms of curl-free and divergence-free componentslemma: any (ergodic random) dynamical system (m) that possesses a Markov blanket will appear to actively maintain its structural and dynamical integrity
5
External statesInternal states
But what about the Markov blanket?Reinforcement learning, optimal control and expected utility theory
Information theory and minimum redundancy
Self-organisation, cybernetics and homoeostasis
Bayesian brain, active inference and predictive coding
Value
Surprise (free energy)
Entropy
Model evidence
PavlovAshbyHelmholtz
Barlow
OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup
The anatomy of inferencegraphical models and predictive codingcanonical microcircuits
Action and perceptionperceptual omission responsessimulations of action observation
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 encodes the children, parents and parents of childrenFinding the (principal) Markov blanketA
Autopoiesis, oscillator death and simulated brain lesions100200300400500-0.4-0.3-0.2-0.10TimeMotion of external stateTrue and predicted motion -505-8-6-4-20468PositionPositionPredictability2
TimeModesInternal states10020030040050051015202530Decoding through the Markov blanket and simulated brain activation
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 perceptionperceptual omission responsessimulations of action observation
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 blanket
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 Plato: The Republic (514a-520a)
Impressions on the Markov blanketBayesian filtering and predictive coding
prediction update
prediction error
Making our own sensations
Change sensationssensations predictionsPrediction errorChange predictionsActionPerception
DescendingpredictionsAscending prediction errors
A simple hierarchy
whatwhereSensory fluctuations
Hierarchical generative models
frontal eye fieldsgeniculatevisual cortexretinal inputponsoculomotor signals
Prediction error (superficial pyramidal cells)Expectations (deep pyramidal cells)Top-down or backward predictionsBottom-up or forward prediction errorproprioceptive inputreflex arcPerception
David MumfordPredictive coding with reflexesAction
Canonical microcircuits for predictive coding
Friston: Nat Rev Neurosci. 2010 11:127-38 Haeusler and Maass: Cereb. Cortex 2006;17:149-162 Bastos et al: Neuron 2012; 76:695-711 Shipp et al: TINS 2013
Biological agents minimize their average surprise (entropy)
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 perceptionperceptual omission responsessimulations of action observation
Perceptual inference and sequences of sequencesSyrinxNeuronal hierarchy
Time (sec)Frequency (KHz)sonogram0.511.5
omission and violation of predictionsStimulus but no perceptPercept but no stimulusFrequency (Hz)stimulus (sonogram)25003000350040004500Time (sec)Frequency (Hz)percept0.511.525003000350040004500500100015002000-100-50050100peristimulus time (ms)LFP (micro-volts)ERP (prediction error)without last syllableTime (sec)percept0.511.5500100015002000-100-50050100peristimulus time (ms)LFP (micro-volts)with omission
OverviewThe statistics of lifeMarkov blankets and ergodic systemssimulations of a primordial soup
The anatomy of inferencegraphical models and predictive codingcanonical microcircuits
Action and perceptionperceptual omission responsessimulations of action observation
proprioceptive inputAction with point attractors
visual input
Descendingproprioceptive predictions
00.20.40.60.811.21.40.40.60.811.21.4actionposition (x)position (y)00.20.40.60.811.21.4observationposition (x)Heteroclinic cycle (central pattern generator)
Descendingproprioceptive predictions
Each movement we make by which we alter the appearance of objects should be thought of as an experiment designed to test whether we have understood correctly the invariant relations of the phenomena before us, that is, their existence in definite spatial relations.
'The Facts of Perception' (1878) in The Selected Writings of Hermann von Helmholtz,Ed.R. Karl, Middletown: Wesleyan University Press, 1971 p. 384
Hermann von Helmholtz 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 HintonJames 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
Searching to test hypotheses life as an efficient experimentFree energy principleminimise uncertainty