Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany

A hierarchy of time-scales and the brain

Stefan Kiebel

1 Biophysical models for EEG/MEG

2 Functional model: An agent just like the brain

3 Auditory example

Overview

1 Biophysical models for EEG/MEG

2 Functional model: An agent just like the brain

3 Auditory example

Overview

EEG and MEG: Connectivity analysis

David et al. (2006), NeuroImage; Kiebel et al. (2006), NeuroImage, Kiebel et al. (2009), Human Brain Mapping

μV

time (ms)

Biophysical modelBiophysical model

Deviant stimulus

Standard stimulus Network of nodes Neural mass model

Sigmoid Potential function

Exc IN

PC

Inh IN

Evoked responseEvoked response

Auditory perceptionAuditory perception

Biophysical modelling: Applications

Garrido et al. PNAS (2007), J Neurophy(2009)

Schofield et al. PNAS (2009)

ME

G:

Evo

ked

EE

G:

Evo

ked

EEG/MEG: evidence of prediction error?EEG/MEG: evidence of prediction error?

Prediction error of which predictions?Prediction error of which predictions?

Functional role of network nodes

Functional role of network nodes?

Biophysical model (MEG)Biophysical model (MEG)

Input

Functional modelFunctional model

ModelModelBrain dataBrain data

Time (ms)

Am

plit

ud

e (

fT)

1 Biophysical models for EEG/MEG

2 Functional model: An agent just like the brain

3 Auditory example

Overview

Meaning: Hidden at slow time-scales

Single time-scaleSingle time-scale Multiple time-scalesMultiple time-scales

SlowSlow

Recognition: • non-robust • no higher level representation

Recognition: • non-robust • no higher level representation

Recognition: • robust by more constraints• higher level representation

Recognition: • robust by more constraints• higher level representation

FastFaste1 e2 e3 e4 e1 e2 e3 e4

s1 s2

Speech example: Fast and slow

freq

uen

cy

time

freq

uen

cy

time

von Kriegstein et al. (2008), Curr Biol

Speech example: Fast and slow

l l

von Kriegstein et al. (2008), Curr Biol

freq

uen

cy

time

freq

uen

cy

time

Auditory recognition: The brain challenge

Sound wave

Bayesian agent

Bayesian agent

EnvironmentEnvironment

Time (ms)

Am

plit

ud

e

Online recognition/predictionOnline recognition/prediction

at multiple time-scalesat multiple time-scales

using continuous dynamicsusing continuous dynamics

continuous dynamicscontinuous dynamics

expressing prediction errorexpressing prediction error

at multiple time-scalesat multiple time-scales

1 Biophysical models for EEG/MEG

2 Functional model: An agent just like the brain

3 Auditory example

Overview

Functional model of speech perception

Kiebel et al. (2009), PLoS Comp Biol, Friston. (2008), PLoS Comp Biol

1 1 1 1 2 1 1

1 1 1

x x v S x w

v S x z

2 2 2 2 2 2

2 2 2

x x S x w

v S x z

1w Wv Acoustic level

Phonemic level

Syllabic level

wWv 1

22 and for

dynamicsn Recognitio

vx

11 and for

dynamicsn Recognitio

vx

Sound wave

Online decodingOnline decoding

EnvironmentEnvironment AgentAgent

Temporal hierarchyTemporal hierarchy

Generative model: Hierarchy of sequences

Kiebel et al. (2009), PLoS Comp Biol

Phonemes Hidden states 1x

2vSyllables Hidden states 2x

1v

a

e

i

o

a

e

i

o

a

e

i

o

1

2

3

1

j j j

j j j

j j j j j j

j j

x f w

v g z

f x v S x

g S x

42

1

Recognition of sequences

Phonemes

Syllables Syllables

Phonemes

Sound wave

EnvironmentEnvironment AgentAgent

This means...

Hidden message at slow time-scale can be decoded

Hidden message at slow time-scale can be decoded

Bayesian agentBayesian agent

Hi

Hi

Deviations from phonotactic rules

a

e

i

o

a

e

i

o

a

e

o

1

2

3

Generative model ofenvironment

a

e

i

o

a

e

i

o

a

e

o

1

2

3

Generative model ofagent

Deviations from phonotactic rules

(2)vRecognized syllables(2)v

True syllables

Syllables

Prediction errors

Phonemes Syllables

This means...

Prediction error: deviations from expected temporal

structure

Prediction error: deviations from expected temporal

structure

Bayesian agentBayesian agent

Hola

???

Predictions for experiments?

Sound wave

Bayesian agent

Bayesian agent

EnvironmentEnvironment

Time (ms)

Am

plit

ud

e

Online recognition/predictionOnline recognition/prediction

at multiple time-scalesat multiple time-scales

using continuous dynamicsusing continuous dynamics

continuous dynamicscontinuous dynamics

expressing prediction errorexpressing prediction error

Input

Conclusions

Outlook: derive functional predictions for experimental testingOutlook: derive functional predictions for experimental testing

Auditory recognition/prediction can be modelled by Bayesian online inference.Auditory recognition/prediction can be modelled by Bayesian online inference.

Input must be based on multi-scale temporal hierarchy. Input must be based on multi-scale temporal hierarchy.

Thank you

Karl FristonJean Daunizeau

Katharina von Kriegstein