Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany A hierarchy of...
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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