Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, 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 μVμV time (ms) Biophysical model Deviant stimulus Standard stimulus Network of nodes Neural mass model Sigmoid Potential function Exc IN PC Inh IN Evoked response

Auditory perception Biophysical modelling: Applications Garrido et al. PNAS (2007), J Neurophy(2009) Schofield et al. PNAS (2009) MEG: Evoked EEG: Evoked EEG/MEG: evidence of prediction error? Prediction error of which predictions?

Functional role of network nodes Functional role of network nodes? Biophysical model (MEG) Input Functional model Model Brain data Time (ms) Amplitude (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-scale Multiple time-scales Slow 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 Fast e1e1 e2e2 e3e3 e4e4 e1e1 e2e2 e3e3 e4e4 s1s1 s2s2

Speech example: Fast and slow frequency time frequency time von Kriegstein et al. (2008), Curr Biol

Speech example: Fast and slow l l von Kriegstein et al. (2008), Curr Biol frequency time frequency time

Auditory recognition: The brain challenge Sound wave Bayesian agent Environment Time (ms) Amplitude Online recognition/prediction at multiple time-scales using continuous dynamics continuous dynamics expressing prediction error at 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 Acoustic level Phonemic level Syllabic level Sound wave Online decoding Environment Agent Temporal hierarchy

Generative model: Hierarchy of sequences Kiebel et al. (2009), PLoS Comp Biol Phonemes Hidden states Syllables Hidden states a e i o a e i o a e i o 1 2 3

Recognition of sequences Phonemes Syllables Phonemes Sound wave Environment Agent

This means... Hidden message at slow time- scale can be decoded Bayesian agent Hi

Deviations from phonotactic rules a e i o a e i o a e o Generative model of environment a e i o a e i o a e o Generative model of agent

Deviations from phonotactic rules Recognized syllables True syllables Syllables Prediction errors PhonemesSyllables

This means... Prediction error: deviations from expected temporal structure Bayesian agent Hola ???

Predictions for experiments? Sound wave Bayesian agent Environment Time (ms) Amplitude Online recognition/prediction at multiple time-scales using continuous dynamics continuous dynamics expressing prediction error Input

Conclusions Outlook: derive functional predictions for experimental testing Auditory recognition/prediction can be modelled by Bayesian online inference. Input must be based on multi-scale temporal hierarchy.

Thank you Karl Friston Jean Daunizeau Katharina von Kriegstein