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Effective Connectivity of Audiovisual Integration

Zobeiri, Omid | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44117 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Vosoughi Vahdat, Bijan
  7. Abstract:
  8. The aim of this thesis was to test predictive coding as a model of cortical organization and function in multisensory processing using a specific brain response, the mismatch negativity (MMN) in both Auditory and visual modalities, and a novel tool for connectivity analysis, dynamic causal modeling (DCM). Predictive coding models state that the brain perceives and makes inferences about the world by recursively updating predictions about sensory input. Thus, perception would result from comparing bottom-up input from the environment with top-down predictions. The generation of the MMN, an event related response elicited by violations in the regularity of a structured auditory or visual sequence has been discussed extensively in the literature. This thesis discusses the generation of the MMN in the light of predictive coding, in other words, the MMN could reflect prediction error, occurring whenever the current input does not match a previously learnt rule. In addition, in this work, the modality nature of sensory information during this mechanism is investigated. Interpretation is tested using DCM, a methodological approach which assumes the activity in one cortical area is caused by the activity in another cortical area. In brief, this thesis assesses the validity of DCM, shows the usefulness of DCM in explaining how cortical activity is expressed at the scalp level and exploits the potential of DCM for testing hierarchical models underlying the MMN and audiovisual integration. The first part of this thesis is reviewed the previous works regarding predictive coding, audiovisual processing, MMN and DCM. The second part addresses hierarchical cortical organization in MMN generation, plausible network models or mechanisms underlying the MMN, and finally, and integration of audiovisual information
  9. Keywords:
  10. Visual Evoked Potential ; Audio Visual Processing ; Predictive Coding ; Neural Modeling

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