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  Quantifying and modelling non-local information processing of associative brain regions

Balkenhol, J., Prada, J., Ehrenreich, H., Grohmann, J., Kistowski, J., Wojcik, S. M., et al. (2019). Quantifying and modelling non-local information processing of associative brain regions. bioRxiv. doi:10.1101/2019.12.19.883124.

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2019.12.19.883124v1.full.pdf (Preprint), 3MB
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2019.12.19.883124v1.full.pdf
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Balkenhol, J., Author
Prada, J., Author
Ehrenreich, Hannelore1, Author           
Grohmann, J., Author
Kistowski, J.V., Author
Wojcik, Sonja M.2, Author           
Blum, R., Author
Kounev, S., Author
Dandekar, T., Author
Affiliations:
1Clinical neuroscience, Max Planck Institute of Experimental Medicine, Max Planck Society, Hermann-Rein-Str. 3, 37075 Göttingen, DE, ou_2173651              
2Molecular neurobiology, Max Planck Institute of Experimental Medicine, Max Planck Society, Hermann-Rein-Str. 3, 37075 Göttingen, DE, ou_2173659              

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 Abstract: Brain world representation emerges not by philosophy but from integrating simple followed by more complex actions (due to drives, instincts) with sensory feedback and inputs such as rewards. Our simulation provides this world representation holistically by identical information encoded as holographic wave patterns for all associative cortex regions. Observed circular activation in cell culture experiments provides building blocks from which such an integrative circuit can evolve just by excitation and inhibition transfer to neighbouring neurons. Large-scale grid-computing of the simulation brought no new emergent phenomena but rather linear gains and losses regarding performance. The circuit integrates perceptions and actions. The resulting simulation compares well with data from electrophysiology, visual perception tasks, and oscillations in cortical areas. Non-local, wave-like information processing in the cortex agrees well with EEG observations such as cortical alpha, beta, and gamma oscillations. Non-local information processing has powerful emergent properties, including key features of conscious information processing.

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Language(s): eng - English
 Dates: 2019-12-20
 Publication Status: Published online
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 Rev. Type: No review
 Identifiers: DOI: 10.1101/2019.12.19.883124
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Title: bioRxiv
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