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Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models

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Kunze,  Tim
Methods and Development Group MEG and EEG - Cortical Networks an Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute for Biomedical Engineering and Informatics, TU Ilmenau, Germany;

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Knösche,  Thomas R.
Methods and Development Group MEG and EEG - Cortical Networks an Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Kunze, T., Haueisen, J., & Knösche, T. R. (2019). Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models. Biological Cybernetics, 113(3), 273-291. doi:10.1007/s00422-019-00792-y.


Cite as: https://hdl.handle.net/21.11116/0000-0003-2149-6
Abstract
The concept of connectionism states that higher cognitive functions emerge from the interaction of many simple elements. Accordingly, research on canonical microcircuits conceptualizes findings on fundamental neuroanatomical circuits as well as recurrent organizational principles of the cerebral cortex and examines the link between architectures and their associated functionality. In this study, we establish minimal canonical microcircuit models as elements of hierarchical processing networks. Based on a combination of descriptive time simulations and explanatory state-space mappings, we show that minimal canonical microcircuits effectively segregate feedforward and feedback information flows and that feedback information conditions basic processing operations in minimal canonical microcircuits. Further, we derive and examine two prototypical meta-circuits of cooperating minimal canonical microcircuits for the neurocognitive problems of priming and structure building. Through the application of these findings to a language network of syntax parsing, this study embodies neurocognitive research on hierarchical communication in light of canonical microcircuits, cell assembly theory, and predictive coding. © 2019, The Author(s).