Beyond-local neural information processing in neuronal networks
While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use osc...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702400360X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850065529453150208 |
|---|---|
| author | Johannes Balkenhol Barbara Händel Sounak Biswas Johannes Grohmann Jóakim v. Kistowski Juan Prada Conrado A. Bosman Hannelore Ehrenreich Sonja M. Wojcik Samuel Kounev Robert Blum Thomas Dandekar |
| author_facet | Johannes Balkenhol Barbara Händel Sounak Biswas Johannes Grohmann Jóakim v. Kistowski Juan Prada Conrado A. Bosman Hannelore Ehrenreich Sonja M. Wojcik Samuel Kounev Robert Blum Thomas Dandekar |
| author_sort | Johannes Balkenhol |
| collection | DOAJ |
| description | While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use oscillating activity for information exchange between distributed nodes. To better understand building principles underlying the observation of synchronized oscillatory activity in a large-scale network, we developed a reductionistic neuronal network model. Fundamental building principles are laterally and temporally interconnected virtual nodes (microcircuits), wherein each node was modeled as a local oscillator. By this building principle, the neuronal network model can integrate information in time and space. The simulation gives rise to a wave interference pattern that spreads over all simulated columns in form of a travelling wave. The model design stabilizes states of efficient information processing across all participating neuronal equivalents. Model-specific oscillatory patterns, generated by complex input stimuli, were similar to electrophysiological high-frequency signals that we could confirm in the primate visual cortex during a visual perception task. Important oscillatory model pre-runners, limitations and strength of our reductionistic model are discussed. Our simple scalable model shows unique integration properties and successfully reproduces a variety of biological phenomena such as harmonics, coherence patterns, frequency-speed relationships, and oscillatory activities. We suggest that our scalable model simulates aspects of a basic building principle underlying oscillatory, large-scale integration of information in small and large brains. |
| format | Article |
| id | doaj-art-4dc9cf9436824f67bbfc93c4d96009a6 |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-4dc9cf9436824f67bbfc93c4d96009a62025-08-20T02:48:58ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-01234288430510.1016/j.csbj.2024.10.040Beyond-local neural information processing in neuronal networksJohannes Balkenhol0Barbara Händel1Sounak Biswas2Johannes Grohmann3Jóakim v. Kistowski4Juan Prada5Conrado A. Bosman6Hannelore Ehrenreich7Sonja M. Wojcik8Samuel Kounev9Robert Blum10Thomas Dandekar11Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, GermanyDepartment of Psychology (III), University of Würzburg, 97070 Würzburg, Germany; Department of Neurology, University Hospital Würzburg, 97080 Würzburg, GermanyDepartment of Theoretical Physics I, University of Würzburg, 97074 Würzburg, GermanyInstitute of Computer Science, Chair of Software Engineering (Computer Science II), University of Würzburg, 97074 Würzburg, GermanyInstitute of Computer Science, Chair of Software Engineering (Computer Science II), University of Würzburg, 97074 Würzburg, GermanyDepartment of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, GermanyCognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, 1105 BA Amsterdam, NetherlandsExperimentelle Medizin, Zentralinstitut für Seelische Gesundheit, 68159 Mannheim, GermanyNeurosciences, Max-Planck-Institut für Multidisziplinäre Naturwissenschaften, 37075 Göttingen, GermanyInstitute of Computer Science, Chair of Software Engineering (Computer Science II), University of Würzburg, 97074 Würzburg, GermanyDepartment of Neurology, University Hospital Würzburg, 97080 Würzburg, Germany; Corresponding author.Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany; European Molecular Biology Laboratory (EMBL), 69012 Heidelberg, Germany; Corresponding author at: Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany.While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use oscillating activity for information exchange between distributed nodes. To better understand building principles underlying the observation of synchronized oscillatory activity in a large-scale network, we developed a reductionistic neuronal network model. Fundamental building principles are laterally and temporally interconnected virtual nodes (microcircuits), wherein each node was modeled as a local oscillator. By this building principle, the neuronal network model can integrate information in time and space. The simulation gives rise to a wave interference pattern that spreads over all simulated columns in form of a travelling wave. The model design stabilizes states of efficient information processing across all participating neuronal equivalents. Model-specific oscillatory patterns, generated by complex input stimuli, were similar to electrophysiological high-frequency signals that we could confirm in the primate visual cortex during a visual perception task. Important oscillatory model pre-runners, limitations and strength of our reductionistic model are discussed. Our simple scalable model shows unique integration properties and successfully reproduces a variety of biological phenomena such as harmonics, coherence patterns, frequency-speed relationships, and oscillatory activities. We suggest that our scalable model simulates aspects of a basic building principle underlying oscillatory, large-scale integration of information in small and large brains.http://www.sciencedirect.com/science/article/pii/S200103702400360XNeuronal field modelInformation integrationNeural networkColumnar architectureParallel computingNeuronal oscillations |
| spellingShingle | Johannes Balkenhol Barbara Händel Sounak Biswas Johannes Grohmann Jóakim v. Kistowski Juan Prada Conrado A. Bosman Hannelore Ehrenreich Sonja M. Wojcik Samuel Kounev Robert Blum Thomas Dandekar Beyond-local neural information processing in neuronal networks Computational and Structural Biotechnology Journal Neuronal field model Information integration Neural network Columnar architecture Parallel computing Neuronal oscillations |
| title | Beyond-local neural information processing in neuronal networks |
| title_full | Beyond-local neural information processing in neuronal networks |
| title_fullStr | Beyond-local neural information processing in neuronal networks |
| title_full_unstemmed | Beyond-local neural information processing in neuronal networks |
| title_short | Beyond-local neural information processing in neuronal networks |
| title_sort | beyond local neural information processing in neuronal networks |
| topic | Neuronal field model Information integration Neural network Columnar architecture Parallel computing Neuronal oscillations |
| url | http://www.sciencedirect.com/science/article/pii/S200103702400360X |
| work_keys_str_mv | AT johannesbalkenhol beyondlocalneuralinformationprocessinginneuronalnetworks AT barbarahandel beyondlocalneuralinformationprocessinginneuronalnetworks AT sounakbiswas beyondlocalneuralinformationprocessinginneuronalnetworks AT johannesgrohmann beyondlocalneuralinformationprocessinginneuronalnetworks AT joakimvkistowski beyondlocalneuralinformationprocessinginneuronalnetworks AT juanprada beyondlocalneuralinformationprocessinginneuronalnetworks AT conradoabosman beyondlocalneuralinformationprocessinginneuronalnetworks AT hanneloreehrenreich beyondlocalneuralinformationprocessinginneuronalnetworks AT sonjamwojcik beyondlocalneuralinformationprocessinginneuronalnetworks AT samuelkounev beyondlocalneuralinformationprocessinginneuronalnetworks AT robertblum beyondlocalneuralinformationprocessinginneuronalnetworks AT thomasdandekar beyondlocalneuralinformationprocessinginneuronalnetworks |