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| Home > Publications database > Activity-constrained full-scale cortical microcircuitmodels of macaque higher-order cortices |
| Poster (After Call) | FZJ-2019-05009 |
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2019
Please use a persistent id in citations: http://hdl.handle.net/2128/23094
Abstract: The primate brain is a complex system and many aspects of the relation between its cortical structure and network activity remain to be understood. Networks with closely similar structures can perform very different tasks [1]. Conversely, very different network structures can lead to similar activity [2].A bottom-up cortical point neuron network model of early sensory cortices is able to recover realistic first-order spike train statistics [3], giving some insight into the relationship between structure and activity. This model can be constructed thanks to the availability of extensive anatomical and physiological data from visual and somatosensory areas. However, such measurements are less abundant for higher-order areas (motor, prefrontal, parietal). Thus, the bottom-up modeling approach cannot be applied to all cortical areas until further biological measurements are published.In order to overcome this limitation, we construct spiking models of macaque higher-order cortical areas by joining bottom-up modeling with a top-down method. The models are first characterized using the available anatomical and physiological data for the corresponding brain regions, after which the missing information is completed with observations from other areas and species. Subsequently, we explore the parameter space within biological ranges by comparing the simulated dynamics against electrophysiological in vivo single-unit activity. Standardized statistical test metrics enable the quantitative assessment of similarity between the network models and experimental recordings, on the level of the single-unit spike train dynamics [4]. This enables parameter sets to be identified for which the models produce realistic dynamics.Extracellular recordings from macaque motor (M1, PMd) [5], prefrontal (FEF) and visual (V4) [6] cortices are used to construct models of the respective areas. A quantitative assessment of the model parameters provides insight into whether communication within higher cortical areas shares comparable principles with sensory areas or follows different schemes. Future work will integrate these models into a single large-scale cortical multi-area model, extending previous work [7, 8].References: 1. R. J. Douglas, K. A. C. Martin and D. Whitteridge. A canonical microcircuit for neocortex. Neural computation 1.4, 480-488 (1989), 10.1162/neco.1989.1.4.480 2. A. A. Prinz, D. Bucher and E. Marder. Similar network activity from disparate circuit parameters. Nature Neuroscience 7, 1345-1352 (2004), 10.1038/nn1352 3. T. C. Potjans and M. Diesmann. The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model. Cerebral Cortex 24(3), 785–806 (2014), 10.1093/cercor/bhs358 4. R. Gutzen, M. von Papen, G. Trensch, P. Quaglio, S. Grün, and M. Denker. Reproducible neural network simulations: Statistical methods for model validation on the level of network activity data. Frontiers in Neuroinformatics, 12:90, (2018), 10.3389/fninf.2018.00090 5. B. E. Kilavik. Directional selectivity across macaque motor cortical layers during reach planning and execution. Program No. 587.19. 2018 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2018. Online. 6. G. G. Gregoriou, S.J. Gotts and R. Desimone. Cell-type-specific synchroniza-tion of neural activity in FEF with V4 during attention. Neuron 73:581-594,2012. 7. M. Schmidt, R. Bakker, C. C. Hilgetag, M. Diesmann, and S. J. van Albada. Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function 223(3), 1409-1435 (2018), 10.1007/s00429-017-1554-4 8. M. Schmidt, R. Bakker, K. Shen, G. Bezgin, M. Diesmann, and S. J. van Albada. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology 14(10), e1006359 (2018), 10.1371/journal.pcbi.1006359
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