This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/19379 in citations.
Comparison of experimental monkey resting state data with large scale neural network simulations
Comparison of experimental monkey resting state data with large scale neural network simulations
To get a better understanding of network mechanisms, we study simulated neuronal networks validated against massively parallel spiking activities. Experimental data were recorded with a Utah array (100 electrodes, 4x4mm2, Blackrock Microsystems) from motor cortex of a monkey at rest. The simulated...
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Personal Name(s): | Dabrowska, Paulina (Corresponding author) |
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Voges, Nicole / von Papen, Michael / Riehle, Alexa / Brochier, Thomas / Senk, Johanna / Grün, Sonja / Diesmann, Markus | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 |
Imprint: |
2018
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Conference: | 11th FENS Forum of Neuroscience, Berlin (Germany), 2018-07-07 - 2018-07-11 |
Document Type: |
Poster |
Research Program: |
Human Brain Project Specific Grant Agreement 2 Supercomputing and Modelling for the Human Brain Human Brain Project Specific Grant Agreement 1 Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination Connectivity and Activity |
Link: |
OpenAccess |
Publikationsportal JuSER |
To get a better understanding of network mechanisms, we study simulated neuronal networks validated against massively parallel spiking activities. Experimental data were recorded with a Utah array (100 electrodes, 4x4mm2, Blackrock Microsystems) from motor cortex of a monkey at rest. The simulated network is a layered cortical microcircuit (Potjans & Diesmann 2014, simulated with NEST http://nest-initiative.org/) extended by lateral connections to 4x4mm2 corresponding to the cortical patch we recorded from. For a fair comparison the model data were subsampled to analogous number of excitatory and inhibitory neurons (140 in total) from the same layer (presumably layer 5). The experimental data were separated into putative inhibitory (INH) and excitatory (EXC) neurons according to their wave shape (Dehghani et al. 2016).To directly compare the experimental and simulated data, we estimated (using identical workflow and software, (http://neuralensemble.org/elephant/)) distributions of firing rates (FR), pairwise fine temporal correlations (CC), and rate covariances (COV). Both datasets agree in the sense that the COV of INH are larger than EXC. This is similar for the CC of the experimental data but differs in the model data. We find a similarly differing result for the FR. Presumably, the reason for this difference is that connectivity parameters in the model are derived mainly from cat and rat visual cortex. We subsequently aim to adjust the model connectivities to the ones derived from monkey motor cortex. |