This title appears in the Scientific Report :
2018
Please use the identifier:
http://hdl.handle.net/2128/19602 in citations.
Quantitative comparison of a mesocircuit model with motor cortical resting state activity in the macaque monkey
Quantitative comparison of a mesocircuit model with motor cortical resting state activity in the macaque monkey
Modeling studies of cortical network dynamics frequently aim to include realistic assumptions on structural and effective connectivity [Voges & Perrinet, 2012; Potjans & Diesmann, 2014] to achieve a qualitative reproduction of experimentally observed neuronal activity. Here, we develop a qua...
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Personal Name(s): | von Papen, Michael (Corresponding author) |
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Voges, Nicole / Dabrowska, Paulina / Senk, Johanna / Hagen, Espen / Diesmann, Markus / Dahmen, David / Deutz, Lukas / Helias, Moritz / Brochier, Thomas / Riehle, Alexa / Grün, Sonja | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Imprint: |
2018
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Conference: | Computational Neuroscience 2018, Seattle (USA), 2018-07-13 - 2018-07-18 |
Document Type: |
Poster |
Research Program: |
Theory of multi-scale neuronal networks Human Brain Project Specific Grant Agreement 2 Theory, modelling and simulation Connectivity and Activity |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Modeling studies of cortical network dynamics frequently aim to include realistic assumptions on structural and effective connectivity [Voges & Perrinet, 2012; Potjans & Diesmann, 2014] to achieve a qualitative reproduction of experimentally observed neuronal activity. Here, we develop a quantitative validation approach where mean-field theory [Dahmen et al., 2017] guides the adaptation of a generic point-neuron network model to macaque motor cortex. We describe the characteristics of the experimental data extracted and used for comparison and present preliminary results for the generic network model.<br/>The underlying network model is an upscaled version of the Potjans & Diesmann (2014) layered spiking network model extended to a size of 4x4mm2 and a total of ~1.2 million leaky integrate-and-fire neurons [Hagen et al., 2016]. In contrast to the original model this mesocircuit model uses lateral distance-dependent connection probabilities derived from cortical neuroanatomical data. To compare the output with observations we subsample single unit activities from the corresponding layer in the simulated network with the same number of neurons and with the same spatial arrangement of the recording array as in the experimental data.<br/>The model describes a system in ground, idle or resting state with uncorrelated input. In order to perform a quantitative comparison with experimental data we therefore conducted a resting state experiment with macaque monkeys not given any specific task or stimulus. We recorded neuronal activity from premotor and motor cortex using a chronically implanted 4x4mm2 Utah array with 100 electrodes [Riehle et al., 2013; Brochier et al., 2018]. A video of the monkey was used to differentiate between periods of rest and spontaneous movements.<br/>The experimental single unit activities (~140 neurons) are subdivided into putative excitatory and inhibitory neurons based on their spike widths. We find that a) putative inhibitory and excitatory activity is in a balanced state, b) spike counts increase during movement, c) inhibitory units contribute more strongly to firing rate modulations than excitatory units, d) they also tend to be more strongly correlated among each other and e) the dimensionality of cortical activity is decreased during movement. Our results are to a large degree in accordance with mean-field theoretic predictions and may thus allow us to infer constraints on the parameter space of the mesocircuit model. |