This title appears in the Scientific Report :
2015
Please use the identifier:
http://hdl.handle.net/2128/9755 in citations.
Construction of a multi-scale spiking model of macaque visual cortex
Construction of a multi-scale spiking model of macaque visual cortex
Understanding the relationship between structure and dynamics of the mammalian cortex is a key challenge of neuroscience. So far, it has been tackled in two ways: by modeling neurons or small circuits in great detail, and through large-scale models representing each area with a small number of diffe...
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Personal Name(s): | Schmidt, Maximilian (Corresponding author) |
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Bakker, Rembrandt / Diesmann, Markus / van Albada, Sacha | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Computational and Systems Neuroscience; IAS-6 |
Imprint: |
2015
|
PubMed ID: |
30335761 |
Document Type: |
Preprint |
Research Program: |
Brain-Scale Simulations The Human Brain Project Brain-inspired multiscale computation in neuromorphic hybrid systems Connectivity and Activity Theory, modelling and simulation Brain-Scale Simulations Brain-Scale Simulations Supercomputing and Modelling for the Human Brain |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Understanding the relationship between structure and dynamics of the mammalian cortex is a key challenge of neuroscience. So far, it has been tackled in two ways: by modeling neurons or small circuits in great detail, and through large-scale models representing each area with a small number of differential equations. To bridge the gap between these two approaches, we construct a spiking network model extending earlier work on the cortical microcircuit by Potjans & Diesmann (2014) to all 32 areas of the macaque visual cortex in the parcellation of Felleman & Van Essen (1991). The model takes into account spe- cific neuronal densities and laminar thicknesses of the individual areas. The connectivity of the model combines recently updated binary tracing data from the CoCoMac database (Stephan et al., 2001) with quantitative tracing data providing connection densities (Markov et al., 2014a) and laminar connection patterns (Stephan et al., 2001; Markov et al., 2014b). We estimate missing data using structural regular- ities such as the exponential decay of connection densities with distance between areas (Ercsey-Ravasz et al., 2013) and a fit of laminar patterns versus logarithmic ratios of neuron densities. The model integrates a large body of knowledge on the structure of macaque visual cortex into a consistent framework that allows for progressive refinement. |