This title appears in the Scientific Report :
2016
Global stability reveals critical components in the structure of multi-scale neural networks
Global stability reveals critical components in the structure of multi-scale neural networks
One of the major challenges of computational neuroscience is the integration of available multi-scale experimental data into coherent models of the brain. Hereby, the exploration of the inevitable uncertainties of anatomical data should be guided by physiological observations. To this end we use a m...
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Personal Name(s): | Schücker, Jannis (Corresponding author) |
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Schmidt, Maximilian / van Albada, Sacha / Diesmann, Markus / Helias, Moritz | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 JARA-BRAIN; JARA-BRAIN Computational and Systems Neuroscience; IAS-6 |
Imprint: |
2016
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Conference: | 80. Jahrestagung der DPG und DPG-Frühjahrstagung, Regensburg (Germany), 2016-03-06 - 2016-03-11 |
Document Type: |
Conference Presentation |
Research Program: |
Theory, modelling and simulation (Dys-)function and Plasticity Connectivity and Activity |
Publikationsportal JuSER |
One of the major challenges of computational neuroscience is the integration of available multi-scale experimental data into coherent models of the brain. Hereby, the exploration of the inevitable uncertainties of anatomical data should be guided by physiological observations. To this end we use a mean-field reduction of spiking network dynamics, which allows us to include fundamental activity constraints, i.e., prohibiting quiescence and requiring global stability, to shape the phase space of large-scale network models. In particular, we apply this framework to a multi-area model of macaque visual cortex to obtain realistic layer- and area-specific activity. To achieve this, we control the location of the separatrix dividing the phase space into realistic low-activity behavior and unrealistic high-activity states. We identify and systematically modify components on the macroscopic level of cortical areas and on the layer-specific population level that are critical for the stability properties of the network. |