March 01, 2018 15:00 - 16:00
BSI Central Building 1F Seminar Room
The cortical network exhibits organization on multiple levels but an integrated view is missing. In particular, it has been known for a long time that cortical architecture, the area-specific cellular and laminar composition of the network, is related to the connectivity between areas, forming a hierarchical and recurrent network at the brain scale. Based on earlier work on the cortical microcircuit, our recent study  integrates data on cortical architecture and axonal tracing data into a multi-scale framework describing one hemisphere of macaque vision-related cortex.
Simulating multi-area models at the level of resolution of neurons and synapses still taxes the largest supercomputers available , but downscaling entails the danger of perturbing the higher-order statistics of neuronal activity .
As a compromise between these constraints, we represent each area by the network below a square millimeter of cortical surface. These circuits capturing the majority of the local connections are modeled at full density, i.e., with their natural number of neurons and synapses. The resulting network contains a few million neurons and can be simulated using the simulation code NEST.
Simulations confirm a realistic activity regime after adjustments of the connectivity within the margins of error . At a sufficiently large coupling between the areas, spike patterns, the distribution of spike rates, and the power spectrum of the activity are compatible with in-vivo resting-state data. Furthermore, the matrix of correlations between the activities of areas is more similar to the experimentally measured functional connectivity of resting-state fMRI than the anatomical matrix.
This correspondence on multiple spatial scales is achieved in a metastable state exhibiting time scales much larger than any time constant of the system.
The open development of NEST is guided by the NEST Initiative. Partial funding comes from the Human Brain Project through EU grants 604102 and 720270.
 Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ (2017) Brain Struct Func, advance online doi: 10.1007/s00429-017-1554-4  Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S (2018) Front Neuroinform 12:2  van Albada SJ, Helias M, Diesmann M (2015). PLOS Comput Biol 11:e1004490  Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M (2017). PLOS Comput Biol 13:e1005179
- Open to Public
- Tomoki Fukai [Tomoki Fukai, Neural Circuit Theory ]
Name: Tomoki Fukai