227465
20190317000700.0
doi
10.1371/journal.pcbi.1005507
1553-734X
ISI
000402542900041
ARTICLE
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
San Francisco
Public Library of Science
2017
2017
63
Journal Articles
Simulation code available from https://github.com/schwalger/mesopopdyn_gif
This project received funding from the European Unionâ€™s Horizon 2020 research and innovation programme under grant agreement No. 720270 and from the European Research Council under grant agreement No. 268689, MultiRules.
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50 -- 2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics like finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly simulate a model of a local cortical microcircuit consisting of eight neuron types. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.
247354
Schwalger, Tilo
233212
246749
Deger, Moritz
228519
240007
Gerstner, Wulfram
111732
13
4
e1005507
PLoS Computational Biology
https://github.com/schwalger/mesopopdyn_gif
URL
http://lcn.epfl.ch/~schwalge/
URL
2506747
http://infoscience.epfl.ch/record/227465/files/2017_Schwalger_finiteN_popdyn.pdf
Postprint
Postprint
3833856
http://infoscience.epfl.ch/record/227465/files/journal.pcbi.1005507.pdf
Publisher's version
Publisher's version
252006
LCN
oai:infoscience.tind.io:227465
IC
article
SV
GLOBAL_SET
233212
144315
EPFL-ARTICLE-227465
EPFL
REVIEWED
PUBLISHED
ARTICLE