000227465 001__ 227465
000227465 005__ 20190317000700.0
000227465 0247_ $$2doi$$a10.1371/journal.pcbi.1005507
000227465 022__ $$a1553-734X
000227465 02470 $$2ISI$$a000402542900041
000227465 037__ $$aARTICLE
000227465 245__ $$aTowards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size
000227465 260__ $$aSan Francisco$$bPublic Library of Science$$c2017
000227465 269__ $$a2017
000227465 300__ $$a63
000227465 336__ $$aJournal Articles
000227465 500__ $$aSimulation code available from https://github.com/schwalger/mesopopdyn_gif
000227465 500__ $$aThis project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 720270 and from the European Research Council under grant agreement No. 268689, MultiRules.
000227465 520__ $$aNeural 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.
000227465 700__ $$0247354$$aSchwalger, Tilo$$g233212
000227465 700__ $$0246749$$aDeger, Moritz$$g228519
000227465 700__ $$0240007$$aGerstner, Wulfram$$g111732
000227465 773__ $$j13$$k4$$qe1005507$$tPLoS Computational Biology
000227465 8564_ $$uhttps://github.com/schwalger/mesopopdyn_gif$$zURL
000227465 8564_ $$uhttp://lcn.epfl.ch/~schwalge/$$zURL
000227465 8564_ $$s2506747$$uhttps://infoscience.epfl.ch/record/227465/files/2017_Schwalger_finiteN_popdyn.pdf$$yPostprint$$zPostprint
000227465 8564_ $$s3833856$$uhttps://infoscience.epfl.ch/record/227465/files/journal.pcbi.1005507.pdf$$yPublisher's version$$zPublisher's version
000227465 909C0 $$0252006$$pLCN
000227465 909CO $$ooai:infoscience.tind.io:227465$$pIC$$particle$$pSV$$qGLOBAL_SET
000227465 917Z8 $$x233212
000227465 917Z8 $$x144315
000227465 937__ $$aEPFL-ARTICLE-227465
000227465 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000227465 980__ $$aARTICLE