000173332 001__ 173332
000173332 005__ 20240313095020.0
000173332 037__ $$aFZJ-2014-06742
000173332 041__ $$aEnglish
000173332 1001_ $$0P:(DE-Juel1)145897$$aSchmidt, Maximilian$$b0$$eCorresponding Author
000173332 1112_ $$aAnnual meeting of the SfN$$cWashington, DC$$d2014-11-15 - 2014-11-19$$gSfN2014$$wUSA
000173332 245__ $$aA spiking multi-area network model of macaque visual cortex
000173332 260__ $$c2014
000173332 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1479205965_789
000173332 3367_ $$033$$2EndNote$$aConference Paper
000173332 3367_ $$2BibTeX$$aINPROCEEDINGS
000173332 3367_ $$2DRIVER$$aconferenceObject
000173332 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000173332 3367_ $$2ORCID$$aOTHER
000173332 520__ $$aThe primate visual cortex consists of a set of specialized areas whose inter-connections have been shown to influence its dynamics both in spontaneous and driven conditions. Hitherto, models of this system have either concentrated on local detailed circuits or studied the interplay of areas, each represented by a few dynamical equations. We present a model which bridges this gap between microscopic and macroscopic dynamics by extending a spiking model of a 1mm2 patch of early sensory cortex [1] to all vision-related areas of the macaque cortex. The single-cell dynamics is kept simple in order to bring out the influence of the complex connectivity, which is based on a systematic synthesis of anatomical and electrophysiological findings. The extension to multiple areas allows us to replace random inputs to the network in part by simulated synapses, thereby increasing the self-consistency of the model. Here the immediate aim is not to address network function from a top-down perspective but to explore the relationship between network structure and fundamental multi-scale activity states.Neuron densities and laminar thicknesses are taken from available data sets or determined based on structural regularities across the cortex. The cortico-cortical connectivity is defined combining binary information from a large number of data sets collected in the CoCoMac database [2] with quantitative data from retrograde tracing studies [3], which is completed by exploiting an exponential decay of connection densities over distance [4]. Furthermore, we implement laminar connection patterns [5] and estimate missing data using a sigmoidal relation between the fraction of supragranularly originating projections and architectural types of areas [6]. We perform simulations of the system using NEST and find a broad parameter regime with asynchronous, irregular spiking across populations, characteristic of spontaneous cortical activity. The rich connectivity structure is reflected in a complex pattern of firing rates across areas and populations, where inhibitory neurons show higher activity than excitatory cells despite identical intrinsic dynamics.
000173332 536__ $$0G:(DE-HGF)POF2-331$$a331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331)$$cPOF2-331$$fPOF II$$x0
000173332 536__ $$0G:(DE-HGF)POF2-89574$$a89574 - Theory, modelling and simulation (POF2-89574)$$cPOF2-89574$$fPOF II T$$x1
000173332 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x2
000173332 536__ $$0G:(DE-Juel1)jinb33_20121101$$aBrain-Scale Simulations (jinb33_20121101)$$cjinb33_20121101$$fBrain-Scale Simulations$$x3
000173332 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x4
000173332 536__ $$0G:(EU-Grant)604102$$aHBP - The Human Brain Project (604102)$$c604102$$fFP7-ICT-2013-FET-F$$x5
000173332 536__ $$0G:(DE-Juel1)BTN-Peta-2008-2012$$aBTN-Peta - The Next-Generation Integrated Simulation of Living Matter (BTN-Peta-2008-2012)$$cBTN-Peta-2008-2012$$fBTN-Peta-2008-2012$$x6
000173332 536__ $$0G:(DE-Juel1)jinb33_20121101$$aBrain-Scale Simulations (jinb33_20121101)$$cjinb33_20121101$$fBrain-Scale Simulations$$x7
000173332 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b1
000173332 7001_ $$0P:(DE-Juel1)145578$$aBakker, Rembrandt$$b2
000173332 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b3
000173332 773__ $$y2014
000173332 909CO $$ooai:juser.fz-juelich.de:173332$$popenaire$$pec_fundedresources$$pVDB
000173332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145897$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000173332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138512$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000173332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145578$$aForschungszentrum Jülich GmbH$$b2$$kFZJ
000173332 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich GmbH$$b3$$kFZJ
000173332 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000173332 9131_ $$0G:(DE-HGF)POF2-331$$1G:(DE-HGF)POF2-330$$2G:(DE-HGF)POF2-300$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lFunktion und Dysfunktion des Nervensystems$$vSignalling Pathways and Mechanisms in the Nervous System$$x0
000173332 9131_ $$0G:(DE-HGF)POF2-89574$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vTheory, modelling and simulation$$x1
000173332 9141_ $$y2014
000173332 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000173332 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000173332 980__ $$aabstract
000173332 980__ $$aVDB
000173332 980__ $$aI:(DE-Juel1)INM-6-20090406
000173332 980__ $$aI:(DE-Juel1)IAS-6-20130828
000173332 980__ $$aUNRESTRICTED
000173332 981__ $$aI:(DE-Juel1)IAS-6-20130828
000173332 981__ $$aI:(DE-Juel1)IAS-6-20130828