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000836547 037__ $$aFZJ-2017-05651
000836547 041__ $$aEnglish
000836547 1001_ $$0P:(DE-Juel1)168479$$aVoges, Nicole$$b0$$ufzj
000836547 1112_ $$aComputational Neuroscience Society$$cAntwerpen$$d2017-07-15 - 2017-07-20$$gCNS$$wBelgium
000836547 245__ $$aCharacterization of resting state dynamics in monkey motor cortex
000836547 260__ $$c2017
000836547 3367_ $$033$$2EndNote$$aConference Paper
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000836547 520__ $$aNowadays, modeling studies of cortical network dynamics aim to include realistic assumptions on structural and functional properties of the corresponding neurons [1,2]. Such models often do not consider functional aspects but rather describe the “ground”, “idle”, or “resting state” of the cortical network, typically characterized as asynchronous irregular spiking [2]. However, for model validation, i.e., for a concrete comparison of experimental  versus model data aiming at a more realistic model, one needs to compare this cortical state to the corresponding experimental data. Therefore we performed a “resting state” experiment (this term is adapted from human fMRI studies where it is defined as brain activity observed when the subject is at rest [3]). We recorded the neuronal activity from macaque monkey motor cortex with a chronically implanted 4x4mm2 100 electrode Utah Array (Blackrock Microsystems) for 15min, while the monkey was sitting in a chair without any task or given stimulus. This is in contrast to most neurophysiological studies that focus on a task- or stimulus-specific analysis [e.g. 4]. Based on a video recording of the monkey during the neuronal recording, we differentiate between “resting” intervals and intervals when the monkey spontaneously moved.The goal of this study is to thoroughly characterize the simultaneous spiking activity recorded from 146 single units during resting state. To enable a detailed comparison to simulated spiking data, we subdivide the single units into putative excitatory and inhibitory neurons based on their spike shapes [5]. We apply common statistical measures, e.g., firing rate, (local) coefficient of variation for single unit characterization, and we also compute the pairwise fine temporal correlation by correlation coefficients. These measures are calculated in two ways: averaged over time and single units, as well as averaged over time but separately for each single unit (except for the correlation coefficients). Comparing the distributions of these measures from the two behavioral states we do not find any difference – when averaging over single units. However, when focusing on non-averaged, single unit data we notice that some neurons increase their firing rates systematically when the monkey moves compared to rest, whereas others decrease or do not change their rates. Thus, there was seemingly no difference on the population level, but significant differences on the level of individual neurons. Moreover, we observe a strong  correlation between a few neuronal units, independent of their cortical distance, while others show lower or no correlation. Our next steps are to characterize if such findings are particularly different for excitatory and inhibitory neurons. Further, we aim to study the underlying network mechanisms. One possibility would be to re-consider the balancing effects of inhibition and recurrence [5,6].
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000836547 536__ $$0G:(GEPRIS)238707842$$aDFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842)$$c238707842$$x1
000836547 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000836547 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3
000836547 536__ $$0G:(EU-Grant)90251$$aSmartstart - SMARTSTART Training Program in Computational Neuroscience (90251)$$c90251$$x4
000836547 65027 $$0V:(DE-MLZ)SciArea-160$$2V:(DE-HGF)$$aBiology$$x0
000836547 7001_ $$0P:(DE-Juel1)171408$$aDabrowska, Paulina$$b1$$ufzj
000836547 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b2$$ufzj
000836547 7001_ $$0P:(DE-Juel1)164166$$aHagen, Espen$$b3$$ufzj
000836547 7001_ $$0P:(DE-Juel1)172858$$aRiehle, Alexa$$b4$$ufzj
000836547 7001_ $$0P:(DE-HGF)0$$aBrochier, Thomas$$b5
000836547 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b6$$ufzj
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000836547 9141_ $$y2017
000836547 920__ $$lno
000836547 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000836547 9201_ $$0I:(DE-Juel1)VDB1106$$kIAS$$lInstitute for Advanced Simulation$$x1
000836547 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000836547 9201_ $$0I:(DE-588b)1043886400$$kAMU$$lAix-Marseille Université $$x3
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