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@INPROCEEDINGS{Bos:256315,
      author       = {Bos, Hannah and Schücker, Jannis and Diesmann, Markus and
                      Helias, Moritz},
      title        = {{I}dentifying and exploiting the anatomical origin of
                      population rate oscillations in spiking networks},
      reportid     = {FZJ-2015-06278},
      year         = {2015},
      abstract     = {Fast oscillations of the population firing rate in the high
                      gamma range (50-200 Hz), where individual neurons fire
                      slowly and irregularly, are observed in the living brain and
                      in network models of leaky integrate-and-fire (LIF) neurons,
                      that have also been studied analytically [1]. However, a
                      systematic approach identifying sub-circuits responsible for
                      specific oscillations in a structured network of neural
                      populations is currently unavailable.We consider a
                      large-scale, neural network consisting of 4 layers each
                      composed of an excitatory and inhibitory population of
                      LIF-neurons with connectivity determined by
                      electrophysiological and anatomical studies [2]. In
                      simulations we observe a peak in the power spectrum around
                      83 Hz in all populations and low frequency oscillations with
                      smaller power in a subset of the populations. Mapping the
                      dynamics of the fluctuations to an effective linear rate
                      model, using the recently derived transfer function for
                      LIF-neurons with synaptic filtering [3], we derive the
                      spectra of the population firing rates
                      analytically.Decomposing the noise-driven fluctuations into
                      eigenmodes of the effective connectivity, we identify the
                      modes responsible for peaks in the spectra. Applying
                      perturbation theory, we quantify the influence of individual
                      anatomical connections on the spectrum at given frequencies
                      and identify a sub-circuitry, localized in the
                      supra-granular and granular layer, generating the
                      oscillation. These findings are in agreement with
                      layer-specific local field potential measurements in the
                      Macaque primary visual cortex, where gamma-frequency
                      oscillations were mostly pronounced in layer 2,3 and 4B [4].
                      We exploit this method i) to identify the connectivity loops
                      responsible for the observed peaks and ii) to alter the
                      circuitry in a targeted manner to control the position and
                      amplitude of the peaks and the generation of slow frequency
                      fluctuations. This requires removal and addition of only
                      small numbers of synapses. The analytical framework moreover
                      explains the suppression of higher frequencies by
                      distributed delays and the amplification of population
                      specific oscillatory input. Mapping the stimulus vector onto
                      the eigenmodes of the system shows how the components of the
                      input vector are processed in the network. Thus one can
                      derive the sensitivity of the population rate dynamics to
                      the direction and frequency of stimuli.Our method finds
                      application in the identification of the connectivity loops
                      that determine emergent and externally driven global
                      measures of activity observable in experiments as well as in
                      engineering circuits that exhibit desired correlations on
                      the population level.},
      month         = {Jul},
      date          = {2015-07-18},
      organization  = {CNS 2015 Prague, Prague (Czech
                       Republic), 18 Jul 2015 - 23 Jul 2015},
      subtyp        = {Other},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 89571 -
                      Connectivity and Activity (POF2-89571) / HASB - Helmholtz
                      Alliance on Systems Biology (HGF-SystemsBiology) / MSNN -
                      Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
                      / SMHB - Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF2-89571 /
                      G:(DE-Juel1)HGF-SystemsBiology /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/256315},
}