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@INPROCEEDINGS{Jiang:890909,
      author       = {Jiang, Han-Jia and van Albada, Sacha},
      title        = {{A} {M}ulti-layer {M}icrocircuit {M}odel of {S}omatosensory
                      {C}ortex with {M}ultiple {I}nterneuron {C}lasses},
      school       = {University of Cologne},
      reportid     = {FZJ-2021-01240},
      year         = {2021},
      abstract     = {Three major classes of GABAergic inhibitory interneurons
                      play critical and distinct roles in the regulation of
                      cortical network dynamics and signal processing [1, 2]. The
                      mechanisms of regulation are linked to the anatomical and
                      physiological diversity of these interneurons. While
                      experimental studies provide realistic observations,
                      neuronal network simulations with parameters from
                      experimental data can aid in direct exploration of the
                      mechanisms and conveniently test hypotheses. Therefore, we
                      develop a microcircuit model of mouse somatosensory (barrel)
                      cortex incorporating three major classes of interneurons as
                      a tool to study cortical network dynamics and sensory signal
                      processing.The simulation software NEST 2.16.0 [3] is used
                      to create a multi-layer (L2/3, L4, L5, and L6) cortical
                      microcircuit model adapted from [4]. The neuron model is the
                      leaky-integrate-and-fire neuron model with exponentially
                      decaying post-synaptic currents (PSC). The network includes
                      populations of excitatory (Exc) cells and three classes of
                      interneurons: parvalbumin (PV), somatostatin (SOM) and
                      vasoactive intestinal peptide (VIP) cells. The
                      layer-specific numbers of each cell type are based on the
                      data of a mouse barrel column [5, 6]. Cell-type-specific
                      membrane parameters are according to the measurements in
                      [7]. Probabilities of recurrent connections are determined
                      from experimental data of paired recordings or algorithmic
                      estimates by [8]. Each neuron receives optimized background
                      inputs with cell-type-specific numbers of connections. Two
                      hundred thalamic cells are created and connected to Exc and
                      PV cells with layer-specific probabilities from [9] to test
                      the network responses to a 10-ms transient thalamic input of
                      100 spikes/s. The weights of excitatory recurrent
                      connections are set according to local intracortical unitary
                      excitatory postsynaptic potentials (uEPSPs) [5], while those
                      of inhibitory connections are multiplied by a relative
                      inhibitory strength (g). The background and thalamic inputs
                      are only excitatory, with weights according to
                      thalamocortical uEPSPs [10]. Synaptic short-term
                      plasticities (STPs) are included in the recurrent
                      connections according to the Tsodyks model [11]. The STP
                      parameters of different connections are individually fitted
                      to match the experimentally measured STPs.The ground-state
                      firing rates of the populations in the optimized model are
                      comparable to those of in vivo data from [12], although some
                      deviations remain (Figure 1A). The lower firing rates of VIP
                      cells and Exc cells in L5 and L6 compared to the in vivo
                      data are possibly due to the incomplete STP and connectivity
                      data (data not shown), respectively. The changes of firing
                      rates in L2/3 in response to activation of PV, SOM, and VIP
                      cells show their respective roles of inhibition and
                      disinhibition (Figure 2A), as observed in most experimental
                      studies. Over a range of external input and relative
                      inhibitory strengths, the model is able to fulfill the
                      criteria on firing rates, spiking irregularity, and pairwise
                      correlations of spike counts derived in [13] (Figure 1B). In
                      addition, the model with STPs shows clearer multi-layer
                      spiking responses to simulated transient thalamic input as
                      compared to a version without STPs (Figure 2B). Further
                      mechanistic analysis of the model may reveal the mechanisms
                      behind these results and explore the specific roles of
                      different interneuron types in state-dependent modulation of
                      sensory signal processing. Specifically, with the
                      experimental data of the thalamic input responsible for the
                      whisker sensation [9], this model is useful in simulating
                      somatosensory inputs and provides a tool to analyze the
                      roles of interneurons in its regulation and signal
                      processing.},
      month         = {Feb},
      date          = {2021-02-01},
      organization  = {The 5th HBP Student Conference,
                       virtual (virtual), 1 Feb 2021 - 4 Feb
                       2021},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/890909},
}