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@INPROCEEDINGS{VanDael:1047420,
      author       = {Van Dael, Lies and Pronold, Jari and Shimoura, Renan and
                      Rostami, Vahid and Morales-Gregorio, Aitor and van Albada,
                      Sacha},
      title        = {{J}oint excitatory-inhibitory clustering supports realistic
                      spiking statistics and signal propagation},
      reportid     = {FZJ-2025-04289},
      year         = {2025},
      abstract     = {The connectivity among billions of neurons underlies the
                      brain’s cognitive and information processing functions.
                      Fully characterizing the single-neuron structural
                      connectivity of the entire brain is technically not
                      feasible. Furthermore, most studies focus on a single scale
                      (population, area or single-neuron level); thus, bridging
                      brain scales remains a challenge in neuroscience.
                      Experimental data indicate that neuronal connections in the
                      cerebral cortex are clustered, with stronger connections
                      within clusters and weaker ones across them. Previous models
                      linked such clustering to observed cortical activity.
                      However, the mechanisms governing large-scale cortical
                      dynamics - specifically reliable inter-area signal
                      propagation, while maintaining stable activity and realistic
                      spiking statistics- remain unclear. This study examines how
                      joint clustering of excitatory and inhibitory cells
                      contributes to explaining these dynamics. We hypothesize
                      that local cortical circuits form joint clusters of
                      excitatory and inhibitory neurons, and explore how this
                      affects resting-state activity, inter-area signal
                      transmission, and trial-to-trial variability. Building on a
                      previously developed unclustered spiking neural network
                      model of all vision-related areas in one hemisphere of the
                      macaque cortex, each cortical area is modeled by a 1 mm2
                      microcircuit with biologically realistic neuron and synapse
                      densities, avoiding downscaling artifacts. We extend this by
                      subdividing each area equally into a number of joint
                      excitatory-inhibitory clusters. Figure 1 schematically shows
                      this organization.Validation against in vivo resting-state
                      data reveals that the model with joint clusters of
                      excitatory and inhibitory cells matches observed activity
                      better than the unclustered model, in terms of firing
                      behavior, firing rate distributions, and inter-spike
                      interval variability. This type of clustering also enables
                      reliable signal propagation across areas in feedforward and
                      feedback directions, with biologically plausible response
                      latencies. Finally, the clustered model reproduces the
                      experimentally observed phenomenon of reduced trial-to-trial
                      variability in response to stimulus onset.To conclude, these
                      results demonstrate that joint clustering of excitatory and
                      inhibitory neurons is a plausible organizational principle
                      of local cortical circuits. This architecture simultaneously
                      supports resting-state spiking statistics, inter-area signal
                      propagation, and trial-to-trial variability dynamics.},
      month         = {Sep},
      date          = {2025-09-29},
      organization  = {Bernstein Conference, Frankfurt am
                       Main (Germany), 29 Sep 2025 - 2 Oct
                       2025},
      subtyp        = {Other},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / GRK 2416 - GRK 2416: MultiSenses-MultiScales:
                      Neue Ansätze zur Aufklärung neuronaler multisensorischer
                      Integration (368482240) / DFG project G:(GEPRIS)313856816 -
                      SPP 2041: Computational Connectomics (313856816) /
                      Brain-Scale Simulations $(jinb33_20220812)$},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(GEPRIS)368482240 /
                      G:(GEPRIS)313856816 / $G:(DE-Juel1)jinb33_20220812$},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1047420},
}