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@ARTICLE{Ness:1040966,
      author       = {Ness, Torbjørn V. and Tetzlaff, Tom and Einevoll, Gaute T.
                      and Dahmen, David},
      title        = {{O}n the validity of electric brain signal predictions
                      based on population firing rates},
      journal      = {PLoS Computational Biology},
      volume       = {21},
      number       = {4},
      issn         = {1553-734X},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2025-02088},
      pages        = {e1012303},
      year         = {2025},
      abstract     = {Neural activity at the population level is commonly studied
                      experimentally through measurements of electric brain
                      signals like local field potentials (LFPs), or
                      electroencephalography (EEG) signals. To allow for
                      comparison between observed and simulated neural activity it
                      is therefore important that simulations of neural activity
                      can accurately predict these brain signals. Simulations of
                      neural activity at the population level often rely on
                      point-neuron network models or firing-rate models. While
                      these simplified representations of neural activity are
                      computationally efficient, they lack the explicit spatial
                      information needed for calculating LFP/EEG signals.
                      Different heuristic approaches have been suggested for
                      overcoming this limitation, but the accuracy of these
                      approaches has not fully been assessed. One such heuristic
                      approach, the so-called kernel method, has previously been
                      applied with promising results and has the additional
                      advantage of being well-grounded in the biophysics
                      underlying electric brain signal generation. It is based on
                      calculating rate-to-LFP/EEG kernels for each synaptic
                      pathway in a network model, after which LFP/EEG signals can
                      be obtained directly from population firing rates. This
                      amounts to a massive reduction in the computational effort
                      of calculating brain signals because the brain signals are
                      calculated for each population instead of for each neuron.
                      Here, we investigate how and when the kernel method can be
                      expected to work, and present a theoretical framework for
                      predicting its accuracy. We show that the relative error of
                      the brain signal predictions is a function of the
                      single-cell kernel heterogeneity and the spike-train
                      correlations. Finally, we demonstrate that the kernel method
                      is most accurate for contributions which are also dominating
                      the brain signals: spatially clustered and correlated
                      synaptic input to large populations of pyramidal cells. We
                      thereby further establish the kernel method as a promising
                      approach for calculating electric brain signals from
                      large-scale neural simulations.},
      cin          = {IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / ACA - Advanced
                      Computing Architectures (SO-092) / MetaMoSim - Generic
                      metadata management for reproducible
                      high-performance-computing simulation workflows - MetaMoSim
                      (ZT-I-PF-3-026) / DFG project G:(GEPRIS)491111487 -
                      Open-Access-Publikationskosten / 2025 - 2027 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(EU-Grant)945539 / G:(DE-HGF)SO-092 /
                      G:(DE-Juel-1)ZT-I-PF-3-026 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {40228210},
      UT           = {WOS:001466754000003},
      doi          = {10.1371/journal.pcbi.1012303},
      url          = {https://juser.fz-juelich.de/record/1040966},
}