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@INPROCEEDINGS{Schutzeichel:1044514,
      author       = {Schutzeichel, Lars and Bauer, Jan and Bouss, Peter and
                      Musall, Simon and Dahmen, David and Helias, Moritz},
      title        = {{D}ynamics of sensory stimulus representations in recurrent
                      neural networks and in mice},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2025-03251},
      year         = {2025},
      abstract     = {Different stimuli elicit different transient neural
                      responses in the brain. But how is theinformation
                      represented in the parallel neuronal activity and how is it
                      reshaped by the dynamics oflocal recurrent circuits? We
                      investigate these questions in Neuropixels recordings of
                      awake behavingmice and recurrent neural network models by
                      inferring stimulus classes (e.g., visual or tactile) from
                      thenetwork activity. We derive a mean-field theory that
                      reduces the dynamics of complex networks to onlythree
                      relevant dynamical quantities that fully determine the
                      separability of stimulus representations.These dynamical
                      quantities geometrically represent the length of the neural
                      state vector, given by themean population activity $R$, and
                      the typical overlaps Q= and Q≠ of neural state vectors
                      representingthe variability of responses within or across
                      stimulus classes, respectively.Mean-field theory predicts
                      the time evolution of $R$, $Q^{=}$ , and $Q^{\neq}$ and
                      enables us to quantitatively explainexperimental
                      observables. The analytical theory predicts the temporal
                      dynamics of stimulus separabilityas an interplay of firing
                      rate dynamics and overlaps. It reveals how inhibitory
                      balancing controls the timecourse of $R$ and chaotic
                      dynamics control $Q^{=}$ and $Q^{\neq}$ , exposing the
                      mechanisms underlying separabilitybetween stimuli.The
                      analysis of mutual information of an optimally trained
                      readout on the population signal revealsa trade-off between
                      more information conveyed with an increasing number of
                      stimuli, and stimuli be-coming less separable due to their
                      increased overlap in the finite dimensional neuronal space.
                      We findthat the experimentally observed small population
                      activity $R$ is located in a regime where informationgrows
                      extensively with the number of stimuli, which is sharply
                      separated from a second regime, in whichinformation
                      converges to zero, revealing a crucial advantage of sparse
                      coding. Our work thus providesa novel understanding of
                      separability of stimuli shaped by collective network
                      dynamics.},
      month         = {Jul},
      date          = {2025-07-05},
      organization  = {CNS 34th Annual Computational
                       Neuroscience Meeting, Florence (Italy),
                       5 Jul 2025 - 9 Jul 2025},
      subtyp        = {After Call},
      cin          = {IBI-3 / IAS-6},
      cid          = {I:(DE-Juel1)IBI-3-20200312 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / GRK 2416 - GRK 2416:
                      MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / DFG
                      project G:(GEPRIS)533396241 - Evolutionäre Optimierung
                      neuronaler Netzwerkdynamik auf eine empfängerspezifische
                      interareale Kommunikation (533396241)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(GEPRIS)368482240 / G:(GEPRIS)533396241},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1044514},
}