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@INPROCEEDINGS{Schutzeichel:1044515,
      author       = {Schutzeichel, Lars and Bauer, Jan and Bouss, Peter and
                      Musall, Simon and Dahmen, David and Helias, Moritz},
      title        = {{R}ecurrent network dynamics underlying transient sensory
                      stimulus representations in mice},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2025-03252},
      year         = {2025},
      abstract     = {Different stimuli elicit different transient neural
                      responses in the brain. How is theinformation represented in
                      the parallel neuronal activity and how is it reshaped by
                      thedynamics of local recurrent circuits? We investigate
                      these questions in Neuropixels recordings of awake behaving
                      mice and recurrent neural network models by inferring the
                      stimulusclass from the network activity.We employ methods
                      from statistical physics of disordered systems to derive a
                      two-replica mean-field theory that reduces complex network
                      dynamics to three dynamicalquantities that fully determine
                      the separability of stimulus representations. These
                      dynamical quantities are the mean population activity $R$
                      and the overlaps $Q^{=}$ and $Q^{\neq}$,representing
                      response variability within or across stimulus classes,
                      respectively.Mean-field theory predicts the time evolution
                      of $R$, $Q^{=}$, and $Q^{\neq}$ and enables us to
                      quantitatively explain experimental observables. The
                      analytical theory predicts the temporaldynamics of stimulus
                      separability as an interplay of firing rate dynamics,
                      controlled byinhibitory balancing, and overlaps, governed by
                      chaotic dynamics.The analysis of mutual information of an
                      optimally trained readout on the populationsignal reveals a
                      trade-off between more information conveyed with an
                      increasing numberof stimuli, and stimuli becoming less
                      separable due to their increased overlap in the
                      finite-dimensional neuronal space. We find that the
                      experimentally observed small populationactivity $R$ lies in
                      a regime where information grows with the number of stimuli,
                      which issharply separated from a second regime, in which
                      information converges to zero, revealinga crucial advantage
                      of sparse coding.},
      month         = {Jul},
      date          = {2025-07-13},
      organization  = {29th International Conference on
                       Statistical Physics, Florence (Italy),
                       13 Jul 2025 - 18 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/1044515},
}