% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Schutzeichel:1031785,
      author       = {Schutzeichel, Lars and Bauer, Jan and Bouss, Peter and
                      Musall, Simon and Dahmen, David and Helias, Moritz},
      title        = {{I}nfluence of {C}ollective {N}etwork {D}ynamics on
                      {S}timulus {S}eparation},
      reportid     = {FZJ-2024-05812},
      year         = {2024},
      abstract     = {Different stimuli elicit different transient neural
                      responses in the brain. How is the information represented
                      in the parallel neuronal activity of a neuronal population?
                      We investigate this question in Neuropixel recordings of
                      awake behaving mice by inferring the stimulus class (e.g.,
                      visual or tactile) from the activity. To quantify the
                      separability, we use an optimally trained linear readout,
                      which can be understood through the framework of Bayesian
                      inference. We find that separability only depends on three
                      geometric properties of the neuronal population vector,
                      described by cones within the space of neuronal activity:
                      The length $R$, the width of the cones $Q^{intra}$, given by
                      the overlap of two vectors within classes, and the distance
                      between two cones $Q^{inter}$, given by the overlap across
                      two classes (A).We then analyze how the dynamics of a
                      recurrent network deforms stimulus representations as a
                      function of time. We employ a two-replica calculation to
                      compute the time-evolutions of $R$, $Q^{intra}$ and
                      $Q^{inter}$ In the limit of large networks, the dynamics of
                      this model are fully described by these three quantities,
                      which are matched to the corresponding experimental
                      observables. The analytical theory, in turn, predicts the
                      separability of the stimuli as a function of time and shows
                      how the time-evolution of separability can be understood by
                      the dynamic interplay between balance by inhibition, which
                      controls the time course of $R$, and chaotic dynamics, which
                      controls $Q^{intra}$ and $Q^{inter}$. Neither the decay of
                      the firing rate alone nor that of the overlaps is predictive
                      for separability, but rather their mutual relationships. For
                      stimuli with reliable responses, the network can transiently
                      increase the separability, as previously only seen for a
                      constant population mean [1].To further probe the network's
                      capacity for separation, we use mutual information to
                      quantify the information contained in the population signal
                      as a function of the number of stimuli. This reveals an
                      optimal number of stimuli from the trade-off between
                      encoding more information with more stimuli, and stimuli
                      becoming less separable due to their increased overlap in
                      neuron space (B). We show that the optimum depends on the
                      population mean, allowing for the embedding of more stimuli
                      for a smaller population mean.},
      month         = {Oct},
      date          = {2024-10-01},
      organization  = {Bernstein Conference, Frankfurt
                       (Germany), 1 Oct 2024 - 1 Oct 2024},
      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/1031785},
}