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@INPROCEEDINGS{Nestler:1009239,
      author       = {Nestler, Sandra and Keup, Christian and Dahmen, David and
                      Gilson, Matthieu and Rauhut, Holger and Boutaib, Youness and
                      Bouss, Peter and Merger, Claudia Lioba and Fischer, Kirsten
                      and Rene, Alexandre and Helias, Moritz},
      title        = {{A} statistical perspective on learning of time series in
                      neural networks},
      reportid     = {FZJ-2023-02701},
      year         = {2023},
      abstract     = {In this talk, we explore a statistical perspective on
                      learning in neural networks, drawing inspiration from both
                      neuroscience and machine learning. We investigate the
                      stochastic nature of neural activity and stimuli and utilize
                      tools from statistical physics to address these aspects. The
                      focus lies on the time-dependent processing of
                      stimuli.Recurrent neural networks, a concept inspired by the
                      brain, handle time series naturally. For weakly non-linear
                      interactions, a method is developed to approximate network
                      dynamics, leading to improved performance in a random
                      recurrent reservoir. For the scenario of linear
                      interactions, we investigate how the optimal classifier
                      balances stability and performance in the presence of
                      background noise.We then study how non-linear interactions
                      shape the statistical processing of stimuli, demonstrating a
                      direct relationship between non-linearity, representation,
                      and higher-order statistics using a single-layer perceptron.
                      Moreover, we explore learning the data distribution itself,
                      employing an invertible neural network (normalizing flows)
                      to extract informative modes. This unsupervised approach
                      uncovers underlying structure, dimensionality, and
                      meaningful latent features in the data.},
      month         = {Jul},
      date          = {2023-07-13},
      organization  = {Seminar Talk, Machine Learning
                       Seminar, Aachen (Germany), 13 Jul 2023
                       - 13 Jul 2023},
      subtyp        = {Invited},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / 5234 - Emerging NC
                      Architectures (POF4-523) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / ACA - Advanced
                      Computing Architectures (SO-092) / RenormalizedFlows -
                      Transparent Deep Learning with Renormalized Flows
                      (BMBF-01IS19077A) / SDS005 - Towards an integrated data
                      science of complex natural systems (PF-JARA-SDS005) /
                      neuroIC002 - Recurrence and stochasticity for neuro-inspired
                      computation (EXS-SF-neuroIC002) / GRK 2416 - GRK 2416:
                      MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(DE-HGF)POF4-5234 / G:(EU-Grant)945539 / G:(DE-HGF)SO-092
                      / G:(DE-Juel-1)BMBF-01IS19077A / G:(DE-Juel-1)PF-JARA-SDS005
                      / G:(DE-82)EXS-SF-neuroIC002 / G:(GEPRIS)368482240},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1009239},
}