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@INPROCEEDINGS{Nestler:1006561,
      author       = {Nestler, Sandra and Helias, Moritz and Gilson, Matthieu},
      title        = {{N}euronal extraction of statistical patterns embedded in
                      time series},
      reportid     = {FZJ-2023-01709},
      year         = {2023},
      abstract     = {Neuronal systems need to process temporal signals. Here, we
                      hypothesize that temporal (co-)fluctuations –
                      corresponding to high-order statistics beyond the average
                      activity – are relevant for computation. The proposed
                      biologically inspired feedforward neuronal model is able to
                      extract information from up to the third order cumulant to
                      perform time series classification. This model relies on a
                      nonlinear gain function to combine the different statistical
                      orders of the inputs, after a usual weighted summation. In
                      addition to the afferent synaptic weights, the nonlinear
                      gain function is optimized, which enables the combination of
                      cumulants in a synergistic manner to maximize the
                      classification accuracy. The approach is demonstrated both
                      on synthetic and real world datasets of multivariate time
                      series to test the classification performance and to
                      interpret the tunable gain function. Moreover, we show that
                      our biological scheme makes a better use of the number of
                      trainable parameters as compared to a classical
                      machine-learning scheme. Our findings emphasize the benefit
                      of biological neuronal architectures, paired with dedicated
                      learning algorithms, for the processing of information
                      embedded in higher-order statistical cumulants of temporal
                      (co-)fluctuations.},
      month         = {Mar},
      date          = {2023-03-09},
      organization  = {Cosyne 2023, Montreal (Canada), 9 Mar
                       2023 - 14 Mar 2023},
      subtyp        = {After Call},
      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)},
      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},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1006561},
}