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@ARTICLE{ObersteFrielinghaus:1045452,
      author       = {Oberste-Frielinghaus, Jonas and Kurth, Anno C. and Göltz,
                      Julian and Kriener, Laura and Ito, Junji and Petrovici,
                      Mihai A. and Grün, Sonja},
      title        = {{S}ynchronization and semantization in deep spiking
                      networks},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-03504},
      year         = {2025},
      abstract     = {Recent studies have shown how spiking networks can learn
                      complex functionality through error-correcting plasticity,
                      but the resulting structures and dynamics remain poorly
                      studied. To elucidate how these models may link to observed
                      dynamics in vivo and thus how they may ultimately explain
                      cortical computation, we need a better understanding of
                      their emerging patterns. We train a multi-layer spiking
                      network, as a conceptual analog of the bottom-up visual
                      hierarchy, for visual input classification using spike-time
                      encoding. After learning, we observe the development of
                      distinct spatio-temporal activity patterns. While input
                      patterns are synchronous by construction, activity in early
                      layers first spreads out over time, followed by
                      re-convergence into sharp pulses as classes are gradually
                      extracted. The emergence of synchronicity is accompanied by
                      the formation of increasingly distinct pathways, reflecting
                      the gradual semantization of input activity. We thus observe
                      hierarchical networks learning spike latency codes to
                      naturally acquire activity patterns characterized by
                      synchronicity and separability, with pronounced excitatory
                      pathways ascending through the layers. This provides a
                      rigorous computational hypothesis for the experimentally
                      observed synchronicity in the visual system as a natural
                      consequence of deep learning in cortex.},
      keywords     = {Neurons and Cognition (q-bio.NC) (Other) / Neural and
                      Evolutionary Computing (cs.NE) (Other) / Computation
                      (stat.CO) (Other) / FOS: Biological sciences (Other) / FOS:
                      Computer and information sciences (Other)},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) / JL SMHB
                      - Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / Algorithms of Adaptive Behavior and
                      their Neuronal Implementation in Health and Disease
                      (iBehave-20220812) / HDS LEE - Helmholtz School for Data
                      Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)945539 /
                      G:(EU-Grant)101147319 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(DE-Juel-1)iBehave-20220812 /
                      G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2508.12975},
      url          = {https://juser.fz-juelich.de/record/1045452},
}