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@PHDTHESIS{KorcsakGorzo:1050546,
      author       = {Korcsak-Gorzo, Agnes},
      title        = {{F}unctions of spiking neural networks constrained by
                      biology},
      volume       = {119},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2026-00306},
      isbn         = {978-3-95806-876-6},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {xvi, 145},
      year         = {2025},
      note         = {Dissertation, RWTH Aachen University, 2025},
      abstract     = {Artificial intelligence (AI) solutions are increasingly
                      taking on tasks traditionally performed by humans. However,
                      their rising computational demands and energy consumption
                      are unsustainable, highlighting the need for more efficient
                      designs. The human brain, evolved to function effectively
                      even when energy is scarce, offers inspiration. Since
                      learning is central to both artificial intelligence and the
                      brain, insights about its underlying principles can deepen
                      our understanding of human learning while informing the
                      development of algorithms that transcend purely
                      engineering-based methods. This thesis investigates
                      biological learning through two studies, examining it from
                      mechanistic and functional perspectives at an abstraction
                      level commonly employed in neurophysics and computational
                      neuroscience. These fields distill complex neural systems
                      and phenomena into tractable mathematical and computational
                      models, enabling insights beyond the reach of traditional
                      biological approaches. Recognizing that synapses — the
                      connections between neurons — are fundamental to learning,
                      the thesis begins with a review of state-of-the-art
                      computational neuroscience methods for modeling synaptic
                      organization. This review highlights critical aspects of
                      synaptic signaling, including connectivity, transmission,
                      plasticity, and heterogeneity. In the first study, a
                      synaptic plasticity model is integrated into a spiking
                      neural network simulator and extended with biologically
                      plausible features, for example, continuous dynamics and
                      increased locality. The effectiveness of this enhanced model
                      is demonstrated by training it on a standard neuromorphic
                      benchmark task, incorporating biologically realistic sparse
                      connectivity and weight constraints. The second study
                      demonstrates that the sampling efficiency of pre-trained
                      spiking neural networks can be enhanced by exposing them to
                      oscillating background spiking activity. Analogous to
                      simulated tempering, these rhythmic oscillations modulate
                      state space exploration, facilitating transitions between
                      high-probability states within the learned representation.
                      These findings establish a link between cortical
                      oscillations and sampling-based computations, offering new
                      insights into memory retrieval and consolidation from a
                      computational perspective. The research involves developing
                      mathematical and computational models, which are simulated
                      on high-performance computing systems, evaluating learning
                      and sampling performance using standard machine learning
                      metrics, and assessing computational efficiency by analyzing
                      runtime. This thesis shows how biologically inspired
                      mechanisms enhance the functional capabilities of spiking
                      neural networks and how they can guide the development of
                      scalable and efficient AI systems.},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5232 - Computational Principles (POF4-523) / 5234 -
                      Emerging NC Architectures (POF4-523) / HBP SGA1 - Human
                      Brain Project Specific Grant Agreement 1 (720270) / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / ACA - Advanced Computing Architectures (SO-092) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027) / $HiRSE_PS$ - Helmholtz
                      Platform for Research Software Engineering - Preparatory
                      Study $(HiRSE_PS-20220812)$ / BMBF 03ZU1106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU1106CB) / Brain-Scale Simulations
                      $(jinb33_20220812)$},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5234 /
                      G:(EU-Grant)720270 / G:(EU-Grant)785907 / G:(EU-Grant)945539
                      / G:(DE-HGF)SO-092 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      $G:(DE-Juel-1)HiRSE_PS-20220812$ /
                      G:(DE-Juel1)BMBF-03ZU1106CB / $G:(DE-Juel1)jinb33_20220812$},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2601271033499.327456697914},
      doi          = {10.34734/FZJ-2026-00306},
      url          = {https://juser.fz-juelich.de/record/1050546},
}