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@INPROCEEDINGS{Grn:1044915,
      author       = {Grün, Sonja},
      title        = {{S}pike patterns in nature and {AI}},
      reportid     = {FZJ-2025-03437},
      year         = {2024},
      abstract     = {The energy consumption of present AI systems is
                      unsustainable and undemocratic. Understanding the energy
                      efficiency of the brain may uncover pathways out of the
                      dilemma. A promising observation is that unlike artificial
                      neural networks, mammalian brains are sparse in space and
                      time. Spatial sparseness means that neurons are only
                      connected to a tiny fraction of the other neurons, temporal
                      sparseness means that on the few connections there is
                      activity only a few times per second. This architecture
                      suggests that the brain relies on the coordinated activity
                      of populations of neurons to process sensory information and
                      organize behavior.In the past three decades computational
                      neuroscience has learned how to extract and interpret the
                      dynamics of spatio-temporal patterns in massively parallel
                      spike trains. Despite this progress it is still unclear how
                      function and in particular learning is implemented by the
                      mammalian cortex. Reasons may be the undersampling of the
                      system and the limited capability to observe the system
                      during learning.Ironically in artificial neural networks we
                      have a similar situation. These networks can learn complex
                      tasks with high accuracy. But our understanding of what has
                      been learned and how the trained network solves the task is
                      limited. Progress has already been made by transferring
                      ideas of generic learning algorithms for artificial neural
                      networks to spiking systems. This gives us benchmarks on
                      what we can find out about such a system with statistical
                      tools, but the analysis may also give inspiration on the
                      working principles of the natural system.The talk first
                      reviews the state-of-the-art of the time-resolved analysis
                      of spike patterns required for undersampled brain data.
                      Subsequently, the talk reports on the analysis of a
                      spike-based artificial neural network trained on the MNIST
                      data set, which exhibits precise spike timing.},
      month         = {Oct},
      date          = {2024-10-15},
      organization  = {FIAS Neuroscience Seminar, Frankfurt
                       (Germany), 15 Oct 2024 - 16 Oct 2024},
      subtyp        = {Invited},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / HDS LEE - Helmholtz School for Data Science in
                      Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) /
                      Algorithms of Adaptive Behavior and their Neuronal
                      Implementation in Health and Disease (iBehave-20220812) /
                      BMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte
                      Technologien der künstlichen Intelligenz für die
                      Elektronik der Zukunft - NEUROTEC II - (16ME0400) /
                      In2PrimateBrains - Intra- and Inter-Areal Communication in
                      Primate Brain Networks (956669) / 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-Juel1)JL
                      SMHB-2021-2027 / G:(EU-Grant)101147319 /
                      G:(DE-Juel1)HDS-LEE-20190612 / G:(DE-Juel-1)iBehave-20220812
                      / G:(BMBF)16ME0400 / G:(EU-Grant)956669 /
                      G:(GEPRIS)368482240},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1044915},
}