% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Diaz:905031,
      author       = {Diaz, Sandra},
      title        = {{S}imulating and analyzing the structural plasticity of the
                      brain using {HPC}},
      reportid     = {FZJ-2022-00341},
      year         = {2021},
      abstract     = {In many fields of science, models are based on sets of
                      differential equations which need to be fit against
                      experimental data. In order to do this, parameter spaces are
                      searched to find specific values which make these models
                      useful to answer relevant scientific questions. In
                      computational neuroscience, models of spiking networks of
                      neurons play an important role in understanding how the
                      brain encodes information and achieves high level cognitive
                      functions. These models are not only of interest for
                      neuroscience but also to many other related fields including
                      artificial intelligence, robotics and control. However,
                      these models are very underconstrained, degenerate and show
                      chaotic dynamics which makes it challenging to find suitable
                      and robust solutions.In this presentation I propose
                      structural plasticity as an optimization algorithm inspired
                      by neurobiology able to generate, modify and tune
                      connectivity parameters for neural network models.
                      Structural plasticity refers to the ability of neurons to
                      change their structure by creating and deleting connections
                      with other neurons in a network in order to preserve
                      specific metabolic levels. First, I introduce the
                      characteristics of structural plasticity as an optimization
                      algorithm together with details about its implementation in
                      NEST, a well-known neural network simulator within the
                      computational neuroscience community. This implementation
                      can efficiently leverage computational resources and is
                      applicable to large scale neural networks. I also briefly
                      present a tool which I have co-developed in order to
                      visualize, analyze and interact with simulations using
                      structural plasticity.The rules under which structural
                      plasticity operates in the brain have been tuned through
                      centuries of natural evolutionary optimization. In the
                      second part of my talk I present how meta-optimization can
                      be used to artificially explore the general rules which make
                      structural plasticity able to work with a variety of network
                      configurations and reach different functional regimes at
                      each portion of the network.},
      organization  = {JSC-Jahresabschlusskolloquium 2021,
                       (Germany)},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / HBP SGA3 - Human Brain Project Specific
                      Grant Agreement 3 (945539) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/905031},
}