% 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”.

@ARTICLE{Milano:890077,
      author       = {Milano, Gianluca and Pedretti, Giacomo and Fretto, Matteo
                      and Boarino, Luca and Benfenati, Fabio and Ielmini, Daniele
                      and Valov, Ilia and Ricciardi, Carlo},
      title        = {{B}rain‐{I}nspired {S}tructural {P}lasticity through
                      {R}eweighting and {R}ewiring in {M}ulti‐{T}erminal
                      {S}elf‐{O}rganizing {M}emristive {N}anowire {N}etworks},
      journal      = {Advanced intelligent systems},
      volume       = {2},
      number       = {8},
      issn         = {2640-4567},
      address      = {Weinheim},
      publisher    = {Wiley-VCH Verlag GmbH $\&$ Co. KGaA},
      reportid     = {FZJ-2021-00667},
      pages        = {2000096 -},
      year         = {2020},
      abstract     = {Acting as artificial synapses, two‐terminal memristive
                      devices are considered fundamental building blocks for the
                      realization of artificial neural networks. Current
                      memristive crossbar architectures demonstrate the
                      implementation of neuromorphic computing paradigms, although
                      they are unable to emulate typical features of biological
                      neural networks such as high connectivity, adaptability
                      through reconnection and rewiring, and long‐range
                      spatio‐temporal correlation. Herein, self‐organizing
                      memristive random nanowire (NW) networks with functional
                      connectivity able to display homo‐ and heterosynaptic
                      plasticity is reported thanks to the mutual electrochemical
                      interaction among memristive NWs and NW junctions. In
                      particular, it is shown that rewiring and reweighting
                      effects observed in single NWs and single NW junctions,
                      respectively, are responsible for structural plasticity of
                      the network under electrical stimulation. Such biologically
                      inspired systems allow a low‐cost realization of neural
                      networks that can learn and adapt when subjected to multiple
                      external stimuli, emulating the experience‐dependent
                      synaptic plasticity that shape the connectivity and
                      functionalities of the nervous system that can be exploited
                      for hardware implementation of unconventional computing
                      paradigms.},
      cin          = {PGI-7},
      ddc          = {620},
      cid          = {I:(DE-Juel1)PGI-7-20110106},
      pnm          = {521 - Controlling Electron Charge-Based Phenomena
                      (POF3-521)},
      pid          = {G:(DE-HGF)POF3-521},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000669782900014},
      doi          = {10.1002/aisy.202000096},
      url          = {https://juser.fz-juelich.de/record/890077},
}