% 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{Jordan:255703,
      author       = {Jordan, Jakob and Pfeil, Thomas and Tetzlaff, Tom and
                      Grübl, Andreas and Schemmel, Johannes and Diesmann, Markus
                      and Meier, Karlheinz},
      title        = {{T}he effect of heterogeneity on decorrelation mechanisms
                      in spiking neural networks: a neuromorphic-hardware study},
      reportid     = {FZJ-2015-05833},
      year         = {2015},
      abstract     = {Correlations in neural activity can severely impair the
                      processing of information in neural networks.In finite-size
                      networks, correlations are however inevitable due to common
                      presynaptic sources.Recent theoretical studies have shown
                      that inhibitory feedback, abundant in biological neural
                      networks,can actively suppress these shared-input
                      correlations and thereby enable neurons to fire nearly
                      independently. [1,2]For networks of spiking neurons, the
                      decorrelating effect of inhibitory feedback has so far been
                      explicitly demonstrated only for homogeneous networks of
                      neurons with linear sub-threshold dynamics.Theory, however,
                      suggests that the effect is a general phenomenon, present in
                      any system with inhibitory feedback,irrespective of the
                      details of the network structure and the neuron and synapse
                      properties.Here, we investigate the effect of network
                      heterogeneity on correlations in sparse random networks of
                      inhibitory neurons with conductance-based
                      synapses.Accelerated neuromorphic hardware [3] is used as a
                      user-friendly stand-alone research tool to emulate these
                      networks.The configurability of the hardware substrate
                      enables us to modulate the extent of network heterogeneity
                      in a systematic manner.We selectively study the effects of
                      shared-input (light gray symbols in Fig.) and recurrent
                      connections (black and dark gray symbols in Fig.) on
                      correlations in synaptic inputs (Fig a) and spike trains
                      (Fig b).Our results confirm that shared-input correlations
                      are actively suppressed by inhibitory feedback also in
                      highly heterogeneous networks exhibiting broad, heavy-tailed
                      firing-rate distributions.However, while cell and synapse
                      heterogeneities lead to a reduction of shared-input
                      correlations (feedforward decorrelation),feedback
                      decorrelation is impaired as a consequence of diminished
                      effective feedback (see Fig.).Acknowledgments: Partially
                      supported by the Helmholtz Association portfolio theme SMHB,
                      the Jülich Aachen Research Alliance (JARA), EU Grant 269921
                      (BrainScaleS), and EU Grant 604102 (Human Brain Project,
                      HBP).[1] Renart et al. (2010), Science 327:587–590[2]
                      Tetzlaff et al. (2012), PLoS Comp Biol 8(8):e1002596[3]
                      Pfeil et al. (2013), Front Neurosci 7:11},
      month         = {Mar},
      date          = {2015-03-18},
      organization  = {11th Göttingen Meeting of the German
                       Neuroscience Society, Göttingen
                       (Germany), 18 Mar 2015 - 21 Mar 2015},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      BRAINSCALES - Brain-inspired multiscale computation in
                      neuromorphic hybrid systems (269921) / HBP - The Human Brain
                      Project (604102) / HASB - Helmholtz Alliance on Systems
                      Biology (HGF-SystemsBiology) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)269921 /
                      G:(EU-Grant)604102 / G:(DE-Juel1)HGF-SystemsBiology /
                      G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/255703},
}