---
res:
  bibo_abstract:
  - The brain efficiently performs nonlinear computations through its intricate networks
    of spiking neurons, but how this is done remains elusive. While nonlinear computations
    can be implemented successfully in spiking neural networks, this requires supervised
    training and the resulting connectivity can be hard to interpret. In contrast,
    the required connectivity for any computation in the form of a linear dynamical
    system can be directly derived and understood with the spike coding network (SCN)
    framework. These networks also have biologically realistic activity patterns and
    are highly robust to cell death. Here we extend the SCN framework to directly
    implement any polynomial dynamical system, without the need for training. This
    results in networks requiring a mix of synapse types (fast, slow, and multiplicative),
    which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate
    how to directly derive the required connectivity for several nonlinear dynamical
    systems. We also show how to carry out higher-order polynomials with coupled networks
    that use only pair-wise multiplicative synapses, and provide expected numbers
    of connections for each synapse type. Overall, our work demonstrates a novel method
    for implementing nonlinear computations in spiking neural networks, while keeping
    the attractive features of standard SCNs (robustness, realistic activity patterns,
    and interpretable connectivity). Finally, we discuss the biological plausibility
    of our approach, and how the high accuracy and robustness of the approach may
    be of interest for neuromorphic computing.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Michele
      foaf_name: Nardin, Michele
      foaf_surname: Nardin
      foaf_workInfoHomepage: http://www.librecat.org/personId=30BD0376-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8849-6570
  - foaf_Person:
      foaf_givenName: James W.
      foaf_name: Phillips, James W.
      foaf_surname: Phillips
  - foaf_Person:
      foaf_givenName: William F.
      foaf_name: Podlaski, William F.
      foaf_surname: Podlaski
  - foaf_Person:
      foaf_givenName: Sander W.
      foaf_name: Keemink, Sander W.
      foaf_surname: Keemink
  bibo_doi: 10.24072/pcjournal.69
  bibo_volume: 1
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2804-3871
  dct_language: eng
  dct_publisher: Peer Community In@
  dct_title: Nonlinear computations in spiking neural networks through multiplicative
    synapses@
...