---
res:
  bibo_abstract:
  - We propose a neural information processing system obtained by re-purposing the
    function of a biological neural circuit model to govern simulated and real-world
    control tasks. Inspired by the structure of the nervous system of the soil-worm,
    C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model
    of biological neural circuits reparameterized for the control of alternative tasks.
    We first demonstrate that ONCs realize networks with higher maximum flow compared
    to arbitrary wired networks. We then learn instances of ONCs to control a series
    of robotic tasks, including the autonomous parking of a real-world rover robot.
    For reconfiguration of the purpose of the neural circuit, we adopt a search-based
    optimization algorithm. Ordinary neural circuits perform on par and, in some cases,
    significantly surpass the performance of contemporary deep learning models. ONC
    networks are compact, 77% sparser than their counterpart neural controllers, and
    their neural dynamics are fully interpretable at the cell-level.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Ramin
      foaf_name: Hasani, Ramin
      foaf_surname: Hasani
  - foaf_Person:
      foaf_givenName: Mathias
      foaf_name: Lechner, Mathias
      foaf_surname: Lechner
      foaf_workInfoHomepage: http://www.librecat.org/personId=3DC22916-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Amini, Alexander
      foaf_surname: Amini
  - foaf_Person:
      foaf_givenName: Daniela
      foaf_name: Rus, Daniela
      foaf_surname: Rus
  - foaf_Person:
      foaf_givenName: Radu
      foaf_name: Grosu, Radu
      foaf_surname: Grosu
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2640-3498
  dct_language: eng
  dct_title: 'A natural lottery ticket winner: Reinforcement learning with ordinary
    neural circuits@'
...