---
_id: '10673'
abstract:
- lang: eng
  text: 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.
acknowledgement: "RH and RG are partially supported by Horizon-2020 ECSEL Project
  grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was
  supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein
  Award). AA is supported by the National Science Foundation (NSF) Graduate Research
  Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and
  JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation
  of RH.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Ramin
  full_name: Hasani, Ramin
  last_name: Hasani
- first_name: Mathias
  full_name: Lechner, Mathias
  id: 3DC22916-F248-11E8-B48F-1D18A9856A87
  last_name: Lechner
- first_name: Alexander
  full_name: Amini, Alexander
  last_name: Amini
- first_name: Daniela
  full_name: Rus, Daniela
  last_name: Rus
- first_name: Radu
  full_name: Grosu, Radu
  last_name: Grosu
citation:
  ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner:
    Reinforcement learning with ordinary neural circuits. In: <i>Proceedings of the
    37th International Conference on Machine Learning</i>. PMLR. ; 2020:4082-4093.'
  apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., &#38; Grosu, R. (2020). A natural
    lottery ticket winner: Reinforcement learning with ordinary neural circuits. In
    <i>Proceedings of the 37th International Conference on Machine Learning</i> (pp.
    4082–4093). Virtual.'
  chicago: 'Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
    Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary
    Neural Circuits.” In <i>Proceedings of the 37th International Conference on Machine
    Learning</i>, 4082–93. PMLR, 2020.'
  ieee: 'R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery
    ticket winner: Reinforcement learning with ordinary neural circuits,” in <i>Proceedings
    of the 37th International Conference on Machine Learning</i>, Virtual, 2020, pp.
    4082–4093.'
  ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket
    winner: Reinforcement learning with ordinary neural circuits. Proceedings of the
    37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR,
    , 4082–4093.'
  mla: 'Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning
    with Ordinary Neural Circuits.” <i>Proceedings of the 37th International Conference
    on Machine Learning</i>, 2020, pp. 4082–93.'
  short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
    37th International Conference on Machine Learning, 2020, pp. 4082–4093.
conference:
  end_date: 2020-07-18
  location: Virtual
  name: 'ML: Machine Learning'
  start_date: 2020-07-12
date_created: 2022-01-25T15:50:34Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
  checksum: c9a4a29161777fc1a89ef451c040e3b1
  content_type: application/pdf
  creator: cchlebak
  date_created: 2022-01-26T11:08:51Z
  date_updated: 2022-01-26T11:08:51Z
  file_id: '10691'
  file_name: 2020_PMLR_Hasani.pdf
  file_size: 2329798
  relation: main_file
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file_date_updated: 2022-01-26T11:08:51Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/3.0/
main_file_link:
- open_access: '1'
  url: http://proceedings.mlr.press/v119/hasani20a.html
oa: 1
oa_version: Published Version
page: 4082-4093
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
  issn:
  - 2640-3498
publication_status: published
quality_controlled: '1'
scopus_import: '1'
series_title: PMLR
status: public
title: 'A natural lottery ticket winner: Reinforcement learning with ordinary neural
  circuits'
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND
    3.0)
  short: CC BY-NC-ND (3.0)
type: conference
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...