---
_id: '8679'
abstract:
- lang: eng
text: A central goal of artificial intelligence in high-stakes decision-making applications
is to design a single algorithm that simultaneously expresses generalizability
by learning coherent representations of their world and interpretable explanations
of its dynamics. Here, we combine brain-inspired neural computation principles
and scalable deep learning architectures to design compact neural controllers
for task-specific compartments of a full-stack autonomous vehicle control system.
We discover that a single algorithm with 19 control neurons, connecting 32 encapsulated
input features to outputs by 253 synapses, learns to map high-dimensional inputs
into steering commands. This system shows superior generalizability, interpretability
and robustness compared with orders-of-magnitude larger black-box learning systems.
The obtained neural agents enable high-fidelity autonomy for task-specific parts
of a complex autonomous system.
article_processing_charge: No
article_type: original
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: Lechner M, Hasani R, Amini A, Henzinger TA, Rus D, Grosu R. Neural circuit
policies enabling auditable autonomy. Nature Machine Intelligence. 2020;2:642-652.
doi:10.1038/s42256-020-00237-3
apa: Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D., & Grosu,
R. (2020). Neural circuit policies enabling auditable autonomy. Nature Machine
Intelligence. Springer Nature. https://doi.org/10.1038/s42256-020-00237-3
chicago: Lechner, Mathias, Ramin Hasani, Alexander Amini, Thomas A Henzinger, Daniela
Rus, and Radu Grosu. “Neural Circuit Policies Enabling Auditable Autonomy.” Nature
Machine Intelligence. Springer Nature, 2020. https://doi.org/10.1038/s42256-020-00237-3.
ieee: M. Lechner, R. Hasani, A. Amini, T. A. Henzinger, D. Rus, and R. Grosu, “Neural
circuit policies enabling auditable autonomy,” Nature Machine Intelligence,
vol. 2. Springer Nature, pp. 642–652, 2020.
ista: Lechner M, Hasani R, Amini A, Henzinger TA, Rus D, Grosu R. 2020. Neural circuit
policies enabling auditable autonomy. Nature Machine Intelligence. 2, 642–652.
mla: Lechner, Mathias, et al. “Neural Circuit Policies Enabling Auditable Autonomy.”
Nature Machine Intelligence, vol. 2, Springer Nature, 2020, pp. 642–52,
doi:10.1038/s42256-020-00237-3.
short: M. Lechner, R. Hasani, A. Amini, T.A. Henzinger, D. Rus, R. Grosu, Nature
Machine Intelligence 2 (2020) 642–652.
date_created: 2020-10-19T13:46:06Z
date_published: 2020-10-01T00:00:00Z
date_updated: 2023-08-22T10:36:06Z
day: '01'
department:
- _id: ToHe
doi: 10.1038/s42256-020-00237-3
external_id:
isi:
- '000583337200011'
intvolume: ' 2'
isi: 1
language:
- iso: eng
month: '10'
oa_version: None
page: 642-652
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
publication: Nature Machine Intelligence
publication_identifier:
eissn:
- 2522-5839
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/new-deep-learning-models/
scopus_import: '1'
status: public
title: Neural circuit policies enabling auditable autonomy
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 2
year: '2020'
...