--- _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' ...