--- _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' ... --- _id: '8670' abstract: - lang: eng text: The α–z Rényi relative entropies are a two-parameter family of Rényi relative entropies that are quantum generalizations of the classical α-Rényi relative entropies. In the work [Adv. Math. 365, 107053 (2020)], we decided the full range of (α, z) for which the data processing inequality (DPI) is valid. In this paper, we give algebraic conditions for the equality in DPI. For the full range of parameters (α, z), we give necessary conditions and sufficient conditions. For most parameters, we give equivalent conditions. This generalizes and strengthens the results of Leditzky et al. [Lett. Math. Phys. 107, 61–80 (2017)]. acknowledgement: This research was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 754411. The author would like to thank Anna Vershynina and Sarah Chehade for their helpful comments. article_number: '102201' article_processing_charge: No article_type: original author: - first_name: Haonan full_name: Zhang, Haonan id: D8F41E38-9E66-11E9-A9E2-65C2E5697425 last_name: Zhang citation: ama: Zhang H. Equality conditions of data processing inequality for α-z Rényi relative entropies. Journal of Mathematical Physics. 2020;61(10). doi:10.1063/5.0022787 apa: Zhang, H. (2020). Equality conditions of data processing inequality for α-z Rényi relative entropies. Journal of Mathematical Physics. AIP Publishing. https://doi.org/10.1063/5.0022787 chicago: Zhang, Haonan. “Equality Conditions of Data Processing Inequality for α-z Rényi Relative Entropies.” Journal of Mathematical Physics. AIP Publishing, 2020. https://doi.org/10.1063/5.0022787. ieee: H. Zhang, “Equality conditions of data processing inequality for α-z Rényi relative entropies,” Journal of Mathematical Physics, vol. 61, no. 10. AIP Publishing, 2020. ista: Zhang H. 2020. Equality conditions of data processing inequality for α-z Rényi relative entropies. Journal of Mathematical Physics. 61(10), 102201. mla: Zhang, Haonan. “Equality Conditions of Data Processing Inequality for α-z Rényi Relative Entropies.” Journal of Mathematical Physics, vol. 61, no. 10, 102201, AIP Publishing, 2020, doi:10.1063/5.0022787. short: H. Zhang, Journal of Mathematical Physics 61 (2020). date_created: 2020-10-18T22:01:36Z date_published: 2020-10-01T00:00:00Z date_updated: 2023-08-22T10:32:29Z day: '01' department: - _id: JaMa doi: 10.1063/5.0022787 ec_funded: 1 external_id: arxiv: - '2007.06644' isi: - '000578529200001' intvolume: ' 61' isi: 1 issue: '10' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2007.06644 month: '10' oa: 1 oa_version: Preprint project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Journal of Mathematical Physics publication_identifier: issn: - '00222488' publication_status: published publisher: AIP Publishing quality_controlled: '1' scopus_import: '1' status: public title: Equality conditions of data processing inequality for α-z Rényi relative entropies type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 61 year: '2020' ... --- _id: '8698' abstract: - lang: eng text: The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation. acknowledgement: We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.). article_processing_charge: No article_type: original author: - first_name: Ori full_name: Maoz, Ori last_name: Maoz - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Mohamad Saleh full_name: Esteki, Mohamad Saleh last_name: Esteki - first_name: Roozbeh full_name: Kiani, Roozbeh last_name: Kiani - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(40):25066-25073. doi:10.1073/pnas.1912804117 apa: Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.1912804117 chicago: Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.1912804117. ieee: O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020. ista: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073. mla: Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:10.1073/pnas.1912804117. short: O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073. date_created: 2020-10-25T23:01:16Z date_published: 2020-10-06T00:00:00Z date_updated: 2023-08-22T12:11:23Z day: '06' ddc: - '570' department: - _id: GaTk doi: 10.1073/pnas.1912804117 external_id: isi: - '000579045200012' pmid: - '32948691' file: - access_level: open_access checksum: c6a24fdecf3f28faf447078e7a274a88 content_type: application/pdf creator: cziletti date_created: 2020-10-27T14:57:50Z date_updated: 2020-10-27T14:57:50Z file_id: '8713' file_name: 2020_PNAS_Maoz.pdf file_size: 1755359 relation: main_file success: 1 file_date_updated: 2020-10-27T14:57:50Z has_accepted_license: '1' intvolume: ' 117' isi: 1 issue: '40' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '10' oa: 1 oa_version: Published Version page: 25066-25073 pmid: 1 publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - '10916490' issn: - '00278424' publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Learning probabilistic neural representations with randomly connected circuits tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 117 year: '2020' ... --- _id: '8704' abstract: - lang: eng text: Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about the application area. We design a learning scheme to train end-to-end linear dynamical systems (LDS)s by gradient descent in imitation learning robotic domains. We introduce a new regularization loss component together with a learning algorithm that improves the stability of the learned autonomous system, by forcing the eigenvalues of the internal state updates of an LDS to be negative reals. We evaluate our approach on a series of real-life and simulated robotic experiments, in comparison to linear and nonlinear Recurrent Neural Network (RNN) architectures. Our results show that our stabilizing method significantly improves test performance of LDS, enabling such linear models to match the performance of contemporary nonlinear RNN architectures. A video of the obstacle avoidance performance of our method on a mobile robot, in unseen environments, compared to other methods can be viewed at https://youtu.be/mhEsCoNao5E. acknowledgement: M.L. is supported in parts by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H., and R.G. are partially supported by the Horizon-2020 ECSELProject grant No. 783163 (iDev40), and the Austrian Research Promotion Agency (FFG), Project No. 860424. R.H. and D.R. is partially supported by the Boeing Company. alternative_title: - ICRA article_processing_charge: No 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: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Lechner M, Hasani R, Rus D, Grosu R. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE; 2020:5446-5452. doi:10.1109/ICRA40945.2020.9196608' apa: 'Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608' chicago: Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” In Proceedings - IEEE International Conference on Robotics and Automation, 5446–52. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608. ieee: M. Lechner, R. Hasani, D. Rus, and R. Grosu, “Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme,” in Proceedings - IEEE International Conference on Robotics and Automation, Paris, France, 2020, pp. 5446–5452. ista: 'Lechner M, Hasani R, Rus D, Grosu R. 2020. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings - IEEE International Conference on Robotics and Automation. ICRA: International Conference on Robotics and Automation, ICRA, , 5446–5452.' mla: Lechner, Mathias, et al. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–52, doi:10.1109/ICRA40945.2020.9196608. short: M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452. conference: end_date: 2020-08-31 location: Paris, France name: 'ICRA: International Conference on Robotics and Automation' start_date: 2020-05-31 date_created: 2020-10-25T23:01:19Z date_published: 2020-05-01T00:00:00Z date_updated: 2023-08-22T10:40:15Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1109/ICRA40945.2020.9196608 external_id: isi: - '000712319503110' file: - access_level: open_access checksum: fccf7b986ac78046918a298cc6849a50 content_type: application/pdf creator: dernst date_created: 2020-11-06T10:58:49Z date_updated: 2020-11-06T10:58:49Z file_id: '8733' file_name: 2020_ICRA_Lechner.pdf file_size: 1070010 relation: main_file success: 1 file_date_updated: 2020-11-06T10:58:49Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '05' oa: 1 oa_version: Submitted Version page: 5446-5452 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings - IEEE International Conference on Robotics and Automation publication_identifier: isbn: - '9781728173955' issn: - '10504729' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 year: '2020' ... --- _id: '8700' abstract: - lang: eng text: Translation termination is a finishing step of protein biosynthesis. The significant role in this process belongs not only to protein factors of translation termination but also to the nearest nucleotide environment of stop codons. There are numerous descriptions of stop codons readthrough, which is due to specific nucleotide sequences behind them. However, represented data are segmental and don’t explain the mechanism of the nucleotide context influence on translation termination. It is well known that stop codon UAA usage is preferential for A/T-rich genes, and UAG, UGA—for G/C-rich genes, which is related to an expression level of these genes. We investigated the connection between a frequency of nucleotides occurrence in 3' area of stop codons in the human genome and their influence on translation termination efficiency. We found that 3' context motif, which is cognate to the sequence of a stop codon, stimulates translation termination. At the same time, the nucleotide composition of 3' sequence that differs from stop codon, decreases translation termination efficiency. acknowledgement: We would like to thank the staff of CCU Genome for sequencing, Tat’yana Pestova, Christopher Helen, and Lyudmila Yur’evna Frolova for the plasmids provided, as well as the laboratory staff for productive discussion of the results. We also thank former laboratory employees Yuliya Vladimirovna Bocharova and Polina Nikolaevna Kryuchkova for the exceptional contribution to the present work. article_processing_charge: No article_type: original author: - first_name: E. E. full_name: Sokolova, E. E. last_name: Sokolova - first_name: Petr full_name: Vlasov, Petr id: 38BB9AC4-F248-11E8-B48F-1D18A9856A87 last_name: Vlasov - first_name: T. V. full_name: Egorova, T. V. last_name: Egorova - first_name: A. V. full_name: Shuvalov, A. V. last_name: Shuvalov - first_name: E. Z. full_name: Alkalaeva, E. Z. last_name: Alkalaeva citation: ama: Sokolova EE, Vlasov P, Egorova TV, Shuvalov AV, Alkalaeva EZ. The influence of A/G composition of 3’ stop codon contexts on translation termination efficiency in eukaryotes. Molecular Biology. 2020;54(5):739-748. doi:10.1134/S0026893320050088 apa: Sokolova, E. E., Vlasov, P., Egorova, T. V., Shuvalov, A. V., & Alkalaeva, E. Z. (2020). The influence of A/G composition of 3’ stop codon contexts on translation termination efficiency in eukaryotes. Molecular Biology. Springer Nature. https://doi.org/10.1134/S0026893320050088 chicago: Sokolova, E. E., Petr Vlasov, T. V. Egorova, A. V. Shuvalov, and E. Z. Alkalaeva. “The Influence of A/G Composition of 3’ Stop Codon Contexts on Translation Termination Efficiency in Eukaryotes.” Molecular Biology. Springer Nature, 2020. https://doi.org/10.1134/S0026893320050088. ieee: E. E. Sokolova, P. Vlasov, T. V. Egorova, A. V. Shuvalov, and E. Z. Alkalaeva, “The influence of A/G composition of 3’ stop codon contexts on translation termination efficiency in eukaryotes,” Molecular Biology, vol. 54, no. 5. Springer Nature, pp. 739–748, 2020. ista: Sokolova EE, Vlasov P, Egorova TV, Shuvalov AV, Alkalaeva EZ. 2020. The influence of A/G composition of 3’ stop codon contexts on translation termination efficiency in eukaryotes. Molecular Biology. 54(5), 739–748. mla: Sokolova, E. E., et al. “The Influence of A/G Composition of 3’ Stop Codon Contexts on Translation Termination Efficiency in Eukaryotes.” Molecular Biology, vol. 54, no. 5, Springer Nature, 2020, pp. 739–48, doi:10.1134/S0026893320050088. short: E.E. Sokolova, P. Vlasov, T.V. Egorova, A.V. Shuvalov, E.Z. Alkalaeva, Molecular Biology 54 (2020) 739–748. date_created: 2020-10-25T23:01:17Z date_published: 2020-09-01T00:00:00Z date_updated: 2023-08-22T10:39:38Z day: '01' department: - _id: FyKo doi: 10.1134/S0026893320050088 external_id: isi: - '000579441200009' intvolume: ' 54' isi: 1 issue: '5' language: - iso: eng month: '09' oa_version: None page: 739-748 publication: Molecular Biology publication_identifier: eissn: - '16083245' issn: - '00268933' publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '8701' relation: original status: public scopus_import: '1' status: public title: The influence of A/G composition of 3' stop codon contexts on translation termination efficiency in eukaryotes type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 54 year: '2020' ...