---
_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
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: '14096'
abstract:
- lang: eng
text: A binary neutron star merger has been observed in a multi-messenger detection
of gravitational wave (GW) and electromagnetic (EM) radiation. Binary neutron
stars that merge within a Hubble time, as well as many other compact binaries,
are expected to form via common envelope evolution. Yet five decades of research
on common envelope evolution have not yet resulted in a satisfactory understanding
of the multi-spatial multi-timescale evolution for the systems that lead to compact
binaries. In this paper, we report on the first successful simulations of common
envelope ejection leading to binary neutron star formation in 3D hydrodynamics.
We simulate the dynamical inspiral phase of the interaction between a 12M⊙ red
supergiant and a 1.4M⊙ neutron star for different initial separations and initial
conditions. For all of our simulations, we find complete envelope ejection and
final orbital separations of af≈1.3-5.1R⊙ depending on the simulation and criterion,
leading to binary neutron stars that can merge within a Hubble time. We find αCE-equivalent
efficiencies of ≈0.1-2.7 depending on the simulation and criterion, but this may
be specific for these extended progenitors. We fully resolve the core of the star
to ≲0.005R⊙ and our 3D hydrodynamics simulations are informed by an adjusted 1D
analytic energy formalism and a 2D kinematics study in order to overcome the prohibitive
computational cost of simulating these systems. The framework we develop in this
paper can be used to simulate a wide variety of interactions between stars, from
stellar mergers to common envelope episodes leading to GW sources.
article_number: '2011.06630'
article_processing_charge: No
author:
- first_name: Jamie A. P. Law-Smith
full_name: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith
last_name: Jamie A. P. Law-Smith
- first_name: Rosa Wallace
full_name: Everson, Rosa Wallace
last_name: Everson
- first_name: Enrico Ramirez-Ruiz
full_name: Enrico Ramirez-Ruiz, Enrico Ramirez-Ruiz
last_name: Enrico Ramirez-Ruiz
- first_name: Selma E. de
full_name: Mink, Selma E. de
last_name: Mink
- first_name: Lieke A. C. van
full_name: Son, Lieke A. C. van
last_name: Son
- first_name: Ylva Louise Linsdotter
full_name: Götberg, Ylva Louise Linsdotter
id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d
last_name: Götberg
orcid: 0000-0002-6960-6911
- first_name: Stefan
full_name: Zellmann, Stefan
last_name: Zellmann
- first_name: Alejandro Vigna-Gómez
full_name: Alejandro Vigna-Gómez, Alejandro Vigna-Gómez
last_name: Alejandro Vigna-Gómez
- first_name: Mathieu
full_name: Renzo, Mathieu
last_name: Renzo
- first_name: Samantha
full_name: Wu, Samantha
last_name: Wu
- first_name: Sophie L.
full_name: Schrøder, Sophie L.
last_name: Schrøder
- first_name: Ryan J.
full_name: Foley, Ryan J.
last_name: Foley
- first_name: Tenley Hutchinson-Smith
full_name: Tenley Hutchinson-Smith, Tenley Hutchinson-Smith
last_name: Tenley Hutchinson-Smith
citation:
ama: Jamie A. P. Law-Smith JAPL-S, Everson RW, Enrico Ramirez-Ruiz ER-R, et al.
Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics.
arXiv. doi:10.48550/arXiv.2011.06630
apa: Jamie A. P. Law-Smith, J. A. P. L.-S., Everson, R. W., Enrico Ramirez-Ruiz,
E. R.-R., Mink, S. E. de, Son, L. A. C. van, Götberg, Y. L. L., … Tenley Hutchinson-Smith,
T. H.-S. (n.d.). Successful common envelope ejection and binary neutron star formation
in 3D hydrodynamics. arXiv. https://doi.org/10.48550/arXiv.2011.06630
chicago: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith, Rosa Wallace Everson, Enrico
Ramirez-Ruiz Enrico Ramirez-Ruiz, Selma E. de Mink, Lieke A. C. van Son, Ylva
Louise Linsdotter Götberg, Stefan Zellmann, et al. “Successful Common Envelope
Ejection and Binary Neutron Star Formation in 3D Hydrodynamics.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2011.06630.
ieee: J. A. P. L.-S. Jamie A. P. Law-Smith et al., “Successful common envelope
ejection and binary neutron star formation in 3D hydrodynamics,” arXiv.
.
ista: Jamie A. P. Law-Smith JAPL-S, Everson RW, Enrico Ramirez-Ruiz ER-R, Mink SE
de, Son LAC van, Götberg YLL, Zellmann S, Alejandro Vigna-Gómez AV-G, Renzo M,
Wu S, Schrøder SL, Foley RJ, Tenley Hutchinson-Smith TH-S. Successful common envelope
ejection and binary neutron star formation in 3D hydrodynamics. arXiv, 2011.06630.
mla: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith, et al. “Successful Common Envelope
Ejection and Binary Neutron Star Formation in 3D Hydrodynamics.” ArXiv,
2011.06630, doi:10.48550/arXiv.2011.06630.
short: J.A.P.L.-S. Jamie A. P. Law-Smith, R.W. Everson, E.R.-R. Enrico Ramirez-Ruiz,
S.E. de Mink, L.A.C. van Son, Y.L.L. Götberg, S. Zellmann, A.V.-G. Alejandro Vigna-Gómez,
M. Renzo, S. Wu, S.L. Schrøder, R.J. Foley, T.H.-S. Tenley Hutchinson-Smith, ArXiv
(n.d.).
date_created: 2023-08-21T10:10:41Z
date_published: 2020-11-12T00:00:00Z
date_updated: 2023-08-22T11:03:00Z
day: '12'
doi: 10.48550/arXiv.2011.06630
external_id:
arxiv:
- '2011.06630'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2011.06630
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Successful common envelope ejection and binary neutron star formation in 3D
hydrodynamics
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8699'
abstract:
- lang: eng
text: In the high spin–orbit-coupled Sr2IrO4, the high sensitivity of the ground
state to the details of the local lattice structure shows a large potential for
the manipulation of the functional properties by inducing local lattice distortions.
We use epitaxial strain to modify the Ir–O bond geometry in Sr2IrO4 and perform
momentum-dependent resonant inelastic X-ray scattering (RIXS) at the metal and
at the ligand sites to unveil the response of the low-energy elementary excitations.
We observe that the pseudospin-wave dispersion for tensile-strained Sr2IrO4 films
displays large softening along the [h,0] direction, while along the [h,h] direction
it shows hardening. This evolution reveals a renormalization of the magnetic interactions
caused by a strain-driven cross-over from anisotropic to isotropic interactions
between the magnetic moments. Moreover, we detect dispersive electron–hole pair
excitations which shift to lower (higher) energies upon compressive (tensile)
strain, manifesting a reduction (increase) in the size of the charge gap. This
behavior shows an intimate coupling between charge excitations and lattice distortions
in Sr2IrO4, originating from the modified hopping elements between the t2g orbitals.
Our work highlights the central role played by the lattice degrees of freedom
in determining both the pseudospin and charge excitations of Sr2IrO4 and provides
valuable information toward the control of the ground state of complex oxides
in the presence of high spin–orbit coupling.
acknowledgement: 'We gratefully acknowledge C. Sahle for experimental support at the
ID20 beamline of the ESRF. The soft X-ray experiments were carried out at the ADRESS
beamline of the Swiss Light Source, Paul Scherrer Institut (PSI). E. Paris and T.S.
thank X. Lu and C. Monney for valuable discussions. The work at PSI is supported
by the Swiss National Science Foundation (SNSF) through Project 200021_178867, the
NCCR (National Centre of Competence in Research) MARVEL (Materials’ Revolution:
Computational Design and Discovery of Novel Materials) and the Sinergia network
Mott Physics Beyond the Heisenberg Model (MPBH) (SNSF Research Grants CRSII2_160765/1
and CRSII2_141962). K.W. acknowledges support by the Narodowe Centrum Nauki Projects
2016/22/E/ST3/00560 and 2016/23/B/ST3/00839. E.M.P. and M.N. acknowledge funding
from the European Union’s Horizon 2020 research and innovation programme under the
Marie Sklodowska-Curie Grant Agreements 754411 and 701647, respectively. M.R. was
supported by the Swiss National Science Foundation under Project 200021 – 182695.
This research used resources of the APS, a U.S. Department of Energy (DOE) Office
of Science User Facility operated for the DOE Office of Science by Argonne National
Laboratory under Contract DE-AC02-06CH11357.'
article_processing_charge: No
article_type: original
author:
- first_name: Eugenio
full_name: Paris, Eugenio
last_name: Paris
- first_name: Yi
full_name: Tseng, Yi
last_name: Tseng
- first_name: Ekaterina
full_name: Paerschke, Ekaterina
id: 8275014E-6063-11E9-9B7F-6338E6697425
last_name: Paerschke
orcid: 0000-0003-0853-8182
- first_name: Wenliang
full_name: Zhang, Wenliang
last_name: Zhang
- first_name: Mary H
full_name: Upton, Mary H
last_name: Upton
- first_name: Anna
full_name: Efimenko, Anna
last_name: Efimenko
- first_name: Katharina
full_name: Rolfs, Katharina
last_name: Rolfs
- first_name: Daniel E
full_name: McNally, Daniel E
last_name: McNally
- first_name: Laura
full_name: Maurel, Laura
last_name: Maurel
- first_name: Muntaser
full_name: Naamneh, Muntaser
last_name: Naamneh
- first_name: Marco
full_name: Caputo, Marco
last_name: Caputo
- first_name: Vladimir N
full_name: Strocov, Vladimir N
last_name: Strocov
- first_name: Zhiming
full_name: Wang, Zhiming
last_name: Wang
- first_name: Diego
full_name: Casa, Diego
last_name: Casa
- first_name: Christof W
full_name: Schneider, Christof W
last_name: Schneider
- first_name: Ekaterina
full_name: Pomjakushina, Ekaterina
last_name: Pomjakushina
- first_name: Krzysztof
full_name: Wohlfeld, Krzysztof
last_name: Wohlfeld
- first_name: Milan
full_name: Radovic, Milan
last_name: Radovic
- first_name: Thorsten
full_name: Schmitt, Thorsten
last_name: Schmitt
citation:
ama: Paris E, Tseng Y, Paerschke E, et al. Strain engineering of the charge and
spin-orbital interactions in Sr2IrO4. Proceedings of the National Academy of
Sciences of the United States of America. 2020;117(40):24764-24770. doi:10.1073/pnas.2012043117
apa: Paris, E., Tseng, Y., Paerschke, E., Zhang, W., Upton, M. H., Efimenko, A.,
… Schmitt, T. (2020). Strain engineering of the charge and spin-orbital interactions
in Sr2IrO4. Proceedings of the National Academy of Sciences of the United States
of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2012043117
chicago: Paris, Eugenio, Yi Tseng, Ekaterina Paerschke, Wenliang Zhang, Mary H Upton,
Anna Efimenko, Katharina Rolfs, et al. “Strain Engineering of the Charge and Spin-Orbital
Interactions in Sr2IrO4.” Proceedings of the National Academy of Sciences of
the United States of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.2012043117.
ieee: E. Paris et al., “Strain engineering of the charge and spin-orbital
interactions in Sr2IrO4,” Proceedings of the National Academy of Sciences of
the United States of America, vol. 117, no. 40. National Academy of Sciences,
pp. 24764–24770, 2020.
ista: Paris E, Tseng Y, Paerschke E, Zhang W, Upton MH, Efimenko A, Rolfs K, McNally
DE, Maurel L, Naamneh M, Caputo M, Strocov VN, Wang Z, Casa D, Schneider CW, Pomjakushina
E, Wohlfeld K, Radovic M, Schmitt T. 2020. Strain engineering of the charge and
spin-orbital interactions in Sr2IrO4. Proceedings of the National Academy of Sciences
of the United States of America. 117(40), 24764–24770.
mla: Paris, Eugenio, et al. “Strain Engineering of the Charge and Spin-Orbital Interactions
in Sr2IrO4.” Proceedings of the National Academy of Sciences of the United
States of America, vol. 117, no. 40, National Academy of Sciences, 2020, pp.
24764–70, doi:10.1073/pnas.2012043117.
short: E. Paris, Y. Tseng, E. Paerschke, W. Zhang, M.H. Upton, A. Efimenko, K. Rolfs,
D.E. McNally, L. Maurel, M. Naamneh, M. Caputo, V.N. Strocov, Z. Wang, D. Casa,
C.W. Schneider, E. Pomjakushina, K. Wohlfeld, M. Radovic, T. Schmitt, Proceedings
of the National Academy of Sciences of the United States of America 117 (2020)
24764–24770.
date_created: 2020-10-25T23:01:17Z
date_published: 2020-10-06T00:00:00Z
date_updated: 2023-08-22T12:11:52Z
day: '06'
ddc:
- '530'
department:
- _id: MiLe
doi: 10.1073/pnas.2012043117
ec_funded: 1
external_id:
arxiv:
- '2009.12262'
isi:
- '000579059100029'
pmid:
- '32958669'
file:
- access_level: open_access
checksum: 1638fa36b442e2868576c6dd7d6dc505
content_type: application/pdf
creator: cziletti
date_created: 2020-10-28T11:53:12Z
date_updated: 2020-10-28T11:53:12Z
file_id: '8715'
file_name: 2020_PNAS_Paris.pdf
file_size: 1176522
relation: main_file
success: 1
file_date_updated: 2020-10-28T11:53:12Z
has_accepted_license: '1'
intvolume: ' 117'
isi: 1
issue: '40'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 24764-24770
pmid: 1
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
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: Strain engineering of the charge and spin-orbital interactions in Sr2IrO4
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: '8737'
abstract:
- lang: eng
text: Mitochondrial complex I couples NADH:ubiquinone oxidoreduction to proton pumping
by an unknown mechanism. Here, we present cryo-electron microscopy structures
of ovine complex I in five different conditions, including turnover, at resolutions
up to 2.3 to 2.5 angstroms. Resolved water molecules allowed us to experimentally
define the proton translocation pathways. Quinone binds at three positions along
the quinone cavity, as does the inhibitor rotenone that also binds within subunit
ND4. Dramatic conformational changes around the quinone cavity couple the redox
reaction to proton translocation during open-to-closed state transitions of the
enzyme. In the induced deactive state, the open conformation is arrested by the
ND6 subunit. We propose a detailed molecular coupling mechanism of complex I,
which is an unexpected combination of conformational changes and electrostatic
interactions.
acknowledged_ssus:
- _id: LifeSc
- _id: EM-Fac
acknowledgement: We thank J. Novacek (CEITEC Brno) and V.-V. Hodirnau (IST Austria)
for their help with collecting cryo-EM datasets. We thank the IST Life Science and
Electron Microscopy Facilities for providing equipment. This work has been supported
by iNEXT,project number 653706, funded by the Horizon 2020 program of the European
Union. This article reflects only the authors’view,and the European Commission is
not responsible for any use that may be made of the information it contains. CIISB
research infrastructure project LM2015043 funded by MEYS CR is gratefully acknowledged
for the financial support of the measurements at the CF Cryo-electron Microscopy
and Tomography CEITEC MU.This project has received funding from the European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant
Agreement no. 665385
article_number: eabc4209
article_processing_charge: No
article_type: original
author:
- first_name: Domen
full_name: Kampjut, Domen
id: 37233050-F248-11E8-B48F-1D18A9856A87
last_name: Kampjut
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
citation:
ama: Kampjut D, Sazanov LA. The coupling mechanism of mammalian respiratory complex
I. Science. 2020;370(6516). doi:10.1126/science.abc4209
apa: Kampjut, D., & Sazanov, L. A. (2020). The coupling mechanism of mammalian
respiratory complex I. Science. American Association for the Advancement
of Science. https://doi.org/10.1126/science.abc4209
chicago: Kampjut, Domen, and Leonid A Sazanov. “The Coupling Mechanism of Mammalian
Respiratory Complex I.” Science. American Association for the Advancement
of Science, 2020. https://doi.org/10.1126/science.abc4209.
ieee: D. Kampjut and L. A. Sazanov, “The coupling mechanism of mammalian respiratory
complex I,” Science, vol. 370, no. 6516. American Association for the Advancement
of Science, 2020.
ista: Kampjut D, Sazanov LA. 2020. The coupling mechanism of mammalian respiratory
complex I. Science. 370(6516), eabc4209.
mla: Kampjut, Domen, and Leonid A. Sazanov. “The Coupling Mechanism of Mammalian
Respiratory Complex I.” Science, vol. 370, no. 6516, eabc4209, American
Association for the Advancement of Science, 2020, doi:10.1126/science.abc4209.
short: D. Kampjut, L.A. Sazanov, Science 370 (2020).
date_created: 2020-11-08T23:01:23Z
date_published: 2020-10-30T00:00:00Z
date_updated: 2023-08-22T12:35:38Z
day: '30'
ddc:
- '572'
department:
- _id: LeSa
doi: 10.1126/science.abc4209
ec_funded: 1
external_id:
isi:
- '000583031800004'
pmid:
- '32972993'
file:
- access_level: open_access
checksum: 658ba90979ca9528a2efdfac8547047a
content_type: application/pdf
creator: lsazanov
date_created: 2020-11-26T18:47:58Z
date_updated: 2020-11-26T18:47:58Z
file_id: '8820'
file_name: Full_manuscript_with_SI_opt_red.pdf
file_size: 7618987
relation: main_file
success: 1
file_date_updated: 2020-11-26T18:47:58Z
has_accepted_license: '1'
intvolume: ' 370'
isi: 1
issue: '6516'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
pmid: 1
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Science
publication_identifier:
eissn:
- '10959203'
publication_status: published
publisher: American Association for the Advancement of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: The coupling mechanism of mammalian respiratory complex I
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 370
year: '2020'
...