---
_id: '7490'
abstract:
- lang: eng
text: In plants, clathrin mediated endocytosis (CME) represents the major route
for cargo internalisation from the cell surface. It has been assumed to operate
in an evolutionary conserved manner as in yeast and animals. Here we report characterisation
of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement
in electron microscopy and quantitative live imaging techniques. Arabidopsis CME
appears to follow the constant curvature model and the bona fide CME population
generates vesicles of a predominantly hexagonal-basket type; larger and with faster
kinetics than in other models. Contrary to the existing paradigm, actin is dispensable
for CME events at the plasma membrane but plays a unique role in collecting endocytic
vesicles, sorting of internalised cargos and directional endosome movement that
itself actively promote CME events. Internalized vesicles display a strongly delayed
and sequential uncoating. These unique features highlight the independent evolution
of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes.
acknowledged_ssus:
- _id: LifeSc
- _id: Bio
- _id: EM-Fac
article_number: e52067
article_processing_charge: No
article_type: original
author:
- first_name: Madhumitha
full_name: Narasimhan, Madhumitha
id: 44BF24D0-F248-11E8-B48F-1D18A9856A87
last_name: Narasimhan
orcid: 0000-0002-8600-0671
- first_name: Alexander J
full_name: Johnson, Alexander J
id: 46A62C3A-F248-11E8-B48F-1D18A9856A87
last_name: Johnson
orcid: 0000-0002-2739-8843
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Walter
full_name: Kaufmann, Walter
id: 3F99E422-F248-11E8-B48F-1D18A9856A87
last_name: Kaufmann
orcid: 0000-0001-9735-5315
- first_name: Shutang
full_name: Tan, Shutang
id: 2DE75584-F248-11E8-B48F-1D18A9856A87
last_name: Tan
orcid: 0000-0002-0471-8285
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- first_name: Jiří
full_name: Friml, Jiří
id: 4159519E-F248-11E8-B48F-1D18A9856A87
last_name: Friml
orcid: 0000-0002-8302-7596
citation:
ama: Narasimhan M, Johnson AJ, Prizak R, et al. Evolutionarily unique mechanistic
framework of clathrin-mediated endocytosis in plants. eLife. 2020;9. doi:10.7554/eLife.52067
apa: Narasimhan, M., Johnson, A. J., Prizak, R., Kaufmann, W., Tan, S., Casillas
Perez, B. E., & Friml, J. (2020). Evolutionarily unique mechanistic framework
of clathrin-mediated endocytosis in plants. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.52067
chicago: Narasimhan, Madhumitha, Alexander J Johnson, Roshan Prizak, Walter Kaufmann,
Shutang Tan, Barbara E Casillas Perez, and Jiří Friml. “Evolutionarily Unique
Mechanistic Framework of Clathrin-Mediated Endocytosis in Plants.” ELife.
eLife Sciences Publications, 2020. https://doi.org/10.7554/eLife.52067.
ieee: M. Narasimhan et al., “Evolutionarily unique mechanistic framework
of clathrin-mediated endocytosis in plants,” eLife, vol. 9. eLife Sciences
Publications, 2020.
ista: Narasimhan M, Johnson AJ, Prizak R, Kaufmann W, Tan S, Casillas Perez BE,
Friml J. 2020. Evolutionarily unique mechanistic framework of clathrin-mediated
endocytosis in plants. eLife. 9, e52067.
mla: Narasimhan, Madhumitha, et al. “Evolutionarily Unique Mechanistic Framework
of Clathrin-Mediated Endocytosis in Plants.” ELife, vol. 9, e52067, eLife
Sciences Publications, 2020, doi:10.7554/eLife.52067.
short: M. Narasimhan, A.J. Johnson, R. Prizak, W. Kaufmann, S. Tan, B.E. Casillas
Perez, J. Friml, ELife 9 (2020).
date_created: 2020-02-16T23:00:50Z
date_published: 2020-01-23T00:00:00Z
date_updated: 2023-08-18T06:33:07Z
day: '23'
ddc:
- '570'
- '580'
department:
- _id: JiFr
- _id: GaTk
- _id: EM-Fac
- _id: SyCr
doi: 10.7554/eLife.52067
ec_funded: 1
external_id:
isi:
- '000514104100001'
pmid:
- '31971511'
file:
- access_level: open_access
checksum: 2052daa4be5019534f3a42f200a09f32
content_type: application/pdf
creator: dernst
date_created: 2020-02-18T07:21:16Z
date_updated: 2020-07-14T12:47:59Z
file_id: '7494'
file_name: 2020_eLife_Narasimhan.pdf
file_size: 7247468
relation: main_file
file_date_updated: 2020-07-14T12:47:59Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 261099A6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742985'
name: Tracing Evolution of Auxin Transport and Polarity in Plants
- _id: 26538374-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I03630
name: Molecular mechanisms of endocytic cargo recognition in plants
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis
in plants
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 9
year: '2020'
...
---
_id: '9779'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:10.1371/journal.pcbi.1007642.s003
apa: Grah, R., & Friedlander, T. (2020). Distribution of crosstalk values. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s003
chicago: Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public
Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s003.
ieee: R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library
of Science, 2020.
ista: Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library
of Science, 10.1371/journal.pcbi.1007642.s003.
mla: Grah, Rok, and Tamar Friedlander. Distribution of Crosstalk Values.
Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s003.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:24:37Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s003
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: research_data
status: public
status: public
title: Distribution of crosstalk values
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9776'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Supporting information. 2020. doi:10.1371/journal.pcbi.1007642.s001
apa: Grah, R., & Friedlander, T. (2020). Supporting information. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s001
chicago: Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s001.
ieee: R. Grah and T. Friedlander, “Supporting information.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Supporting information, Public Library of Science,
10.1371/journal.pcbi.1007642.s001.
mla: Grah, Rok, and Tamar Friedlander. Supporting Information. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s001.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:15:04Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s001
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: used_in_publication
status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '7656'
abstract:
- lang: eng
text: 'We propose that correlations among neurons are generically strong enough
to organize neural activity patterns into a discrete set of clusters, which can
each be viewed as a population codeword. Our reasoning starts with the analysis
of retinal ganglion cell data using maximum entropy models, showing that the population
is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads
to an argument that neural populations in many other brain areas might share this
structure. Next, we use latent variable models to show that this glassy state
possesses well-defined clusters of neural activity. Clusters have three appealing
properties: (i) clusters exhibit error correction, i.e., they are reproducibly
elicited by the same stimulus despite variability at the level of constituent
neurons; (ii) clusters encode qualitatively different visual features than their
constituent neurons; and (iii) clusters can be learned by downstream neural circuits
in an unsupervised fashion. We hypothesize that these properties give rise to
a “learnable” neural code which the cortical hierarchy uses to extract increasingly
complex features without supervision or reinforcement.'
article_number: '20'
article_processing_charge: No
article_type: original
author:
- first_name: Michael J.
full_name: Berry, Michael J.
last_name: Berry
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: 'Berry MJ, Tkačik G. Clustering of neural activity: A design principle for
population codes. Frontiers in Computational Neuroscience. 2020;14. doi:10.3389/fncom.2020.00020'
apa: 'Berry, M. J., & Tkačik, G. (2020). Clustering of neural activity: A design
principle for population codes. Frontiers in Computational Neuroscience.
Frontiers. https://doi.org/10.3389/fncom.2020.00020'
chicago: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity:
A Design Principle for Population Codes.” Frontiers in Computational Neuroscience.
Frontiers, 2020. https://doi.org/10.3389/fncom.2020.00020.'
ieee: 'M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle
for population codes,” Frontiers in Computational Neuroscience, vol. 14.
Frontiers, 2020.'
ista: 'Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle
for population codes. Frontiers in Computational Neuroscience. 14, 20.'
mla: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design
Principle for Population Codes.” Frontiers in Computational Neuroscience,
vol. 14, 20, Frontiers, 2020, doi:10.3389/fncom.2020.00020.'
short: M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).
date_created: 2020-04-12T22:00:40Z
date_published: 2020-03-13T00:00:00Z
date_updated: 2023-08-18T10:30:11Z
day: '13'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.3389/fncom.2020.00020
external_id:
isi:
- '000525543200001'
pmid:
- '32231528'
file:
- access_level: open_access
checksum: 2b1da23823eae9cedbb42d701945b61e
content_type: application/pdf
creator: dernst
date_created: 2020-04-14T12:20:39Z
date_updated: 2020-07-14T12:48:01Z
file_id: '7659'
file_name: 2020_Frontiers_Berry.pdf
file_size: 4082937
relation: main_file
file_date_updated: 2020-07-14T12:48:01Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Frontiers in Computational Neuroscience
publication_identifier:
eissn:
- '16625188'
publication_status: published
publisher: Frontiers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Clustering of neural activity: A design principle for population codes'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
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'
...