---
_id: '11638'
abstract:
- lang: eng
text: 'Statistical inference is central to many scientific endeavors, yet how it
works remains unresolved. Answering this requires a quantitative understanding
of the intrinsic interplay between statistical models, inference methods, and
the structure in the data. To this end, we characterize the efficacy of direct
coupling analysis (DCA)—a highly successful method for analyzing amino acid sequence
data—in inferring pairwise interactions from samples of ferromagnetic Ising models
on random graphs. Our approach allows for physically motivated exploration of
qualitatively distinct data regimes separated by phase transitions. We show that
inference quality depends strongly on the nature of data-generating distributions:
optimal accuracy occurs at an intermediate temperature where the detrimental effects
from macroscopic order and thermal noise are minimal. Importantly our results
indicate that DCA does not always outperform its local-statistics-based predecessors;
while DCA excels at low temperatures, it becomes inferior to simple correlation
thresholding at virtually all temperatures when data are limited. Our findings
offer insights into the regime in which DCA operates so successfully, and more
broadly, how inference interacts with the structure in the data.'
acknowledgement: This work was supported in part by the Alfred P. Sloan Foundation,
the Simons Foundation, the National Institutes of Health under Award No. R01EB026943,
and the National Science Foundation, through the Center for the Physics of Biological
Function (PHY-1734030).
article_number: '023240'
article_processing_charge: No
article_type: original
author:
- first_name: Vudtiwat
full_name: Ngampruetikorn, Vudtiwat
last_name: Ngampruetikorn
- first_name: Vedant
full_name: Sachdeva, Vedant
last_name: Sachdeva
- first_name: Johanna
full_name: Torrence, Johanna
last_name: Torrence
- first_name: Jan
full_name: Humplik, Jan
id: 2E9627A8-F248-11E8-B48F-1D18A9856A87
last_name: Humplik
- first_name: David J.
full_name: Schwab, David J.
last_name: Schwab
- first_name: Stephanie E.
full_name: Palmer, Stephanie E.
last_name: Palmer
citation:
ama: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE.
Inferring couplings in networks across order-disorder phase transitions. Physical
Review Research. 2022;4(2). doi:10.1103/PhysRevResearch.4.023240
apa: Ngampruetikorn, V., Sachdeva, V., Torrence, J., Humplik, J., Schwab, D. J.,
& Palmer, S. E. (2022). Inferring couplings in networks across order-disorder
phase transitions. Physical Review Research. American Physical Society.
https://doi.org/10.1103/PhysRevResearch.4.023240
chicago: Ngampruetikorn, Vudtiwat, Vedant Sachdeva, Johanna Torrence, Jan Humplik,
David J. Schwab, and Stephanie E. Palmer. “Inferring Couplings in Networks across
Order-Disorder Phase Transitions.” Physical Review Research. American Physical
Society, 2022. https://doi.org/10.1103/PhysRevResearch.4.023240.
ieee: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D. J. Schwab, and
S. E. Palmer, “Inferring couplings in networks across order-disorder phase transitions,”
Physical Review Research, vol. 4, no. 2. American Physical Society, 2022.
ista: Ngampruetikorn V, Sachdeva V, Torrence J, Humplik J, Schwab DJ, Palmer SE.
2022. Inferring couplings in networks across order-disorder phase transitions.
Physical Review Research. 4(2), 023240.
mla: Ngampruetikorn, Vudtiwat, et al. “Inferring Couplings in Networks across Order-Disorder
Phase Transitions.” Physical Review Research, vol. 4, no. 2, 023240, American
Physical Society, 2022, doi:10.1103/PhysRevResearch.4.023240.
short: V. Ngampruetikorn, V. Sachdeva, J. Torrence, J. Humplik, D.J. Schwab, S.E.
Palmer, Physical Review Research 4 (2022).
date_created: 2022-07-24T22:01:42Z
date_published: 2022-06-24T00:00:00Z
date_updated: 2022-07-25T07:52:35Z
day: '24'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1103/PhysRevResearch.4.023240
external_id:
arxiv:
- '2106.02349'
file:
- access_level: open_access
checksum: ed6fdc2a3a096df785fa5f7b17b716c6
content_type: application/pdf
creator: dernst
date_created: 2022-07-25T07:47:23Z
date_updated: 2022-07-25T07:47:23Z
file_id: '11644'
file_name: 2022_PhysicalReviewResearch_Ngampruetikorn.pdf
file_size: 1379683
relation: main_file
success: 1
file_date_updated: 2022-07-25T07:47:23Z
funded_apc: '1'
has_accepted_license: '1'
intvolume: ' 4'
issue: '2'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
publication: Physical Review Research
publication_identifier:
issn:
- 2643-1564
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inferring couplings in networks across order-disorder phase transitions
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2022'
...
---
_id: '12156'
abstract:
- lang: eng
text: Models of transcriptional regulation that assume equilibrium binding of transcription
factors have been less successful at predicting gene expression from sequence
in eukaryotes than in bacteria. This could be due to the non-equilibrium nature
of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium
mechanisms is vast and predominantly uninteresting. The key question is therefore
how this space can be navigated efficiently, to focus on mechanisms and models
that are biologically relevant. In this review, we advocate for the normative
role of theory—theory that prescribes rather than just describes—in providing
such a focus. Theory should expand its remit beyond inferring mechanistic models
from data, towards identifying non-equilibrium gene regulatory schemes that may
have been evolutionarily selected, despite their energy consumption, because they
are precise, reliable, fast, or otherwise outperform regulation at equilibrium.
We illustrate our reasoning by toy examples for which we provide simulation code.
acknowledgement: 'This work was supported through the Center for the Physics of Biological
Function (PHYe1734030) and by National Institutes of Health Grants R01GM097275 and
U01DK127429 (TG). GT acknowledges the support of the Austrian Science Fund grant
FWF P28844 and the Human Frontiers Science Program. '
article_number: '100435'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Benjamin
full_name: Zoller, Benjamin
last_name: Zoller
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Zoller B, Gregor T, Tkačik G. Eukaryotic gene regulation at equilibrium, or
non? Current Opinion in Systems Biology. 2022;31(9). doi:10.1016/j.coisb.2022.100435
apa: Zoller, B., Gregor, T., & Tkačik, G. (2022). Eukaryotic gene regulation
at equilibrium, or non? Current Opinion in Systems Biology. Elsevier. https://doi.org/10.1016/j.coisb.2022.100435
chicago: Zoller, Benjamin, Thomas Gregor, and Gašper Tkačik. “Eukaryotic Gene Regulation
at Equilibrium, or Non?” Current Opinion in Systems Biology. Elsevier,
2022. https://doi.org/10.1016/j.coisb.2022.100435.
ieee: B. Zoller, T. Gregor, and G. Tkačik, “Eukaryotic gene regulation at equilibrium,
or non?,” Current Opinion in Systems Biology, vol. 31, no. 9. Elsevier,
2022.
ista: Zoller B, Gregor T, Tkačik G. 2022. Eukaryotic gene regulation at equilibrium,
or non? Current Opinion in Systems Biology. 31(9), 100435.
mla: Zoller, Benjamin, et al. “Eukaryotic Gene Regulation at Equilibrium, or Non?”
Current Opinion in Systems Biology, vol. 31, no. 9, 100435, Elsevier, 2022,
doi:10.1016/j.coisb.2022.100435.
short: B. Zoller, T. Gregor, G. Tkačik, Current Opinion in Systems Biology 31 (2022).
date_created: 2023-01-12T12:08:51Z
date_published: 2022-09-01T00:00:00Z
date_updated: 2023-02-13T09:20:34Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1016/j.coisb.2022.100435
file:
- access_level: open_access
checksum: 97ef01e0cc60cdc84f45640a0f248fb0
content_type: application/pdf
creator: dernst
date_created: 2023-01-24T12:14:10Z
date_updated: 2023-01-24T12:14:10Z
file_id: '12362'
file_name: 2022_CurrentBiology_Zoller.pdf
file_size: 2214944
relation: main_file
success: 1
file_date_updated: 2023-01-24T12:14:10Z
has_accepted_license: '1'
intvolume: ' 31'
issue: '9'
keyword:
- Applied Mathematics
- Computer Science Applications
- Drug Discovery
- General Biochemistry
- Genetics and Molecular Biology
- Modeling and Simulation
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Current Opinion in Systems Biology
publication_identifier:
issn:
- 2452-3100
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Eukaryotic gene regulation at equilibrium, or non?
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2022'
...
---
_id: '10530'
abstract:
- lang: eng
text: "Cell dispersion from a confined area is fundamental in a number of biological
processes,\r\nincluding cancer metastasis. To date, a quantitative understanding
of the interplay of single\r\ncell motility, cell proliferation, and intercellular
contacts remains elusive. In particular, the role\r\nof E- and N-Cadherin junctions,
central components of intercellular contacts, is still\r\ncontroversial. Combining
theoretical modeling with in vitro observations, we investigate the\r\ncollective
spreading behavior of colonies of human cancer cells (T24). The spreading of these\r\ncolonies
is driven by stochastic single-cell migration with frequent transient cell-cell
contacts.\r\nWe find that inhibition of E- and N-Cadherin junctions decreases
colony spreading and average\r\nspreading velocities, without affecting the strength
of correlations in spreading velocities of\r\nneighboring cells. Based on a biophysical
simulation model for cell migration, we show that the\r\nbehavioral changes upon
disruption of these junctions can be explained by reduced repulsive\r\nexcluded
volume interactions between cells. This suggests that in cancer cell migration,\r\ncadherin-based
intercellular contacts sharpen cell boundaries leading to repulsive rather than\r\ncohesive
interactions between cells, thereby promoting efficient cell spreading during
collective\r\nmigration.\r\n"
acknowledgement: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) - Project-ID 201269156 - SFB 1032 (Projects B8 and B12). D.B.B. is supported
in part by a DFG fellowship within the Graduate School of Quantitative Biosciences
Munich (QBM) and by the Joachim Herz Stiftung.
article_processing_charge: No
article_type: original
author:
- first_name: Themistoklis
full_name: Zisis, Themistoklis
last_name: Zisis
- first_name: David
full_name: Brückner, David
id: e1e86031-6537-11eb-953a-f7ab92be508d
last_name: Brückner
orcid: 0000-0001-7205-2975
- first_name: Tom
full_name: Brandstätter, Tom
last_name: Brandstätter
- first_name: Wei Xiong
full_name: Siow, Wei Xiong
last_name: Siow
- first_name: Joseph
full_name: d’Alessandro, Joseph
last_name: d’Alessandro
- first_name: Angelika M.
full_name: Vollmar, Angelika M.
last_name: Vollmar
- first_name: Chase P.
full_name: Broedersz, Chase P.
last_name: Broedersz
- first_name: Stefan
full_name: Zahler, Stefan
last_name: Zahler
citation:
ama: Zisis T, Brückner D, Brandstätter T, et al. Disentangling cadherin-mediated
cell-cell interactions in collective cancer cell migration. Biophysical Journal.
2022;121(1):P44-60. doi:10.1016/j.bpj.2021.12.006
apa: Zisis, T., Brückner, D., Brandstätter, T., Siow, W. X., d’Alessandro, J., Vollmar,
A. M., … Zahler, S. (2022). Disentangling cadherin-mediated cell-cell interactions
in collective cancer cell migration. Biophysical Journal. Elsevier. https://doi.org/10.1016/j.bpj.2021.12.006
chicago: Zisis, Themistoklis, David Brückner, Tom Brandstätter, Wei Xiong Siow,
Joseph d’Alessandro, Angelika M. Vollmar, Chase P. Broedersz, and Stefan Zahler.
“Disentangling Cadherin-Mediated Cell-Cell Interactions in Collective Cancer Cell
Migration.” Biophysical Journal. Elsevier, 2022. https://doi.org/10.1016/j.bpj.2021.12.006.
ieee: T. Zisis et al., “Disentangling cadherin-mediated cell-cell interactions
in collective cancer cell migration,” Biophysical Journal, vol. 121, no.
1. Elsevier, pp. P44-60, 2022.
ista: Zisis T, Brückner D, Brandstätter T, Siow WX, d’Alessandro J, Vollmar AM,
Broedersz CP, Zahler S. 2022. Disentangling cadherin-mediated cell-cell interactions
in collective cancer cell migration. Biophysical Journal. 121(1), P44-60.
mla: Zisis, Themistoklis, et al. “Disentangling Cadherin-Mediated Cell-Cell Interactions
in Collective Cancer Cell Migration.” Biophysical Journal, vol. 121, no.
1, Elsevier, 2022, pp. P44-60, doi:10.1016/j.bpj.2021.12.006.
short: T. Zisis, D. Brückner, T. Brandstätter, W.X. Siow, J. d’Alessandro, A.M.
Vollmar, C.P. Broedersz, S. Zahler, Biophysical Journal 121 (2022) P44-60.
date_created: 2021-12-10T09:48:19Z
date_published: 2022-01-04T00:00:00Z
date_updated: 2023-08-02T13:34:25Z
day: '04'
ddc:
- '570'
department:
- _id: EdHa
- _id: GaTk
doi: 10.1016/j.bpj.2021.12.006
external_id:
isi:
- '000740815400007'
file:
- access_level: open_access
checksum: 1aa7c3478e0c8256b973b632efd1f6b4
content_type: application/pdf
creator: dernst
date_created: 2022-07-29T10:17:10Z
date_updated: 2022-07-29T10:17:10Z
file_id: '11697'
file_name: 2022_BiophysicalJour_Zisis.pdf
file_size: 4475504
relation: main_file
success: 1
file_date_updated: 2022-07-29T10:17:10Z
has_accepted_license: '1'
intvolume: ' 121'
isi: 1
issue: '1'
keyword:
- Biophysics
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: P44-60
project:
- _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A
name: NOMIS Fellowship Program
publication: Biophysical Journal
publication_identifier:
issn:
- 0006-3495
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: Disentangling cadherin-mediated cell-cell interactions in collective cancer
cell migration
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: 121
year: '2022'
...
---
_id: '10736'
abstract:
- lang: eng
text: Predicting function from sequence is a central problem of biology. Currently,
this is possible only locally in a narrow mutational neighborhood around a wildtype
sequence rather than globally from any sequence. Using random mutant libraries,
we developed a biophysical model that accounts for multiple features of σ70 binding
bacterial promoters to predict constitutive gene expression levels from any sequence.
We experimentally and theoretically estimated that 10–20% of random sequences
lead to expression and ~80% of non-expressing sequences are one mutation away
from a functional promoter. The potential for generating expression from random
sequences is so pervasive that selection acts against σ70-RNA polymerase binding
sites even within inter-genic, promoter-containing regions. This pervasiveness
of σ70-binding sites implies that emergence of promoters is not the limiting step
in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter
function into a mechanistic model enabled not only more accurate predictions of
gene expression levels, but also identified that promoters evolve more rapidly
than previously thought.
acknowledgement: 'We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao,
Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on
the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded
by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML;
IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme
7 (2007–2013, grant agreement number 648440) to JPB.'
article_number: e64543
article_processing_charge: No
article_type: original
author:
- first_name: Mato
full_name: Lagator, Mato
id: 345D25EC-F248-11E8-B48F-1D18A9856A87
last_name: Lagator
- first_name: Srdjan
full_name: Sarikas, Srdjan
id: 35F0286E-F248-11E8-B48F-1D18A9856A87
last_name: Sarikas
- first_name: Magdalena
full_name: Steinrueck, Magdalena
last_name: Steinrueck
- first_name: David
full_name: Toledo-Aparicio, David
last_name: Toledo-Aparicio
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function
and evolution from random sequences. eLife. 2022;11. doi:10.7554/eLife.64543
apa: Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J.
P., Guet, C. C., & Tkačik, G. (2022). Predicting bacterial promoter function
and evolution from random sequences. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.64543
chicago: Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio,
Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter
Function and Evolution from Random Sequences.” ELife. eLife Sciences Publications,
2022. https://doi.org/10.7554/eLife.64543.
ieee: M. Lagator et al., “Predicting bacterial promoter function and evolution
from random sequences,” eLife, vol. 11. eLife Sciences Publications, 2022.
ista: Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC,
Tkačik G. 2022. Predicting bacterial promoter function and evolution from random
sequences. eLife. 11, e64543.
mla: Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution
from Random Sequences.” ELife, vol. 11, e64543, eLife Sciences Publications,
2022, doi:10.7554/eLife.64543.
short: M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback,
C.C. Guet, G. Tkačik, ELife 11 (2022).
date_created: 2022-02-06T23:01:32Z
date_published: 2022-01-26T00:00:00Z
date_updated: 2023-08-02T14:09:02Z
day: '26'
ddc:
- '576'
department:
- _id: CaGu
- _id: GaTk
- _id: NiBa
doi: 10.7554/eLife.64543
ec_funded: 1
external_id:
isi:
- '000751104400001'
pmid:
- '35080492'
file:
- access_level: open_access
checksum: decdcdf600ff51e9a9703b49ca114170
content_type: application/pdf
creator: cchlebak
date_created: 2022-02-07T07:14:09Z
date_updated: 2022-02-07T07:14:09Z
file_id: '10739'
file_name: 2022_ELife_Lagator.pdf
file_size: 5604343
relation: main_file
success: 1
file_date_updated: 2022-02-07T07:14:09Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2578D616-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '648440'
name: Selective Barriers to Horizontal Gene Transfer
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predicting bacterial promoter function and evolution from random sequences
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: 11
year: '2022'
...
---
_id: '12332'
abstract:
- lang: eng
text: Activity of sensory neurons is driven not only by external stimuli but also
by feedback signals from higher brain areas. Attention is one particularly important
internal signal whose presumed role is to modulate sensory representations such
that they only encode information currently relevant to the organism at minimal
cost. This hypothesis has, however, not yet been expressed in a normative computational
framework. Here, by building on normative principles of probabilistic inference
and efficient coding, we developed a model of dynamic population coding in the
visual cortex. By continuously adapting the sensory code to changing demands of
the perceptual observer, an attention-like modulation emerges. This modulation
can dramatically reduce the amount of neural activity without deteriorating the
accuracy of task-specific inferences. Our results suggest that a range of seemingly
disparate cortical phenomena such as intrinsic gain modulation, attention-related
tuning modulation, and response variability could be manifestations of the same
underlying principles, which combine efficient sensory coding with optimal probabilistic
inference in dynamic environments.
acknowledgement: "We thank Robbe Goris for generously providing figures from his work
and Ann M. Hermundstad for helpful discussions.\r\nGT & WM were supported by the
Austrian Science Fund Standalone Grant P 34015 \"Efficient Coding with Biophysical
Realism\" (https://pf.fwf.ac.at/) WM was additionally supported by the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 754411 (https://ec.europa.eu/research/mariecurieactions/). The
funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript."
article_processing_charge: No
article_type: original
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Mlynarski WF, Tkačik G. Efficient coding theory of dynamic attentional modulation.
PLoS Biology. 2022;20(12):e3001889. doi:10.1371/journal.pbio.3001889
apa: Mlynarski, W. F., & Tkačik, G. (2022). Efficient coding theory of dynamic
attentional modulation. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.3001889
chicago: Mlynarski, Wiktor F, and Gašper Tkačik. “Efficient Coding Theory of Dynamic
Attentional Modulation.” PLoS Biology. Public Library of Science, 2022.
https://doi.org/10.1371/journal.pbio.3001889.
ieee: W. F. Mlynarski and G. Tkačik, “Efficient coding theory of dynamic attentional
modulation,” PLoS Biology, vol. 20, no. 12. Public Library of Science,
p. e3001889, 2022.
ista: Mlynarski WF, Tkačik G. 2022. Efficient coding theory of dynamic attentional
modulation. PLoS Biology. 20(12), e3001889.
mla: Mlynarski, Wiktor F., and Gašper Tkačik. “Efficient Coding Theory of Dynamic
Attentional Modulation.” PLoS Biology, vol. 20, no. 12, Public Library
of Science, 2022, p. e3001889, doi:10.1371/journal.pbio.3001889.
short: W.F. Mlynarski, G. Tkačik, PLoS Biology 20 (2022) e3001889.
date_created: 2023-01-22T23:00:55Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2023-08-03T14:23:49Z
day: '21'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pbio.3001889
ec_funded: 1
external_id:
isi:
- '000925192000001'
file:
- access_level: open_access
checksum: 5d7f1111a87e5f2c1bf92f8886738894
content_type: application/pdf
creator: dernst
date_created: 2023-01-23T08:46:40Z
date_updated: 2023-01-23T08:46:40Z
file_id: '12337'
file_name: 2022_PloSBiology_Mlynarski.pdf
file_size: 4248838
relation: main_file
success: 1
file_date_updated: 2023-01-23T08:46:40Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: e3001889
project:
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: PLoS Biology
publication_identifier:
eissn:
- 1545-7885
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient coding theory of dynamic attentional modulation
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: 20
year: '2022'
...
---
_id: '12081'
abstract:
- lang: eng
text: 'Selection accumulates information in the genome—it guides stochastically
evolving populations toward states (genotype frequencies) that would be unlikely
under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence
between the actual distribution of genotype frequencies and the corresponding
neutral distribution. First, we show that this population-level information sets
an upper bound on the information at the level of genotype and phenotype, limiting
how precisely they can be specified by selection. Next, we study how the accumulation
and maintenance of information is limited by the cost of selection, measured as
the genetic load or the relative fitness variance, both of which we connect to
the control-theoretic KL cost of control. The information accumulation rate is
upper bounded by the population size times the cost of selection. This bound is
very general, and applies across models (Wright–Fisher, Moran, diffusion) and
to arbitrary forms of selection, mutation, and recombination. Finally, the cost
of maintaining information depends on how it is encoded: Specifying a single allele
out of two is expensive, but one bit encoded among many weakly specified loci
(as in a polygenic trait) is cheap.'
acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and
two anonymous reviewers for discussions and comments on the manuscript. G.T. and
M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018.
N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”
article_number: e2123152119
article_processing_charge: No
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 2022;119(36).
doi:10.1073/pnas.2123152119
apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance
of information in evolution. Proceedings of the National Academy of Sciences.
Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119
chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and
Maintenance of Information in Evolution.” Proceedings of the National Academy
of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119.
ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information
in evolution,” Proceedings of the National Academy of Sciences, vol. 119,
no. 36. Proceedings of the National Academy of Sciences, 2022.
ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.
mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.”
Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119,
Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119.
short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of
Sciences 119 (2022).
date_created: 2022-09-11T22:01:55Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2024-03-06T14:22:51Z
day: '29'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1073/pnas.2123152119
ec_funded: 1
external_id:
isi:
- '000889278400014'
pmid:
- '36037343'
file:
- access_level: open_access
checksum: 6dec51f6567da9039982a571508a8e4d
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T08:08:12Z
date_updated: 2022-09-12T08:08:12Z
file_id: '12091'
file_name: 2022_PNAS_Hledik.pdf
file_size: 2165752
relation: main_file
success: 1
file_date_updated: 2022-09-12T08:08:12Z
has_accepted_license: '1'
intvolume: ' 119'
isi: 1
issue: '36'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: Proceedings of the National Academy of Sciences
quality_controlled: '1'
related_material:
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Accumulation and maintenance of information in evolution
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: 119
year: '2022'
...
---
_id: '10535'
abstract:
- lang: eng
text: Realistic models of biological processes typically involve interacting components
on multiple scales, driven by changing environment and inherent stochasticity.
Such models are often analytically and numerically intractable. We revisit a dynamic
maximum entropy method that combines a static maximum entropy with a quasi-stationary
approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed
by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics,
without the need to track microscopic details. Although the method has been previously
applied to a few (rather complicated) applications in population genetics, our
main goal here is to explain and to better understand how the method works. We
demonstrate the usefulness of the method for two widely studied stochastic problems,
highlighting its accuracy in capturing important macroscopic quantities even in
rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process,
the method recovers the exact dynamics whilst for a stochastic island model with
migration from other habitats, the approximation retains high macroscopic accuracy
under a wide range of scenarios in a dynamic environment.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "Computational resources for the study were provided by the Institute
of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant
Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20."
article_number: e1009661
article_processing_charge: No
article_type: original
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation
of structured population dynamics. PLoS Computational Biology. 2021;17(12).
doi:10.1371/journal.pcbi.1009661
apa: Bodova, K., Szep, E., & Barton, N. H. (2021). Dynamic maximum entropy provides
accurate approximation of structured population dynamics. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009661
chicago: Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy
Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational
Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009661.
ieee: K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate
approximation of structured population dynamics,” PLoS Computational Biology,
vol. 17, no. 12. Public Library of Science, 2021.
ista: Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate
approximation of structured population dynamics. PLoS Computational Biology. 17(12),
e1009661.
mla: Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation
of Structured Population Dynamics.” PLoS Computational Biology, vol. 17,
no. 12, e1009661, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009661.
short: K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).
date_created: 2021-12-12T23:01:27Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-08-01T10:48:04Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1371/journal.pcbi.1009661
external_id:
arxiv:
- '2102.03669'
pmid:
- '34851948'
file:
- access_level: open_access
checksum: dcd185d4f7e0acee25edf1d6537f447e
content_type: application/pdf
creator: dernst
date_created: 2022-05-16T08:53:11Z
date_updated: 2022-05-16T08:53:11Z
file_id: '11383'
file_name: 2021_PLOsComBio_Bodova.pdf
file_size: 2299486
relation: main_file
success: 1
file_date_updated: 2022-05-16T08:53:11Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Dynamic maximum entropy provides accurate approximation of structured population
dynamics
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2021'
...
---
_id: '10912'
abstract:
- lang: eng
text: Brain dynamics display collective phenomena as diverse as neuronal oscillations
and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic
scale, whereas avalanches are scale-free cascades of neural activity. Here we
show that such antithetic features can coexist in a very generic class of adaptive
neural networks. In the most simple yet fully microscopic model from this class
we make direct contact with human brain resting-state activity recordings via
tractable inference of the model's two essential parameters. The inferred model
quantitatively captures the dynamics over a broad range of scales, from single
sensor fluctuations, collective behaviors of nearly-synchronous extreme events
on multiple sensors, to neuronal avalanches unfolding over multiple sensors across
multiple time-bins. Importantly, the inferred parameters correlate with model-independent
signatures of "closeness to criticality", suggesting that the coexistence of scale-specific
(neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity
occurs close to a non-equilibrium critical point at the onset of self-sustained
oscillations.
acknowledgement: "FL acknowledges support from the European Union’s Horizon 2020 research
and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411.
GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone
Grant\r\nNo. P34015."
article_processing_charge: No
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Selver
full_name: Pepic, Selver
id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425
last_name: Pepic
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Daniele
full_name: De Martino, Daniele
last_name: De Martino
citation:
ama: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. doi:10.48550/ARXIV.2108.06686
apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (n.d.).
Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. https://doi.org/10.48550/ARXIV.2108.06686
chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele
De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.”
arXiv, n.d. https://doi.org/10.48550/ARXIV.2108.06686.
ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying
the coexistence of neuronal oscillations and avalanches.” arXiv.
ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. 10.48550/ARXIV.2108.06686.
mla: Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations
and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686.
short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).
date_created: 2022-03-21T11:41:28Z
date_published: 2021-08-17T00:00:00Z
date_updated: 2022-03-22T07:53:18Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.48550/ARXIV.2108.06686
ec_funded: 1
external_id:
arxiv:
- '2108.06686'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2108.06686
month: '08'
oa: 1
oa_version: Preprint
page: '37'
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication_status: submitted
publisher: arXiv
status: public
title: Quantifying the coexistence of neuronal oscillations and avalanches
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10579'
abstract:
- lang: eng
text: 'We consider a totally asymmetric simple exclusion process (TASEP) consisting
of particles on a lattice that require binding by a "token" to move. Using a combination
of theory and simulations, we address the following questions: (i) How token binding
kinetics affects the current-density relation; (ii) How the current-density relation
depends on the scarcity of tokens; (iii) How tokens propagate the effects of the
locally-imposed disorder (such a slow site) over the entire lattice; (iv) How
a shared pool of tokens couples concurrent TASEPs running on multiple lattices;
(v) How our results translate to TASEPs with open boundaries that exchange particles
with the reservoir. Since real particle motion (including in systems that inspired
the standard TASEP model, e.g., protein synthesis or movement of molecular motors)
is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven
TASEP dynamics analyzed in this paper should allow for a better understanding
of real systems and enable a closer match between TASEP theory and experimental
observations.'
acknowledgement: B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for
their help with the usage of the cluster. B.K. additionally thanks Călin Guet and
his group for help and advice. We thank M. Hennessey-Wesen for constructive comments
on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting
a typographical error in Eq. (49) in the preprint version of this paper.
article_number: '2112.13558'
article_processing_charge: No
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv. doi:10.48550/arXiv.2112.13558
apa: Kavcic, B., & Tkačik, G. (n.d.). Token-driven totally asymmetric simple
exclusion process. arXiv. https://doi.org/10.48550/arXiv.2112.13558
chicago: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple
Exclusion Process.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2112.13558.
ieee: B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion
process,” arXiv. .
ista: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv, 2112.13558.
mla: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion
Process.” ArXiv, 2112.13558, doi:10.48550/arXiv.2112.13558.
short: B. Kavcic, G. Tkačik, ArXiv (n.d.).
date_created: 2021-12-28T06:52:09Z
date_published: 2021-12-27T00:00:00Z
date_updated: 2023-05-03T10:54:05Z
day: '27'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.48550/arXiv.2112.13558
external_id:
arxiv:
- '2112.13558'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.13558
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Token-driven totally asymmetric simple exclusion process
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: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '7463'
abstract:
- lang: eng
text: Resting-state brain activity is characterized by the presence of neuronal
avalanches showing absence of characteristic size. Such evidence has been interpreted
in the context of criticality and associated with the normal functioning of the
brain. A distinctive attribute of systems at criticality is the presence of long-range
correlations. Thus, to verify the hypothesis that the brain operates close to
a critical point and consequently assess deviations from criticality for diagnostic
purposes, it is of primary importance to robustly and reliably characterize correlations
in resting-state brain activity. Recent works focused on the analysis of narrow-band
electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude
envelope, showing evidence of long-range temporal correlations (LRTC) in neural
oscillations. However, brain activity is a broadband phenomenon, and a significant
piece of information useful to precisely discriminate between normal (critical)
and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal
cortical dynamics. Here we propose to characterize the temporal correlations in
the broadband brain activity through the lens of neuronal avalanches. To this
end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding
neuronal avalanche sequences, and study their temporal correlations. We demonstrate
that the broadband resting-state brain activity consistently exhibits long-range
power-law correlations in both EEG and MEG recordings, with similar values of
the scaling exponents. Importantly, although we observe that the avalanche size
distribution depends on scale parameters, scaling exponents characterizing long-range
correlations are quite robust. In particular, they are independent of the temporal
binning (scale of analysis), indicating that our analysis captures intrinsic characteristics
of the underlying dynamics. Because neuronal avalanches constitute a fundamental
feature of neural systems with universal characteristics, the proposed approach
may serve as a general, systems- and experiment-independent procedure to infer
the existence of underlying long-range correlations in extended neural systems,
and identify pathological behaviors in the complex spatio-temporal interplay of
cortical rhythms.
acknowledgement: LdA would like to acknowledge the financial support from MIUR-PRIN2017
WZFTZP and VALERE:VAnviteLli pEr la RicErca 2019. FL acknowledges support from the
European Union’s Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie Grant Agreement No. 754411. HJH would like to thank the Agencies
CAPES and FUNCAP for financial support.
article_processing_charge: No
article_type: original
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Hans J
full_name: Herrmann, Hans J
last_name: Herrmann
- first_name: Lucilla
full_name: de Arcangelis, Lucilla
last_name: de Arcangelis
citation:
ama: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. Long-range temporal correlations
in the broadband resting state activity of the human brain revealed by neuronal
avalanches. Neurocomputing. 2021;461:657-666. doi:10.1016/j.neucom.2020.05.126
apa: Lombardi, F., Shriki, O., Herrmann, H. J., & de Arcangelis, L. (2021).
Long-range temporal correlations in the broadband resting state activity of the
human brain revealed by neuronal avalanches. Neurocomputing. Elsevier.
https://doi.org/10.1016/j.neucom.2020.05.126
chicago: Lombardi, Fabrizio, Oren Shriki, Hans J Herrmann, and Lucilla de Arcangelis.
“Long-Range Temporal Correlations in the Broadband Resting State Activity of the
Human Brain Revealed by Neuronal Avalanches.” Neurocomputing. Elsevier,
2021. https://doi.org/10.1016/j.neucom.2020.05.126.
ieee: F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range
temporal correlations in the broadband resting state activity of the human brain
revealed by neuronal avalanches,” Neurocomputing, vol. 461. Elsevier, pp.
657–666, 2021.
ista: Lombardi F, Shriki O, Herrmann HJ, de Arcangelis L. 2021. Long-range temporal
correlations in the broadband resting state activity of the human brain revealed
by neuronal avalanches. Neurocomputing. 461, 657–666.
mla: Lombardi, Fabrizio, et al. “Long-Range Temporal Correlations in the Broadband
Resting State Activity of the Human Brain Revealed by Neuronal Avalanches.” Neurocomputing,
vol. 461, Elsevier, 2021, pp. 657–66, doi:10.1016/j.neucom.2020.05.126.
short: F. Lombardi, O. Shriki, H.J. Herrmann, L. de Arcangelis, Neurocomputing 461
(2021) 657–666.
date_created: 2020-02-06T16:09:14Z
date_published: 2021-05-13T00:00:00Z
date_updated: 2023-08-04T10:46:29Z
day: '13'
department:
- _id: GaTk
doi: 10.1016/j.neucom.2020.05.126
ec_funded: 1
external_id:
isi:
- '000704086300015'
intvolume: ' 461'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.02.03.930966
month: '05'
oa: 1
oa_version: Preprint
page: 657-666
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neurocomputing
publication_identifier:
eissn:
- 1872-8286
issn:
- 0925-2312
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Long-range temporal correlations in the broadband resting state activity of
the human brain revealed by neuronal avalanches
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 461
year: '2021'
...