---
APC_amount: 3237,62 EUR
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '19640'
abstract:
- lang: eng
text: Synaptic plasticity is a key player in the brain’s life-long learning abilities.
However, due to experimental limitations, the mechanistic link between synaptic
plasticity rules and the network-level computations they enable remain opaque.
Here we use evolutionary strategies (ES) to meta learn local co-active plasticity
rules in large recurrent spiking networks with excitatory (E) and inhibitory (I)
neurons, using parameterizations of increasing complexity. We discover rules that
robustly stabilize network dynamics for all four synapse types acting in isolation
(E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity
detection can also be included in the search constraints. However, our meta learning
strategy begins to fail for co-active rules of increasing complexity, as it is
challenging to devise loss functions that effectively constrain network dynamics
to plausible solutions a priori. Moreover, in line with previous work, we can
find multiple degenerate solutions with identical network behaviour. As a local
optimization strategy, ES provides one solution at a time and makes exploration
of this degeneracy cumbersome. Regardless, we can glean the interdependecies of
various plasticity parameters by considering the covariance matrix learned alongside
the optimal rule with ES. Our work provides a proof of principle for the success
of machine-learning-guided discovery of plasticity rules in large spiking networks,
and points at the necessity of more elaborate search strategies going forward.
acknowledgement: "We would like to thank Chaitanya Chintaluri, Nicoleta Condruz and
Douglas Feitosa Tomé for insightful discussions. This project has received funding
from the HORIZON EUROPE European Research Council (ERC) consolidator grant\r\n(SYNAPSEEK,
awarded to TV), a Wellcome Trust Sir Henry Dale Research Fellowship (WT100000, awarded
to TV), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z, awarded to TV),
and a Sir Henry Wellcome\r\nFellowship (110124/Z/15/Z, awarded to FZ). The funders
had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript."
article_number: e1012910
article_processing_charge: Yes
article_type: original
author:
- first_name: Basile J
full_name: Confavreux, Basile J
id: C7610134-B532-11EA-BD9F-F5753DDC885E
last_name: Confavreux
- first_name: Everton J.
full_name: Agnes, Everton J.
last_name: Agnes
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
- first_name: Henning
full_name: Sprekeler, Henning
last_name: Sprekeler
- first_name: Tim P
full_name: Vogels, Tim P
id: CB6FF8D2-008F-11EA-8E08-2637E6697425
last_name: Vogels
orcid: 0000-0003-3295-6181
citation:
ama: Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. Balancing complexity,
performance and plausibility to meta learn plasticity rules in recurrent spiking
networks. PLoS Computational Biology. 2025;21(4). doi:10.1371/journal.pcbi.1012910
apa: Confavreux, B. J., Agnes, E. J., Zenke, F., Sprekeler, H., & Vogels, T.
P. (2025). Balancing complexity, performance and plausibility to meta learn plasticity
rules in recurrent spiking networks. PLoS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1012910
chicago: Confavreux, Basile J, Everton J. Agnes, Friedemann Zenke, Henning Sprekeler,
and Tim P Vogels. “Balancing Complexity, Performance and Plausibility to Meta
Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational Biology.
Public Library of Science, 2025. https://doi.org/10.1371/journal.pcbi.1012910.
ieee: B. J. Confavreux, E. J. Agnes, F. Zenke, H. Sprekeler, and T. P. Vogels, “Balancing
complexity, performance and plausibility to meta learn plasticity rules in recurrent
spiking networks,” PLoS Computational Biology, vol. 21, no. 4. Public Library
of Science, 2025.
ista: Confavreux BJ, Agnes EJ, Zenke F, Sprekeler H, Vogels TP. 2025. Balancing
complexity, performance and plausibility to meta learn plasticity rules in recurrent
spiking networks. PLoS Computational Biology. 21(4), e1012910.
mla: Confavreux, Basile J., et al. “Balancing Complexity, Performance and Plausibility
to Meta Learn Plasticity Rules in Recurrent Spiking Networks.” PLoS Computational
Biology, vol. 21, no. 4, e1012910, Public Library of Science, 2025, doi:10.1371/journal.pcbi.1012910.
short: B.J. Confavreux, E.J. Agnes, F. Zenke, H. Sprekeler, T.P. Vogels, PLoS Computational
Biology 21 (2025).
corr_author: '1'
date_created: 2025-05-04T22:02:31Z
date_published: 2025-04-24T00:00:00Z
date_updated: 2026-05-06T13:17:52Z
day: '24'
ddc:
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1012910
ec_funded: 1
external_id:
isi:
- '001474257000002'
pmid:
- '40273284 '
file:
- access_level: open_access
checksum: 6437a1aab52813ab7e310e3b4fb36e3b
content_type: application/pdf
creator: dernst
date_created: 2025-05-05T11:17:49Z
date_updated: 2025-05-05T11:17:49Z
file_id: '19654'
file_name: 2025_PLoSCompBio_Confavreux.pdf
file_size: 9771636
relation: main_file
success: 1
file_date_updated: 2025-05-05T11:17:49Z
has_accepted_license: '1'
intvolume: ' 21'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 0aacfa84-070f-11eb-9043-d7eb2c709234
call_identifier: H2020
grant_number: '819603'
name: Learning the shape of synaptic plasticity rules for neuronal architectures
and function through machine learning.
- _id: c084a126-5a5b-11eb-8a69-d75314a70a87
grant_number: 214316/Z/18/Z
name: What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent
neuronal networks.
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/VogelsLab/SpikES
scopus_import: '1'
status: public
title: Balancing complexity, performance and plausibility to meta learn plasticity
rules in recurrent spiking networks
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: 21
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '15169'
abstract:
- lang: eng
text: Interpretation of extracellular recordings can be challenging due to the long
range of electric field. This challenge can be mitigated by estimating the current
source density (CSD). Here we introduce kCSD-python, an open Python package implementing
Kernel Current Source Density (kCSD) method and related tools to facilitate CSD
analysis of experimental data and the interpretation of results. We show how to
counter the limitations imposed by noise and assumptions in the method itself.
kCSD-python allows CSD estimation for an arbitrary distribution of electrodes
in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in
a single cell, and includes a range of diagnostic aids. We demonstrate its features
in a Jupyter Notebook tutorial which illustrates a typical analytical workflow
and main functionalities useful in validating analysis results.
acknowledgement: 'The Python implementation of kCSD was started by Grzegorz Parka
during Google Summer of Code project through the International Neuroinformatics
Coordinating Facility. Jan Mąka implemented the first Python version of skCSD class.
This work was supported by the Polish National Science Centre (2013/08/W/NZ4/00691
to DKW; 2015/17/B/ST7/04123 to DKW). '
article_number: e1011941
article_processing_charge: Yes
article_type: original
author:
- first_name: Chaitanya
full_name: Chintaluri, Chaitanya
id: E4EDB536-3485-11EA-98D2-20AF3DDC885E
last_name: Chintaluri
- first_name: Marta
full_name: Bejtka, Marta
last_name: Bejtka
- first_name: Wladyslaw
full_name: Sredniawa, Wladyslaw
last_name: Sredniawa
- first_name: Michal
full_name: Czerwinski, Michal
last_name: Czerwinski
- first_name: Jakub M.
full_name: Dzik, Jakub M.
last_name: Dzik
- first_name: Joanna
full_name: Jedrzejewska-Szmek, Joanna
last_name: Jedrzejewska-Szmek
- first_name: Daniel K.
full_name: Wojciki, Daniel K.
last_name: Wojciki
citation:
ama: Chintaluri C, Bejtka M, Sredniawa W, et al. kCSD-python, reliable current source
density estimation with quality control. PLoS Computational Biology. 2024;20(3).
doi:10.1371/journal.pcbi.1011941
apa: Chintaluri, C., Bejtka, M., Sredniawa, W., Czerwinski, M., Dzik, J. M., Jedrzejewska-Szmek,
J., & Wojciki, D. K. (2024). kCSD-python, reliable current source density
estimation with quality control. PLoS Computational Biology. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1011941
chicago: Chintaluri, Chaitanya, Marta Bejtka, Wladyslaw Sredniawa, Michal Czerwinski,
Jakub M. Dzik, Joanna Jedrzejewska-Szmek, and Daniel K. Wojciki. “KCSD-Python,
Reliable Current Source Density Estimation with Quality Control.” PLoS Computational
Biology. Public Library of Science, 2024. https://doi.org/10.1371/journal.pcbi.1011941.
ieee: C. Chintaluri et al., “kCSD-python, reliable current source density
estimation with quality control,” PLoS Computational Biology, vol. 20,
no. 3. Public Library of Science, 2024.
ista: Chintaluri C, Bejtka M, Sredniawa W, Czerwinski M, Dzik JM, Jedrzejewska-Szmek
J, Wojciki DK. 2024. kCSD-python, reliable current source density estimation with
quality control. PLoS Computational Biology. 20(3), e1011941.
mla: Chintaluri, Chaitanya, et al. “KCSD-Python, Reliable Current Source Density
Estimation with Quality Control.” PLoS Computational Biology, vol. 20,
no. 3, e1011941, Public Library of Science, 2024, doi:10.1371/journal.pcbi.1011941.
short: C. Chintaluri, M. Bejtka, W. Sredniawa, M. Czerwinski, J.M. Dzik, J. Jedrzejewska-Szmek,
D.K. Wojciki, PLoS Computational Biology 20 (2024).
corr_author: '1'
date_created: 2024-03-24T23:00:59Z
date_published: 2024-03-14T00:00:00Z
date_updated: 2025-09-04T13:08:54Z
day: '14'
ddc:
- '000'
- '570'
department:
- _id: TiVo
doi: 10.1371/journal.pcbi.1011941
external_id:
isi:
- '001190689800001'
pmid:
- '38484020'
file:
- access_level: open_access
checksum: c09718d0d09614642d877d0716ce32e8
content_type: application/pdf
creator: dernst
date_created: 2025-06-25T05:47:36Z
date_updated: 2025-06-25T05:47:36Z
file_id: '19897'
file_name: 2024_PLoSCompBio_Chintaluri.pdf
file_size: 2540277
relation: main_file
success: 1
file_date_updated: 2025-06-25T05:47:36Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
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'
related_material:
link:
- relation: software
url: https://github.com/Neuroinflab/kCSD-python
scopus_import: '1'
status: public
title: kCSD-python, reliable current source density estimation with quality control
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 20
year: '2024'
...
---
APC_amount: 3149,96 EUR
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '15297'
abstract:
- lang: eng
text: Populations evolve by accumulating advantageous mutations. Every population
has some spatial structure that can be modeled by an underlying network. The network
then influences the probability that new advantageous mutations fixate. Amplifiers
of selection are networks that increase the fixation probability of advantageous
mutants, as compared to the unstructured fully-connected network. Whether or not
a network is an amplifier depends on the choice of the random process that governs
the evolutionary dynamics. Two popular choices are Moran process with Birth-death
updating and Moran process with death-Birth updating. Interestingly, while some
networks are amplifiers under Birth-death updating and other networks are amplifiers
under death-Birth updating, so far no spatial structures have been found that
function as an amplifier under both types of updating simultaneously. In this
work, we identify networks that act as amplifiers of selection under both versions
of the Moran process. The amplifiers are robust, modular, and increase fixation
probability for any mutant fitness advantage in a range r ∈ (1, 1.2). To complement
this positive result, we also prove that for certain quantities closely related
to fixation probability, it is impossible to improve them simultaneously for both
versions of the Moran process. Together, our results highlight how the two versions
of the Moran process differ and what they have in common.
acknowledgement: "We thank Gavin Rees for helpful discussions. J.S., S.J., and K.C
were supported by\r\nEuropean Research Council (ERC) CoG 863818 (ForM-SMArt). J.T
was supported by Center for Foundations of Modern Computer Science (Charles University
project UNCE/SCI/004) and by the project PRIMUS/24/SCI/012 from Charles University. "
article_number: e1012008
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Jakub
full_name: Svoboda, Jakub
id: 130759D2-D7DD-11E9-87D2-DE0DE6697425
last_name: Svoboda
orcid: 0000-0002-1419-3267
- first_name: Soham Shrikant
full_name: Joshi, Soham Shrikant
id: f97aac0e-f57c-11ee-93d0-a5a82d8df168
last_name: Joshi
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: Svoboda J, Joshi SS, Tkadlec J, Chatterjee K. Amplifiers of selection for the
Moran process with both Birth-death and death-Birth updating. PLoS Computational
Biology. 2024;20(3). doi:10.1371/journal.pcbi.1012008
apa: Svoboda, J., Joshi, S. S., Tkadlec, J., & Chatterjee, K. (2024). Amplifiers
of selection for the Moran process with both Birth-death and death-Birth updating.
PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1012008
chicago: Svoboda, Jakub, Soham Shrikant Joshi, Josef Tkadlec, and Krishnendu Chatterjee.
“Amplifiers of Selection for the Moran Process with Both Birth-Death and Death-Birth
Updating.” PLoS Computational Biology. Public Library of Science, 2024.
https://doi.org/10.1371/journal.pcbi.1012008.
ieee: J. Svoboda, S. S. Joshi, J. Tkadlec, and K. Chatterjee, “Amplifiers of selection
for the Moran process with both Birth-death and death-Birth updating,” PLoS
Computational Biology, vol. 20, no. 3. Public Library of Science, 2024.
ista: Svoboda J, Joshi SS, Tkadlec J, Chatterjee K. 2024. Amplifiers of selection
for the Moran process with both Birth-death and death-Birth updating. PLoS Computational
Biology. 20(3), e1012008.
mla: Svoboda, Jakub, et al. “Amplifiers of Selection for the Moran Process with
Both Birth-Death and Death-Birth Updating.” PLoS Computational Biology,
vol. 20, no. 3, e1012008, Public Library of Science, 2024, doi:10.1371/journal.pcbi.1012008.
short: J. Svoboda, S.S. Joshi, J. Tkadlec, K. Chatterjee, PLoS Computational Biology
20 (2024).
corr_author: '1'
date_created: 2024-04-07T22:00:55Z
date_published: 2024-03-29T00:00:00Z
date_updated: 2026-04-07T11:49:11Z
day: '29'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1012008
ec_funded: 1
external_id:
arxiv:
- '2401.14914'
isi:
- '001194482400002'
file:
- access_level: open_access
checksum: a511cf369d9172beb123fe73f291b5cc
content_type: application/pdf
creator: dernst
date_created: 2024-08-20T10:52:28Z
date_updated: 2024-08-20T10:52:28Z
file_id: '17450'
file_name: 2024_PloSComBio_Svoboda.pdf
file_size: 1425292
relation: main_file
success: 1
file_date_updated: 2024-08-20T10:52:28Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '20138'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Amplifiers of selection for the Moran process with both Birth-death and death-Birth
updating
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 20
year: '2024'
...
---
APC_amount: 3197,23 EUR
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18481'
abstract:
- lang: eng
text: A tight regulation of morphogen production is key for morphogen gradient formation
and thereby for reproducible and organised organ development. Although many genetic
interactions involved in the establishment of morphogen production domains are
known, the biophysical mechanisms of morphogen source formation are poorly understood.
Here we addressed this by focusing on the morphogen Sonic hedgehog (Shh) in the
vertebrate neural tube. Shh is produced by the adjacently located notochord and
by the floor plate of the neural tube. Using a data-constrained computational
screen, we identified different possible mechanisms by which floor plate formation
can occur, only one of which is consistent with experimental data. In this mechanism,
the floor plate is established rapidly in response to Shh from the notochord and
the dynamics of regulatory interactions within the neural tube. In this process,
uniform activators and Shh-dependent repressors are key for establishing the floor
plate size. Subsequently, the floor plate becomes insensitive to Shh and increases
in size due to tissue growth, leading to scaling of the floor plate with neural
tube size. In turn, this results in scaling of the Shh amplitude with tissue growth.
Thus, this mechanism ensures a separation of time scales in floor plate formation,
so that the floor plate domain becomes growth-dependent after an initial rapid
establishment phase. Our study raises the possibility that the time scale separation
between specification and growth might be a common strategy for scaling the morphogen
gradient amplitude in growing organs. The model that we developed provides a new
opportunity for quantitative studies of morphogen source formation in growing
tissues.
acknowledgement: "We thank Martina Greunz-Schindler for technical support, and Thomas
Minchington and James Briscoe for comments on the manuscript.\r\nRDJGH, MM and MZ
were supported by a grant from the Priority Research Area DigiWorld\r\nunder the
Strategic Programme Excellence Initiative at Jagiellonian University. The research\r\nwas
supported by the Polish National Agency for Academic Exchange, PN/PPO/2018/1/00011/U/00001
which paid the salary of MM and MZ up to Feb 2023. The research received support
from National Science Center, Poland, 2021/42/E/NZ2/00188 which paid salary of MZ.
Work in the AK labis supported by ISTA to KK and AK, the European\r\nResearch Council
under Horizon Europe: grant 101044579 to AK, and Austrian Science Fund\r\n(FWF):
Grant DOI 10.55776/F78 to AK. The salaries of AK and KK were paid by ISTA. The funders
had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript."
article_number: e1012508
article_processing_charge: No
article_type: original
author:
- first_name: Richard D.J.G.
full_name: Ho, Richard D.J.G.
last_name: Ho
- first_name: Kasumi
full_name: Kishi, Kasumi
id: 3065DFC4-F248-11E8-B48F-1D18A9856A87
last_name: Kishi
orcid: 0000-0001-6060-4795
- first_name: Maciej
full_name: Majka, Maciej
last_name: Majka
- first_name: Anna
full_name: Kicheva, Anna
id: 3959A2A0-F248-11E8-B48F-1D18A9856A87
last_name: Kicheva
orcid: 0000-0003-4509-4998
- first_name: Marcin P
full_name: Zagórski, Marcin P
id: 343DA0DC-F248-11E8-B48F-1D18A9856A87
last_name: Zagórski
orcid: 0000-0001-7896-7762
citation:
ama: Ho RDJG, Kishi K, Majka M, Kicheva A, Zagórski MP. Dynamics of morphogen source
formation in a growing tissue. PLoS Computational Biology. 2024;20. doi:10.1371/journal.pcbi.1012508
apa: Ho, R. D. J. G., Kishi, K., Majka, M., Kicheva, A., & Zagórski, M. P. (2024).
Dynamics of morphogen source formation in a growing tissue. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1012508
chicago: Ho, Richard D.J.G., Kasumi Kishi, Maciej Majka, Anna Kicheva, and Marcin
P Zagórski. “Dynamics of Morphogen Source Formation in a Growing Tissue.” PLoS
Computational Biology. Public Library of Science, 2024. https://doi.org/10.1371/journal.pcbi.1012508.
ieee: R. D. J. G. Ho, K. Kishi, M. Majka, A. Kicheva, and M. P. Zagórski, “Dynamics
of morphogen source formation in a growing tissue,” PLoS Computational Biology,
vol. 20. Public Library of Science, 2024.
ista: Ho RDJG, Kishi K, Majka M, Kicheva A, Zagórski MP. 2024. Dynamics of morphogen
source formation in a growing tissue. PLoS Computational Biology. 20, e1012508.
mla: Ho, Richard D. J. G., et al. “Dynamics of Morphogen Source Formation in a Growing
Tissue.” PLoS Computational Biology, vol. 20, e1012508, Public Library
of Science, 2024, doi:10.1371/journal.pcbi.1012508.
short: R.D.J.G. Ho, K. Kishi, M. Majka, A. Kicheva, M.P. Zagórski, PLoS Computational
Biology 20 (2024).
corr_author: '1'
date_created: 2024-10-27T23:01:45Z
date_published: 2024-10-14T00:00:00Z
date_updated: 2026-04-07T12:31:58Z
day: '14'
ddc:
- '570'
department:
- _id: AnKi
doi: 10.1371/journal.pcbi.1012508
external_id:
isi:
- '001331700300003'
pmid:
- '39401260'
file:
- access_level: open_access
checksum: 42fa714459943cb3961b40fab8fd82c8
content_type: application/pdf
creator: dernst
date_created: 2024-10-29T11:59:09Z
date_updated: 2024-10-29T11:59:09Z
file_id: '18487'
file_name: 2024_PloSComBio_Ho.pdf
file_size: 3732443
relation: main_file
success: 1
file_date_updated: 2024-10-29T11:59:09Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: bd7e737f-d553-11ed-ba76-d69ffb5ee3aa
grant_number: '101044579'
name: Mechanisms of tissue size regulation in spinal cord development
- _id: 059DF620-7A3F-11EA-A408-12923DDC885E
grant_number: F7802
name: Stem Cell Modulation in Neural Development and Regeneration/ P02-Morphogen
control of growth and pattern in the spinal cord
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '20393'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Dynamics of morphogen source formation in a growing tissue
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 20
year: '2024'
...
---
_id: '10939'
abstract:
- lang: eng
text: Understanding and characterising biochemical processes inside single cells
requires experimental platforms that allow one to perturb and observe the dynamics
of such processes as well as computational methods to build and parameterise models
from the collected data. Recent progress with experimental platforms and optogenetics
has made it possible to expose each cell in an experiment to an individualised
input and automatically record cellular responses over days with fine time resolution.
However, methods to infer parameters of stochastic kinetic models from single-cell
longitudinal data have generally been developed under the assumption that experimental
data is sparse and that responses of cells to at most a few different input perturbations
can be observed. Here, we investigate and compare different approaches for calculating
parameter likelihoods of single-cell longitudinal data based on approximations
of the chemical master equation (CME) with a particular focus on coupling the
linear noise approximation (LNA) or moment closure methods to a Kalman filter.
We show that, as long as cells are measured sufficiently frequently, coupling
the LNA to a Kalman filter allows one to accurately approximate likelihoods and
to infer model parameters from data even in cases where the LNA provides poor
approximations of the CME. Furthermore, the computational cost of filtering-based
iterative likelihood evaluation scales advantageously in the number of measurement
times and different input perturbations and is thus ideally suited for data obtained
from modern experimental platforms. To demonstrate the practical usefulness of
these results, we perform an experiment in which single cells, equipped with an
optogenetic gene expression system, are exposed to various different light-input
sequences and measured at several hundred time points and use parameter inference
based on iterative likelihood evaluation to parameterise a stochastic model of
the system.
acknowledgement: We thank Virgile Andreani for useful discussions about the model
and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts
of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
article_number: e1009950
article_processing_charge: No
article_type: original
author:
- first_name: Anđela
full_name: Davidović, Anđela
last_name: Davidović
- first_name: Remy P
full_name: Chait, Remy P
id: 3464AE84-F248-11E8-B48F-1D18A9856A87
last_name: Chait
orcid: 0000-0003-0876-3187
- first_name: Gregory
full_name: Batt, Gregory
last_name: Batt
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 2022;18(3). doi:10.1371/journal.pcbi.1009950
apa: Davidović, A., Chait, R. P., Batt, G., & Ruess, J. (2022). Parameter inference
for stochastic biochemical models from perturbation experiments parallelised at
the single cell level. PLoS Computational Biology. Public Library of Science.
https://doi.org/10.1371/journal.pcbi.1009950
chicago: Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter
Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised
at the Single Cell Level.” PLoS Computational Biology. Public Library of
Science, 2022. https://doi.org/10.1371/journal.pcbi.1009950.
ieee: A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for
stochastic biochemical models from perturbation experiments parallelised at the
single cell level,” PLoS Computational Biology, vol. 18, no. 3. Public
Library of Science, 2022.
ista: Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 18(3), e1009950.
mla: Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models
from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS
Computational Biology, vol. 18, no. 3, e1009950, Public Library of Science,
2022, doi:10.1371/journal.pcbi.1009950.
short: A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18
(2022).
date_created: 2022-04-03T22:01:42Z
date_published: 2022-03-18T00:00:00Z
date_updated: 2025-09-09T14:29:53Z
day: '18'
ddc:
- '570'
- '000'
department:
- _id: CaGu
doi: 10.1371/journal.pcbi.1009950
external_id:
isi:
- '001044208400004'
pmid:
- '35303737'
file:
- access_level: open_access
checksum: 458ef542761fb714ced214f240daf6b2
content_type: application/pdf
creator: dernst
date_created: 2022-04-04T10:14:39Z
date_updated: 2022-04-04T10:14:39Z
file_id: '10947'
file_name: 2022_PLoSCompBio_Davidovic.pdf
file_size: 2958642
relation: main_file
success: 1
file_date_updated: 2022-04-04T10:14:39Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
issue: '3'
language:
- iso: eng
month: '03'
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'
related_material:
link:
- relation: software
url: https://gitlab.pasteur.fr/adavidov/inferencelnakf
scopus_import: '1'
status: public
title: Parameter inference for stochastic biochemical models from perturbation experiments
parallelised at the single cell level
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 18
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
arxiv: 1
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).
corr_author: '1'
date_created: 2021-12-12T23:01:27Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2024-10-09T21:01:16Z
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: '9381'
abstract:
- lang: eng
text: 'A game of rock-paper-scissors is an interesting example of an interaction
where none of the pure strategies strictly dominates all others, leading to a
cyclic pattern. In this work, we consider an unstable version of rock-paper-scissors
dynamics and allow individuals to make behavioural mistakes during the strategy
execution. We show that such an assumption can break a cyclic relationship leading
to a stable equilibrium emerging with only one strategy surviving. We consider
two cases: completely random mistakes when individuals have no bias towards any
strategy and a general form of mistakes. Then, we determine conditions for a strategy
to dominate all other strategies. However, given that individuals who adopt a
dominating strategy are still prone to behavioural mistakes in the observed behaviour,
we may still observe extinct strategies. That is, behavioural mistakes in strategy
execution stabilise evolutionary dynamics leading to an evolutionary stable and,
potentially, mixed co-existence equilibrium.'
acknowledgement: Authors would like to thank Christian Hilbe and Martin Nowak for
their inspiring and very helpful feedback on the manuscript.
article_number: e1008523
article_processing_charge: No
article_type: original
author:
- first_name: Maria
full_name: Kleshnina, Maria
id: 4E21749C-F248-11E8-B48F-1D18A9856A87
last_name: Kleshnina
- first_name: Sabrina S.
full_name: Streipert, Sabrina S.
last_name: Streipert
- first_name: Jerzy A.
full_name: Filar, Jerzy A.
last_name: Filar
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
citation:
ama: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. Mistakes can stabilise the
dynamics of rock-paper-scissors games. PLoS Computational Biology. 2021;17(4).
doi:10.1371/journal.pcbi.1008523
apa: Kleshnina, M., Streipert, S. S., Filar, J. A., & Chatterjee, K. (2021).
Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008523
chicago: Kleshnina, Maria, Sabrina S. Streipert, Jerzy A. Filar, and Krishnendu
Chatterjee. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.”
PLoS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008523.
ieee: M. Kleshnina, S. S. Streipert, J. A. Filar, and K. Chatterjee, “Mistakes can
stabilise the dynamics of rock-paper-scissors games,” PLoS Computational Biology,
vol. 17, no. 4. Public Library of Science, 2021.
ista: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. 2021. Mistakes can stabilise
the dynamics of rock-paper-scissors games. PLoS Computational Biology. 17(4),
e1008523.
mla: Kleshnina, Maria, et al. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors
Games.” PLoS Computational Biology, vol. 17, no. 4, e1008523, Public Library
of Science, 2021, doi:10.1371/journal.pcbi.1008523.
short: M. Kleshnina, S.S. Streipert, J.A. Filar, K. Chatterjee, PLoS Computational
Biology 17 (2021).
date_created: 2021-05-09T22:01:38Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2025-06-12T06:40:39Z
day: '01'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1008523
ec_funded: 1
external_id:
isi:
- '000639711200001'
pmid:
- '33844680'
file:
- access_level: open_access
checksum: a94ebe0c4116f5047eaa6029e54d2dac
content_type: application/pdf
creator: kschuh
date_created: 2021-05-11T13:50:06Z
date_updated: 2021-05-11T13:50:06Z
file_id: '9385'
file_name: 2021_pcbi_Kleshnina.pdf
file_size: 1323820
relation: main_file
success: 1
file_date_updated: 2021-05-11T13:50:06Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
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: Mistakes can stabilise the dynamics of rock-paper-scissors games
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: '9759'
acknowledgement: The authors thank Inez Lam of Johns Hopkins University for valuable
comments on an earlier version of the manuscript. We also thank the facilitators
of the 2019–2020 eLife Community Ambassador program.
article_number: e1009124
article_processing_charge: Yes
article_type: letter_note
author:
- first_name: Michael John
full_name: Bartlett, Michael John
last_name: Bartlett
- first_name: Feyza N
full_name: Arslan, Feyza N
id: 49DA7910-F248-11E8-B48F-1D18A9856A87
last_name: Arslan
orcid: 0000-0001-5809-9566
- first_name: Adriana
full_name: Bankston, Adriana
last_name: Bankston
- first_name: Sarvenaz
full_name: Sarabipour, Sarvenaz
last_name: Sarabipour
citation:
ama: Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. Ten simple rules to improve
academic work- life balance. PLoS Computational Biology. 2021;17(7). doi:10.1371/journal.pcbi.1009124
apa: Bartlett, M. J., Arslan, F. N., Bankston, A., & Sarabipour, S. (2021).
Ten simple rules to improve academic work- life balance. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009124
chicago: Bartlett, Michael John, Feyza N Arslan, Adriana Bankston, and Sarvenaz
Sarabipour. “Ten Simple Rules to Improve Academic Work- Life Balance.” PLoS
Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009124.
ieee: M. J. Bartlett, F. N. Arslan, A. Bankston, and S. Sarabipour, “Ten simple
rules to improve academic work- life balance,” PLoS Computational Biology,
vol. 17, no. 7. Public Library of Science, 2021.
ista: Bartlett MJ, Arslan FN, Bankston A, Sarabipour S. 2021. Ten simple rules to
improve academic work- life balance. PLoS Computational Biology. 17(7), e1009124.
mla: Bartlett, Michael John, et al. “Ten Simple Rules to Improve Academic Work-
Life Balance.” PLoS Computational Biology, vol. 17, no. 7, e1009124, Public
Library of Science, 2021, doi:10.1371/journal.pcbi.1009124.
short: M.J. Bartlett, F.N. Arslan, A. Bankston, S. Sarabipour, PLoS Computational
Biology 17 (2021).
date_created: 2021-08-01T22:01:21Z
date_published: 2021-07-15T00:00:00Z
date_updated: 2025-07-10T12:02:02Z
day: '15'
ddc:
- '613'
department:
- _id: CaHe
doi: 10.1371/journal.pcbi.1009124
external_id:
isi:
- '000677713500008'
pmid:
- '34264932'
file:
- access_level: open_access
checksum: e56d91f0eeadb36f143a90e2c1b3ab63
content_type: application/pdf
creator: cchlebak
date_created: 2021-08-05T12:06:49Z
date_updated: 2021-08-05T12:06:49Z
file_id: '9771'
file_name: 2021_PlosCompBio_Bartlett.pdf
file_size: 693633
relation: main_file
file_date_updated: 2021-08-05T12:06:49Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
issue: '7'
language:
- iso: eng
month: '07'
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
scopus_import: '1'
status: public
title: Ten simple rules to improve academic work- life balance
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: '8767'
abstract:
- lang: eng
text: Resources are rarely distributed uniformly within a population. Heterogeneity
in the concentration of a drug, the quality of breeding sites, or wealth can all
affect evolutionary dynamics. In this study, we represent a collection of properties
affecting the fitness at a given location using a color. A green node is rich
in resources while a red node is poorer. More colors can represent a broader spectrum
of resource qualities. For a population evolving according to the birth-death
Moran model, the first question we address is which structures, identified by
graph connectivity and graph coloring, are evolutionarily equivalent. We prove
that all properly two-colored, undirected, regular graphs are evolutionarily equivalent
(where “properly colored” means that no two neighbors have the same color). We
then compare the effects of background heterogeneity on properly two-colored graphs
to those with alternative schemes in which the colors are permuted. Finally, we
discuss dynamic coloring as a model for spatiotemporal resource fluctuations,
and we illustrate that random dynamic colorings often diminish the effects of
background heterogeneity relative to a proper two-coloring.
acknowledgement: 'We thank Igor Erovenko for many helpful comments on an earlier version
of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the
Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation
(A.M.). The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.'
article_number: e1008402
article_processing_charge: No
article_type: original
author:
- first_name: Kamran
full_name: Kaveh, Kamran
last_name: Kaveh
- first_name: Alex
full_name: McAvoy, Alex
last_name: McAvoy
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin A.
full_name: Nowak, Martin A.
last_name: Nowak
citation:
ama: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 2020;16(11). doi:10.1371/journal.pcbi.1008402
apa: Kaveh, K., McAvoy, A., Chatterjee, K., & Nowak, M. A. (2020). The Moran
process on 2-chromatic graphs. PLOS Computational Biology. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1008402
chicago: Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak.
“The Moran Process on 2-Chromatic Graphs.” PLOS Computational Biology.
Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1008402.
ieee: K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on
2-chromatic graphs,” PLOS Computational Biology, vol. 16, no. 11. Public
Library of Science, 2020.
ista: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 16(11), e1008402.
mla: Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” PLOS Computational
Biology, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:10.1371/journal.pcbi.1008402.
short: K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology
16 (2020).
date_created: 2020-11-18T07:20:23Z
date_published: 2020-11-05T00:00:00Z
date_updated: 2025-06-12T07:02:01Z
day: '05'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1008402
external_id:
isi:
- '000591317200004'
pmid:
- '33151935'
file:
- access_level: open_access
checksum: 555456dd0e47bcf9e0994bcb95577e88
content_type: application/pdf
creator: dernst
date_created: 2020-11-18T07:26:10Z
date_updated: 2020-11-18T07:26:10Z
file_id: '8768'
file_name: 2020_PlosCompBio_Kaveh.pdf
file_size: 2498594
relation: main_file
success: 1
file_date_updated: 2020-11-18T07:26:10Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '11'
keyword:
- Ecology
- Modelling and Simulation
- Computational Theory and Mathematics
- Genetics
- Ecology
- Evolution
- Behavior and Systematics
- Molecular Biology
- Cellular and Molecular Neuroscience
language:
- iso: eng
month: '11'
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: The Moran process on 2-chromatic graphs
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: 16
year: '2020'
...
---
_id: '7212'
abstract:
- lang: eng
text: The fixation probability of a single mutant invading a population of residents
is among the most widely-studied quantities in evolutionary dynamics. Amplifiers
of natural selection are population structures that increase the fixation probability
of advantageous mutants, compared to well-mixed populations. Extensive studies
have shown that many amplifiers exist for the Birth-death Moran process, some
of them substantially increasing the fixation probability or even guaranteeing
fixation in the limit of large population size. On the other hand, no amplifiers
are known for the death-Birth Moran process, and computer-assisted exhaustive
searches have failed to discover amplification. In this work we resolve this disparity,
by showing that any amplification under death-Birth updating is necessarily bounded
and transient. Our boundedness result states that even if a population structure
does amplify selection, the resulting fixation probability is close to that of
the well-mixed population. Our transience result states that for any population
structure there exists a threshold r⋆ such that the population structure ceases
to amplify selection if the mutant fitness advantage r is larger than r⋆. Finally,
we also extend the above results to δ-death-Birth updating, which is a combination
of Birth-death and death-Birth updating. On the positive side, we identify population
structures that maintain amplification for a wide range of values r and δ. These
results demonstrate that amplification of natural selection depends on the specific
mechanisms of the evolutionary process.
article_number: e1007494
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
- first_name: Andreas
full_name: Pavlogiannis, Andreas
id: 49704004-F248-11E8-B48F-1D18A9856A87
last_name: Pavlogiannis
orcid: 0000-0002-8943-0722
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin A.
full_name: Nowak, Martin A.
last_name: Nowak
citation:
ama: Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. Limits on amplifiers of
natural selection under death-Birth updating. PLoS computational biology.
2020;16. doi:10.1371/journal.pcbi.1007494
apa: Tkadlec, J., Pavlogiannis, A., Chatterjee, K., & Nowak, M. A. (2020). Limits
on amplifiers of natural selection under death-Birth updating. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1007494
chicago: Tkadlec, Josef, Andreas Pavlogiannis, Krishnendu Chatterjee, and Martin
A. Nowak. “Limits on Amplifiers of Natural Selection under Death-Birth Updating.”
PLoS Computational Biology. Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007494.
ieee: J. Tkadlec, A. Pavlogiannis, K. Chatterjee, and M. A. Nowak, “Limits on amplifiers
of natural selection under death-Birth updating,” PLoS computational biology,
vol. 16. Public Library of Science, 2020.
ista: Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. 2020. Limits on amplifiers
of natural selection under death-Birth updating. PLoS computational biology. 16,
e1007494.
mla: Tkadlec, Josef, et al. “Limits on Amplifiers of Natural Selection under Death-Birth
Updating.” PLoS Computational Biology, vol. 16, e1007494, Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007494.
short: J. Tkadlec, A. Pavlogiannis, K. Chatterjee, M.A. Nowak, PLoS Computational
Biology 16 (2020).
date_created: 2019-12-23T13:45:11Z
date_published: 2020-01-17T00:00:00Z
date_updated: 2026-04-16T08:32:38Z
day: '17'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1007494
ec_funded: 1
external_id:
arxiv:
- '1906.02785'
isi:
- '000510916500025'
file:
- access_level: open_access
checksum: ce32ee2d2f53aed832f78bbd47e882df
content_type: application/pdf
creator: dernst
date_created: 2020-02-03T07:32:42Z
date_updated: 2020-07-14T12:47:53Z
file_id: '7441'
file_name: 2020_PlosCompBio_Tkadlec.pdf
file_size: 1817531
relation: main_file
file_date_updated: 2020-07-14T12:47:53Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 2581B60A-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '279307'
name: 'Quantitative Graph Games: Theory and Applications'
- _id: 2584A770-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 23499-N23
name: Modern Graph Algorithmic Techniques in Formal Verification
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
publication: PLoS computational biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '7196'
relation: part_of_dissertation
status: public
scopus_import: '1'
status: public
title: Limits on amplifiers of natural selection under death-Birth updating
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 16
year: '2020'
...
---
_id: '6900'
abstract:
- lang: eng
text: Across diverse biological systems—ranging from neural networks to intracellular
signaling and genetic regulatory networks—the information about changes in the
environment is frequently encoded in the full temporal dynamics of the network
nodes. A pressing data-analysis challenge has thus been to efficiently estimate
the amount of information that these dynamics convey from experimental data. Here
we develop and evaluate decoding-based estimation methods to lower bound the mutual
information about a finite set of inputs, encoded in single-cell high-dimensional
time series data. For biological reaction networks governed by the chemical Master
equation, we derive model-based information approximations and analytical upper
bounds, against which we benchmark our proposed model-free decoding estimators.
In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based
estimators robustly extract a large fraction of the available information from
high-dimensional trajectories with a realistic number of data samples. We apply
these estimators to previously published data on Erk and Ca2+ signaling in mammalian
cells and to yeast stress-response, and find that substantial amount of information
about environmental state can be encoded by non-trivial response statistics even
in stationary signals. We argue that these single-cell, decoding-based information
estimates, rather than the commonly-used tests for significant differences between
selected population response statistics, provide a proper and unbiased measure
for the performance of biological signaling networks.
article_processing_charge: No
author:
- first_name: Sarah A
full_name: Cepeda Humerez, Sarah A
id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87
last_name: Cepeda Humerez
- first_name: Jakob
full_name: Ruess, Jakob
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Cepeda Humerez SA, Ruess J, Tkačik G. Estimating information in time-varying
signals. PLoS computational biology. 2019;15(9):e1007290. doi:10.1371/journal.pcbi.1007290
apa: Cepeda Humerez, S. A., Ruess, J., & Tkačik, G. (2019). Estimating information
in time-varying signals. PLoS Computational Biology. Public Library of
Science. https://doi.org/10.1371/journal.pcbi.1007290
chicago: Cepeda Humerez, Sarah A, Jakob Ruess, and Gašper Tkačik. “Estimating Information
in Time-Varying Signals.” PLoS Computational Biology. Public Library of
Science, 2019. https://doi.org/10.1371/journal.pcbi.1007290.
ieee: S. A. Cepeda Humerez, J. Ruess, and G. Tkačik, “Estimating information in
time-varying signals,” PLoS computational biology, vol. 15, no. 9. Public
Library of Science, p. e1007290, 2019.
ista: Cepeda Humerez SA, Ruess J, Tkačik G. 2019. Estimating information in time-varying
signals. PLoS computational biology. 15(9), e1007290.
mla: Cepeda Humerez, Sarah A., et al. “Estimating Information in Time-Varying Signals.”
PLoS Computational Biology, vol. 15, no. 9, Public Library of Science,
2019, p. e1007290, doi:10.1371/journal.pcbi.1007290.
short: S.A. Cepeda Humerez, J. Ruess, G. Tkačik, PLoS Computational Biology 15 (2019)
e1007290.
date_created: 2019-09-22T22:00:37Z
date_published: 2019-09-03T00:00:00Z
date_updated: 2026-04-16T08:37:39Z
day: '03'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007290
external_id:
isi:
- '000489741800021'
pmid:
- '31479447'
file:
- access_level: open_access
checksum: 81bdce1361c9aa8395d6fa635fb6ab47
content_type: application/pdf
creator: kschuh
date_created: 2019-10-01T10:53:45Z
date_updated: 2020-07-14T12:47:44Z
file_id: '6925'
file_name: 2019_PLoS_Cepeda-Humerez.pdf
file_size: 3081855
relation: main_file
file_date_updated: 2020-07-14T12:47:44Z
has_accepted_license: '1'
intvolume: ' 15'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: e1007290
pmid: 1
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLoS computational biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
record:
- id: '6473'
relation: part_of_dissertation
status: public
scopus_import: '1'
status: public
title: Estimating information in time-varying signals
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 15
year: '2019'
...
---
_id: '680'
abstract:
- lang: eng
text: In order to respond reliably to specific features of their environment, sensory
neurons need to integrate multiple incoming noisy signals. Crucially, they also
need to compete for the interpretation of those signals with other neurons representing
similar features. The form that this competition should take depends critically
on the noise corrupting these signals. In this study we show that for the type
of noise commonly observed in sensory systems, whose variance scales with the
mean signal, sensory neurons should selectively divide their input signals by
their predictions, suppressing ambiguous cues while amplifying others. Any change
in the stimulus context alters which inputs are suppressed, leading to a deep
dynamic reshaping of neural receptive fields going far beyond simple surround
suppression. Paradoxically, these highly variable receptive fields go alongside
and are in fact required for an invariant representation of external sensory features.
In addition to offering a normative account of context-dependent changes in sensory
responses, perceptual inference in the presence of signal-dependent noise accounts
for ubiquitous features of sensory neurons such as divisive normalization, gain
control and contrast dependent temporal dynamics.
article_number: e1005582
article_processing_charge: No
author:
- first_name: Matthew J
full_name: Chalk, Matthew J
id: 2BAAC544-F248-11E8-B48F-1D18A9856A87
last_name: Chalk
orcid: 0000-0001-7782-4436
- first_name: Paul
full_name: Masset, Paul
last_name: Masset
- first_name: Boris
full_name: Gutkin, Boris
last_name: Gutkin
- first_name: Sophie
full_name: Denève, Sophie
last_name: Denève
citation:
ama: Chalk MJ, Masset P, Gutkin B, Denève S. Sensory noise predicts divisive reshaping
of receptive fields. PLoS Computational Biology. 2017;13(6). doi:10.1371/journal.pcbi.1005582
apa: Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Sensory noise
predicts divisive reshaping of receptive fields. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582
chicago: Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Sensory
Noise Predicts Divisive Reshaping of Receptive Fields.” PLoS Computational
Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005582.
ieee: M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts
divisive reshaping of receptive fields,” PLoS Computational Biology, vol.
13, no. 6. Public Library of Science, 2017.
ista: Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Sensory noise predicts divisive
reshaping of receptive fields. PLoS Computational Biology. 13(6), e1005582.
mla: Chalk, Matthew J., et al. “Sensory Noise Predicts Divisive Reshaping of Receptive
Fields.” PLoS Computational Biology, vol. 13, no. 6, e1005582, Public Library
of Science, 2017, doi:10.1371/journal.pcbi.1005582.
short: M.J. Chalk, P. Masset, B. Gutkin, S. Denève, PLoS Computational Biology 13
(2017).
corr_author: '1'
date_created: 2018-12-11T11:47:53Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2025-09-10T14:20:48Z
day: '01'
ddc:
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005582
external_id:
isi:
- '000404565400034'
file:
- access_level: open_access
checksum: 796a1026076af6f4405a47d985bc7b68
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:07:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '4645'
file_name: IST-2017-898-v1+1_journal.pcbi.1005582.pdf
file_size: 14555676
relation: main_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
issue: '6'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
publist_id: '7035'
pubrep_id: '898'
quality_controlled: '1'
related_material:
record:
- id: '9855'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Sensory noise predicts divisive reshaping of receptive fields
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2017'
...
---
_id: '720'
abstract:
- lang: eng
text: 'Advances in multi-unit recordings pave the way for statistical modeling of
activity patterns in large neural populations. Recent studies have shown that
the summed activity of all neurons strongly shapes the population response. A
separate recent finding has been that neural populations also exhibit criticality,
an anomalously large dynamic range for the probabilities of different population
activity patterns. Motivated by these two observations, we introduce a class of
probabilistic models which takes into account the prior knowledge that the neural
population could be globally coupled and close to critical. These models consist
of an energy function which parametrizes interactions between small groups of
neurons, and an arbitrary positive, strictly increasing, and twice differentiable
function which maps the energy of a population pattern to its probability. We
show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an
accurate description of the activity of retinal ganglion cells which outperforms
previous models based on the summed activity of neurons; 2) prior knowledge that
the population is critical translates to prior expectations about the shape of
the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous
latent variable globally coupling the system whose distribution we can infer from
data. Our method is independent of the underlying system’s state space; hence,
it can be applied to other systems such as natural scenes or amino acid sequences
of proteins which are also known to exhibit criticality.'
article_number: e1005763
article_processing_charge: Yes
author:
- first_name: Jan
full_name: Humplik, Jan
id: 2E9627A8-F248-11E8-B48F-1D18A9856A87
last_name: Humplik
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally
capture global coupling and criticality. PLoS Computational Biology. 2017;13(9).
doi:10.1371/journal.pcbi.1005763
apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations
that naturally capture global coupling and criticality. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763
chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763.
ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that
naturally capture global coupling and criticality,” PLoS Computational Biology,
vol. 13, no. 9. Public Library of Science, 2017.
ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that
naturally capture global coupling and criticality. PLoS Computational Biology.
13(9), e1005763.
mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763.
short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).
corr_author: '1'
date_created: 2018-12-11T11:48:08Z
date_published: 2017-09-19T00:00:00Z
date_updated: 2025-09-10T10:58:42Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005763
external_id:
isi:
- '000411981000042'
file:
- access_level: open_access
checksum: 81107096c19771c36ddbe6f0282a3acb
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:30Z
date_updated: 2020-07-14T12:47:53Z
file_id: '5352'
file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf
file_size: 14167050
relation: main_file
file_date_updated: 2020-07-14T12:47:53Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
publist_id: '6960'
pubrep_id: '884'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Probabilistic models for neural populations that naturally capture global coupling
and criticality
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2017'
...
---
_id: '696'
abstract:
- lang: eng
text: Mutator strains are expected to evolve when the availability and effect of
beneficial mutations are high enough to counteract the disadvantage from deleterious
mutations that will inevitably accumulate. As the population becomes more adapted
to its environment, both availability and effect of beneficial mutations necessarily
decrease and mutation rates are predicted to decrease. It has been shown that
certain molecular mechanisms can lead to increased mutation rates when the organism
finds itself in a stressful environment. While this may be a correlated response
to other functions, it could also be an adaptive mechanism, raising mutation rates
only when it is most advantageous. Here, we use a mathematical model to investigate
the plausibility of the adaptive hypothesis. We show that such a mechanism can
be mantained if the population is subjected to diverse stresses. By simulating
various antibiotic treatment schemes, we find that combination treatments can
reduce the effectiveness of second-order selection on stress-induced mutagenesis.
We discuss the implications of our results to strategies of antibiotic therapy.
article_number: e1005609
article_processing_charge: No
article_type: original
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
orcid: 0000-0002-2519-824X
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: 'Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity
facilitates the persistence of mutator genes. PLoS Computational Biology.
2017;13(7). doi:10.1371/journal.pcbi.1005609'
apa: 'Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis:
Stress diversity facilitates the persistence of mutator genes. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609'
chicago: 'Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced
Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS
Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.'
ieee: 'M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes,” PLoS Computational
Biology, vol. 13, no. 7. Public Library of Science, 2017.'
ista: 'Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress
diversity facilitates the persistence of mutator genes. PLoS Computational Biology.
13(7), e1005609.'
mla: 'Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity
Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology,
vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.'
short: M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).
corr_author: '1'
date_created: 2018-12-11T11:47:58Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2026-06-20T22:30:53Z
day: '18'
ddc:
- '576'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609
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name: Speed of Adaptation in Population Genetics and Evolutionary Computation
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status: public
title: 'Stress induced mutagenesis: Stress diversity facilitates the persistence of
mutator genes'
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legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
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type: journal_article
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year: '2017'
...
---
_id: '2257'
abstract:
- lang: eng
text: 'Maximum entropy models are the least structured probability distributions
that exactly reproduce a chosen set of statistics measured in an interacting network.
Here we use this principle to construct probabilistic models which describe the
correlated spiking activity of populations of up to 120 neurons in the salamander
retina as it responds to natural movies. Already in groups as small as 10 neurons,
interactions between spikes can no longer be regarded as small perturbations in
an otherwise independent system; for 40 or more neurons pairwise interactions
need to be supplemented by a global interaction that controls the distribution
of synchrony in the population. Here we show that such “K-pairwise” models—being
systematic extensions of the previously used pairwise Ising models—provide an
excellent account of the data. We explore the properties of the neural vocabulary
by: 1) estimating its entropy, which constrains the population''s capacity to
represent visual information; 2) classifying activity patterns into a small set
of metastable collective modes; 3) showing that the neural codeword ensembles
are extremely inhomogenous; 4) demonstrating that the state of individual neurons
is highly predictable from the rest of the population, allowing the capacity for
error correction.'
acknowledgement: 'This work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573,
NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie
and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima
and the French State program “Investissements d''Avenir” [LIFESENSES: ANR-10-LABX-65],
and the Austrian Research Foundation FWF P25651.'
article_number: e1003408
article_processing_charge: No
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
- first_name: William
full_name: Bialek, William
last_name: Bialek
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for
collective behavior in a large network of sensory neurons. PLoS Computational
Biology. 2014;10(1). doi:10.1371/journal.pcbi.1003408
apa: Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., & Berry,
M. (2014). Searching for collective behavior in a large network of sensory neurons.
PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003408
chicago: Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek,
and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory
Neurons.” PLoS Computational Biology. Public Library of Science, 2014.
https://doi.org/10.1371/journal.pcbi.1003408.
ieee: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching
for collective behavior in a large network of sensory neurons,” PLoS Computational
Biology, vol. 10, no. 1. Public Library of Science, 2014.
ista: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching
for collective behavior in a large network of sensory neurons. PLoS Computational
Biology. 10(1), e1003408.
mla: Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network
of Sensory Neurons.” PLoS Computational Biology, vol. 10, no. 1, e1003408,
Public Library of Science, 2014, doi:10.1371/journal.pcbi.1003408.
short: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS
Computational Biology 10 (2014).
corr_author: '1'
date_created: 2018-12-11T11:56:36Z
date_published: 2014-01-02T00:00:00Z
date_updated: 2025-09-29T11:14:06Z
day: '02'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1003408
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date_updated: 2020-07-14T12:45:35Z
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file_name: IST-2016-436-v1+1_journal.pcbi.1003408.pdf
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language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
publist_id: '4689'
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title: Searching for collective behavior in a large network of sensory neurons
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 10
year: '2014'
...