---
_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: 2023-08-08T13:31:08Z
day: '01'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1008523
ec_funded: 1
external_id:
isi:
- '000639711200001'
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
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
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:
- '15537358'
issn:
- 1553734X
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
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: 2023-08-10T14:16:46Z
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:
- '15537358'
issn:
- 1553734X
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 17
year: '2021'
...
---
_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).
date_created: 2018-12-11T11:48:08Z
date_published: 2017-09-19T00:00:00Z
date_updated: 2021-01-12T08:12:21Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005763
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'
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:
- 1553734X
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2017'
...
---
_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
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).
date_created: 2018-12-11T11:47:53Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2023-02-23T14:10:54Z
day: '01'
ddc:
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005582
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'
issue: '6'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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_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).
date_created: 2018-12-11T11:47:58Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2024-03-27T23:30:28Z
day: '18'
ddc:
- '576'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609
ec_funded: 1
file:
- access_level: open_access
checksum: 9143c290fa6458ed2563bff4b295554a
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:01Z
date_updated: 2020-07-14T12:47:46Z
file_id: '5117'
file_name: IST-2017-894-v1+1_journal.pcbi.1005609.pdf
file_size: 3775716
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '7004'
pubrep_id: '894'
quality_controlled: '1'
related_material:
record:
- id: '9849'
relation: research_data
status: public
- id: '9850'
relation: research_data
status: public
- id: '9851'
relation: research_data
status: public
- id: '9852'
relation: research_data
status: public
- id: '6263'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: 'Stress induced mutagenesis: Stress diversity facilitates the persistence of
mutator genes'
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: 13
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: "\r\n\r\n\r\n\r\nThis 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
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).
date_created: 2018-12-11T11:56:36Z
date_published: 2014-01-02T00:00:00Z
date_updated: 2024-02-21T13:46:14Z
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doi: 10.1371/journal.pcbi.1003408
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title: Searching for collective behavior in a large network of sensory neurons
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