---
_id: '1931'
abstract:
- lang: eng
text: A wealth of experimental evidence suggests that working memory circuits preferentially
represent information that is behaviorally relevant. Still, we are missing a mechanistic
account of how these representations come about. Here we provide a simple explanation
for a range of experimental findings, in light of prefrontal circuits adapting
to task constraints by reward-dependent learning. In particular, we model a neural
network shaped by reward-modulated spike-timing dependent plasticity (r-STDP)
and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show
that the experimentally-observed neural representations naturally emerge in an
initially unstructured circuit as it learns to solve several working memory tasks.
These results point to a critical, and previously unappreciated, role for reward-dependent
learning in shaping prefrontal cortex activity.
acknowledgement: Supported in part by EC MEXT project PLICON and the LOEWE-Program
“Neuronal Coordination Research Focus Frankfurt” (NeFF). Jochen Triesch was supported
by the Quandt foundation.
article_number: '57'
author:
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Jochen
full_name: Triesch, Jochen
last_name: Triesch
citation:
ama: Savin C, Triesch J. Emergence of task-dependent representations in working
memory circuits. Frontiers in Computational Neuroscience. 2014;8(MAY).
doi:10.3389/fncom.2014.00057
apa: Savin, C., & Triesch, J. (2014). Emergence of task-dependent representations
in working memory circuits. Frontiers in Computational Neuroscience. Frontiers
Research Foundation. https://doi.org/10.3389/fncom.2014.00057
chicago: Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations
in Working Memory Circuits.” Frontiers in Computational Neuroscience. Frontiers
Research Foundation, 2014. https://doi.org/10.3389/fncom.2014.00057.
ieee: C. Savin and J. Triesch, “Emergence of task-dependent representations in working
memory circuits,” Frontiers in Computational Neuroscience, vol. 8, no.
MAY. Frontiers Research Foundation, 2014.
ista: Savin C, Triesch J. 2014. Emergence of task-dependent representations in working
memory circuits. Frontiers in Computational Neuroscience. 8(MAY), 57.
mla: Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations
in Working Memory Circuits.” Frontiers in Computational Neuroscience, vol.
8, no. MAY, 57, Frontiers Research Foundation, 2014, doi:10.3389/fncom.2014.00057.
short: C. Savin, J. Triesch, Frontiers in Computational Neuroscience 8 (2014).
date_created: 2018-12-11T11:54:46Z
date_published: 2014-05-28T00:00:00Z
date_updated: 2021-01-12T06:54:09Z
day: '28'
department:
- _id: GaTk
doi: 10.3389/fncom.2014.00057
intvolume: ' 8'
issue: MAY
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035833/
month: '05'
oa: 1
oa_version: Submitted Version
publication: Frontiers in Computational Neuroscience
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '5163'
quality_controlled: '1'
scopus_import: 1
status: public
title: Emergence of task-dependent representations in working memory circuits
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 8
year: '2014'
...
---
_id: '2028'
abstract:
- lang: eng
text: 'Understanding the dynamics of noisy neurons remains an important challenge
in neuroscience. Here, we describe a simple probabilistic model that accurately
describes the firing behavior in a large class (type II) of neurons. To demonstrate
the usefulness of this model, we show how it accurately predicts the interspike
interval (ISI) distributions, bursting patterns and mean firing rates found by:
(1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental
data from squid giant axon with a noisy input current and (3) experimental data
on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple
model has 6 parameters, however, in some cases, two of these parameters are coupled
and only 5 parameters account for much of the known behavior. From these parameters,
many properties of spiking can be found through simple calculation. Thus, we show
how the complex effects of noise can be understood through a simple and general
probabilistic model.'
acknowledgement: 'This work is supported by AFOSR grant FA 9550-11-1-0165, program
grant RPG 24/2012 from the Human Frontiers of Science (DBF) and travel support from
the European Commission Marie Curie International Reintegration Grant PIRG04-GA-2008-239429
(KB). DP was supported by NIHR01 GM104987 and the Wyss Institute of Biologically
Inspired Engineering. '
article_processing_charge: No
author:
- first_name: Katarina
full_name: Bodova, Katarina
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bodova
orcid: 0000-0002-7214-0171
- first_name: David
full_name: Paydarfar, David
last_name: Paydarfar
- first_name: Daniel
full_name: Forger, Daniel
last_name: Forger
citation:
ama: Bodova K, Paydarfar D, Forger D. Characterizing spiking in noisy type II neurons.
Journal of Theoretical Biology. 2014;365:40-54. doi:10.1016/j.jtbi.2014.09.041
apa: Bodova, K., Paydarfar, D., & Forger, D. (2014). Characterizing spiking
in noisy type II neurons. Journal of Theoretical Biology. Academic Press.
https://doi.org/10.1016/j.jtbi.2014.09.041
chicago: Bodova, Katarina, David Paydarfar, and Daniel Forger. “Characterizing Spiking
in Noisy Type II Neurons.” Journal of Theoretical Biology. Academic Press,
2014. https://doi.org/10.1016/j.jtbi.2014.09.041.
ieee: K. Bodova, D. Paydarfar, and D. Forger, “Characterizing spiking in noisy type
II neurons,” Journal of Theoretical Biology, vol. 365. Academic Press,
pp. 40–54, 2014.
ista: Bodova K, Paydarfar D, Forger D. 2014. Characterizing spiking in noisy type
II neurons. Journal of Theoretical Biology. 365, 40–54.
mla: Bodova, Katarina, et al. “Characterizing Spiking in Noisy Type II Neurons.”
Journal of Theoretical Biology, vol. 365, Academic Press, 2014, pp. 40–54,
doi:10.1016/j.jtbi.2014.09.041.
short: K. Bodova, D. Paydarfar, D. Forger, Journal of Theoretical Biology 365 (2014)
40–54.
date_created: 2018-12-11T11:55:18Z
date_published: 2014-10-12T00:00:00Z
date_updated: 2022-08-25T14:00:47Z
day: '12'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1016/j.jtbi.2014.09.041
file:
- access_level: open_access
checksum: a9dbae18d3233b3dab6944fd3f2cd49e
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:17:58Z
date_updated: 2020-07-14T12:45:25Z
file_id: '5316'
file_name: IST-2016-444-v1+1_1-s2.0-S0022519314005888-main.pdf
file_size: 2679222
relation: main_file
file_date_updated: 2020-07-14T12:45:25Z
has_accepted_license: '1'
intvolume: ' 365'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '10'
oa: 1
oa_version: Published Version
page: 40 - 54
publication: ' Journal of Theoretical Biology'
publication_status: published
publisher: Academic Press
publist_id: '5043'
pubrep_id: '444'
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1016/j.jtbi.2015.03.013
scopus_import: '1'
status: public
title: Characterizing spiking in noisy type II neurons
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 365
year: '2014'
...
---
_id: '2183'
abstract:
- lang: eng
text: 'We describe a simple adaptive network of coupled chaotic maps. The network
reaches a stationary state (frozen topology) for all values of the coupling parameter,
although the dynamics of the maps at the nodes of the network can be nontrivial.
The structure of the network shows interesting hierarchical properties and in
certain parameter regions the dynamics is polysynchronous: Nodes can be divided
in differently synchronized classes but, contrary to cluster synchronization,
nodes in the same class need not be connected to each other. These complicated
synchrony patterns have been conjectured to play roles in systems biology and
circuits. The adaptive system we study describes ways whereby this behavior can
evolve from undifferentiated nodes.'
acknowledgement: "V.B.S. is partially supported by contract MEC (Grant No. AYA2010-22111-C03-02).\r\n"
article_number: '062809'
article_processing_charge: No
author:
- first_name: Vicente
full_name: Botella Soler, Vicente
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: Paul
full_name: Glendinning, Paul
last_name: Glendinning
citation:
ama: Botella Soler V, Glendinning P. Hierarchy and polysynchrony in an adaptive
network . Physical Review E Statistical Nonlinear and Soft Matter Physics.
2014;89(6). doi:10.1103/PhysRevE.89.062809
apa: Botella Soler, V., & Glendinning, P. (2014). Hierarchy and polysynchrony
in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter
Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.89.062809
chicago: Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony
in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft
Matter Physics. American Institute of Physics, 2014. https://doi.org/10.1103/PhysRevE.89.062809.
ieee: V. Botella Soler and P. Glendinning, “Hierarchy and polysynchrony in an adaptive
network ,” Physical Review E Statistical Nonlinear and Soft Matter Physics,
vol. 89, no. 6. American Institute of Physics, 2014.
ista: Botella Soler V, Glendinning P. 2014. Hierarchy and polysynchrony in an adaptive
network . Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(6),
062809.
mla: Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony
in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft
Matter Physics, vol. 89, no. 6, 062809, American Institute of Physics, 2014,
doi:10.1103/PhysRevE.89.062809.
short: V. Botella Soler, P. Glendinning, Physical Review E Statistical Nonlinear
and Soft Matter Physics 89 (2014).
date_created: 2018-12-11T11:56:11Z
date_published: 2014-06-16T00:00:00Z
date_updated: 2022-08-25T14:04:45Z
day: '16'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.89.062809
ec_funded: 1
intvolume: ' 89'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1403.3209
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '4798'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Hierarchy and polysynchrony in an adaptive network '
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 89
year: '2014'
...
---
_id: '2231'
abstract:
- lang: eng
text: Based on the measurements of noise in gene expression performed during the
past decade, it has become customary to think of gene regulation in terms of a
two-state model, where the promoter of a gene can stochastically switch between
an ON and an OFF state. As experiments are becoming increasingly precise and the
deviations from the two-state model start to be observable, we ask about the experimental
signatures of complex multistate promoters, as well as the functional consequences
of this additional complexity. In detail, we i), extend the calculations for noise
in gene expression to promoters described by state transition diagrams with multiple
states, ii), systematically compute the experimentally accessible noise characteristics
for these complex promoters, and iii), use information theory to evaluate the
channel capacities of complex promoter architectures and compare them with the
baseline provided by the two-state model. We find that adding internal states
to the promoter generically decreases channel capacity, except in certain cases,
three of which (cooperativity, dual-role regulation, promoter cycling) we analyze
in detail.
author:
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Rieckh G, Tkačik G. Noise and information transmission in promoters with multiple
internal states. Biophysical Journal. 2014;106(5):1194-1204. doi:10.1016/j.bpj.2014.01.014
apa: Rieckh, G., & Tkačik, G. (2014). Noise and information transmission in
promoters with multiple internal states. Biophysical Journal. Biophysical
Society. https://doi.org/10.1016/j.bpj.2014.01.014
chicago: Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in
Promoters with Multiple Internal States.” Biophysical Journal. Biophysical
Society, 2014. https://doi.org/10.1016/j.bpj.2014.01.014.
ieee: G. Rieckh and G. Tkačik, “Noise and information transmission in promoters
with multiple internal states,” Biophysical Journal, vol. 106, no. 5. Biophysical
Society, pp. 1194–1204, 2014.
ista: Rieckh G, Tkačik G. 2014. Noise and information transmission in promoters
with multiple internal states. Biophysical Journal. 106(5), 1194–1204.
mla: Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters
with Multiple Internal States.” Biophysical Journal, vol. 106, no. 5, Biophysical
Society, 2014, pp. 1194–204, doi:10.1016/j.bpj.2014.01.014.
short: G. Rieckh, G. Tkačik, Biophysical Journal 106 (2014) 1194–1204.
date_created: 2018-12-11T11:56:28Z
date_published: 2014-03-04T00:00:00Z
date_updated: 2021-01-12T06:56:10Z
day: '04'
department:
- _id: GaTk
doi: 10.1016/j.bpj.2014.01.014
external_id:
pmid:
- '24606943'
intvolume: ' 106'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4026790/
month: '03'
oa: 1
oa_version: Submitted Version
page: 1194 - 1204
pmid: 1
publication: Biophysical Journal
publication_identifier:
issn:
- '00063495'
publication_status: published
publisher: Biophysical Society
publist_id: '4730'
quality_controlled: '1'
scopus_import: 1
status: public
title: Noise and information transmission in promoters with multiple internal states
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 106
year: '2014'
...
---
_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
day: '02'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1003408
file:
- access_level: open_access
checksum: c720222c5e924a4acb17f23b9381a6ca
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:46Z
date_updated: 2020-07-14T12:45:35Z
file_id: '4965'
file_name: IST-2016-436-v1+1_journal.pcbi.1003408.pdf
file_size: 2194790
relation: main_file
file_date_updated: 2020-07-14T12:45:35Z
has_accepted_license: '1'
intvolume: ' 10'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://repository.ist.ac.at/id/eprint/436
month: '01'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '4689'
pubrep_id: '436'
quality_controlled: '1'
related_material:
record:
- id: '5562'
relation: popular_science
status: public
scopus_import: 1
status: public
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: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 10
year: '2014'
...
---
_id: '537'
abstract:
- lang: eng
text: Transgenerational effects are broader than only parental relationships. Despite
mounting evidence that multigenerational effects alter phenotypic and life-history
traits, our understanding of how they combine to determine fitness is not well
developed because of the added complexity necessary to study them. Here, we derive
a quantitative genetic model of adaptation to an extraordinary new environment
by an additive genetic component, phenotypic plasticity, maternal and grandmaternal
effects. We show how, at equilibrium, negative maternal and negative grandmaternal
effects maximize expected population mean fitness. We define negative transgenerational
effects as those that have a negative effect on trait expression in the subsequent
generation, that is, they slow, or potentially reverse, the expected evolutionary
dynamic. When maternal effects are positive, negative grandmaternal effects are
preferred. As expected under Mendelian inheritance, the grandmaternal effects
have a lower impact on fitness than the maternal effects, but this dual inheritance
model predicts a more complex relationship between maternal and grandmaternal
effects to constrain phenotypic variance and so maximize expected population mean
fitness in the offspring.
author:
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Thomas
full_name: Ezard, Thomas
last_name: Ezard
- first_name: Rebecca
full_name: Hoyle, Rebecca
last_name: Hoyle
citation:
ama: Prizak R, Ezard T, Hoyle R. Fitness consequences of maternal and grandmaternal
effects. Ecology and Evolution. 2014;4(15):3139-3145. doi:10.1002/ece3.1150
apa: Prizak, R., Ezard, T., & Hoyle, R. (2014). Fitness consequences of maternal
and grandmaternal effects. Ecology and Evolution. Wiley-Blackwell. https://doi.org/10.1002/ece3.1150
chicago: Prizak, Roshan, Thomas Ezard, and Rebecca Hoyle. “Fitness Consequences
of Maternal and Grandmaternal Effects.” Ecology and Evolution. Wiley-Blackwell,
2014. https://doi.org/10.1002/ece3.1150.
ieee: R. Prizak, T. Ezard, and R. Hoyle, “Fitness consequences of maternal and grandmaternal
effects,” Ecology and Evolution, vol. 4, no. 15. Wiley-Blackwell, pp. 3139–3145,
2014.
ista: Prizak R, Ezard T, Hoyle R. 2014. Fitness consequences of maternal and grandmaternal
effects. Ecology and Evolution. 4(15), 3139–3145.
mla: Prizak, Roshan, et al. “Fitness Consequences of Maternal and Grandmaternal
Effects.” Ecology and Evolution, vol. 4, no. 15, Wiley-Blackwell, 2014,
pp. 3139–45, doi:10.1002/ece3.1150.
short: R. Prizak, T. Ezard, R. Hoyle, Ecology and Evolution 4 (2014) 3139–3145.
date_created: 2018-12-11T11:47:02Z
date_published: 2014-07-19T00:00:00Z
date_updated: 2021-01-12T08:01:30Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1002/ece3.1150
file:
- access_level: open_access
checksum: e32abf75a248e7a11811fd7f60858769
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:11:31Z
date_updated: 2020-07-14T12:46:38Z
file_id: '4886'
file_name: IST-2018-934-v1+1_Prizak_et_al-2014-Ecology_and_Evolution.pdf
file_size: 621582
relation: main_file
file_date_updated: 2020-07-14T12:46:38Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '15'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 3139 - 3145
publication: Ecology and Evolution
publication_status: published
publisher: Wiley-Blackwell
publist_id: '7280'
pubrep_id: '934'
scopus_import: 1
status: public
title: Fitness consequences of maternal and grandmaternal effects
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2014'
...
---
_id: '9752'
abstract:
- lang: eng
text: Redundancies and correlations in the responses of sensory neurons may seem
to waste neural resources, but they can also carry cues about structured stimuli
and may help the brain to correct for response errors. To investigate the effect
of stimulus structure on redundancy in retina, we measured simultaneous responses
from populations of retinal ganglion cells presented with natural and artificial
stimuli that varied greatly in correlation structure; these stimuli and recordings
are publicly available online. Responding to spatio-temporally structured stimuli
such as natural movies, pairs of ganglion cells were modestly more correlated
than in response to white noise checkerboards, but they were much less correlated
than predicted by a non-adapting functional model of retinal response. Meanwhile,
responding to stimuli with purely spatial correlations, pairs of ganglion cells
showed increased correlations consistent with a static, non-adapting receptive
field and nonlinearity. We found that in response to spatio-temporally correlated
stimuli, ganglion cells had faster temporal kernels and tended to have stronger
surrounds. These properties of individual cells, along with gain changes that
opposed changes in effective contrast at the ganglion cell input, largely explained
the pattern of pairwise correlations across stimuli where receptive field measurements
were possible.
article_processing_charge: No
author:
- first_name: Kristina
full_name: Simmons, Kristina
last_name: Simmons
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Jan
full_name: Homann, Jan
last_name: Homann
- first_name: Heather
full_name: Yee, Heather
last_name: Yee
- first_name: Stephanie
full_name: Palmer, Stephanie
last_name: Palmer
- first_name: Philip
full_name: Nelson, Philip
last_name: Nelson
- first_name: Vijay
full_name: Balasubramanian, Vijay
last_name: Balasubramanian
citation:
ama: 'Simmons K, Prentice J, Tkačik G, et al. Data from: Transformation of stimulus
correlations by the retina. 2014. doi:10.5061/dryad.246qg'
apa: 'Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., …
Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations
by the retina. Dryad. https://doi.org/10.5061/dryad.246qg'
chicago: 'Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather
Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Data from: Transformation
of Stimulus Correlations by the Retina.” Dryad, 2014. https://doi.org/10.5061/dryad.246qg.'
ieee: 'K. Simmons et al., “Data from: Transformation of stimulus correlations
by the retina.” Dryad, 2014.'
ista: 'Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian
V. 2014. Data from: Transformation of stimulus correlations by the retina, Dryad,
10.5061/dryad.246qg.'
mla: 'Simmons, Kristina, et al. Data from: Transformation of Stimulus Correlations
by the Retina. Dryad, 2014, doi:10.5061/dryad.246qg.'
short: K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson,
V. Balasubramanian, (2014).
date_created: 2021-07-30T08:13:52Z
date_published: 2014-11-07T00:00:00Z
date_updated: 2023-02-23T10:35:57Z
day: '07'
department:
- _id: GaTk
doi: 10.5061/dryad.246qg
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.246qg
month: '11'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '2277'
relation: used_in_publication
status: public
status: public
title: 'Data from: Transformation of stimulus correlations by the retina'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2014'
...
---
_id: '2818'
abstract:
- lang: eng
text: Models of neural responses to stimuli with complex spatiotemporal correlation
structure often assume that neurons are selective for only a small number of linear
projections of a potentially high-dimensional input. In this review, we explore
recent modeling approaches where the neural response depends on the quadratic
form of the input rather than on its linear projection, that is, the neuron is
sensitive to the local covariance structure of the signal preceding the spike.
To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic)
stimulus distribution, we review several inference methods, focusing in particular
on two information theory–based approaches (maximization of stimulus energy and
of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered
covariance and extensions of generalized linear models). We analyze the formal
relationship between the likelihood-based and information-based approaches to
demonstrate how they lead to consistent inference. We demonstrate the practical
feasibility of these procedures by using model neurons responding to a flickering
variance stimulus.
arxiv: 1
author:
- first_name: Kanaka
full_name: Rajan, Kanaka
last_name: Rajan
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Rajan K, Marre O, Tkačik G. Learning quadratic receptive fields from neural
responses to natural stimuli. Neural Computation. 2013;25(7):1661-1692.
doi:10.1162/NECO_a_00463
apa: Rajan, K., Marre, O., & Tkačik, G. (2013). Learning quadratic receptive
fields from neural responses to natural stimuli. Neural Computation. MIT
Press . https://doi.org/10.1162/NECO_a_00463
chicago: Rajan, Kanaka, Olivier Marre, and Gašper Tkačik. “Learning Quadratic Receptive
Fields from Neural Responses to Natural Stimuli.” Neural Computation. MIT
Press , 2013. https://doi.org/10.1162/NECO_a_00463.
ieee: K. Rajan, O. Marre, and G. Tkačik, “Learning quadratic receptive fields from
neural responses to natural stimuli,” Neural Computation, vol. 25, no.
7. MIT Press , pp. 1661–1692, 2013.
ista: Rajan K, Marre O, Tkačik G. 2013. Learning quadratic receptive fields from
neural responses to natural stimuli. Neural Computation. 25(7), 1661–1692.
mla: Rajan, Kanaka, et al. “Learning Quadratic Receptive Fields from Neural Responses
to Natural Stimuli.” Neural Computation, vol. 25, no. 7, MIT Press , 2013,
pp. 1661–92, doi:10.1162/NECO_a_00463.
short: K. Rajan, O. Marre, G. Tkačik, Neural Computation 25 (2013) 1661–1692.
date_created: 2018-12-11T11:59:45Z
date_published: 2013-07-01T00:00:00Z
date_updated: 2021-01-12T06:59:56Z
day: '01'
department:
- _id: GaTk
doi: 10.1162/NECO_a_00463
external_id:
arxiv:
- '1209.0121'
intvolume: ' 25'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1209.0121
month: '07'
oa: 1
oa_version: Preprint
page: 1661 - 1692
publication: Neural Computation
publication_status: published
publisher: 'MIT Press '
publist_id: '3983'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning quadratic receptive fields from neural responses to natural stimuli
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2013'
...
---
_id: '2850'
abstract:
- lang: eng
text: "Recent work emphasizes that the maximum entropy principle provides a bridge
between statistical mechanics models for collective behavior in neural networks
and experiments on networks of real neurons. Most of this work has focused on
capturing the measured correlations among pairs of neurons. Here we suggest an
alternative, constructing models that are consistent with the distribution of
global network activity, i.e. the probability that K out of N cells in the network
generate action potentials in the same small time bin. The inverse problem that
we need to solve in constructing the model is analytically tractable, and provides
a natural 'thermodynamics' for the network in the limit of large N. We analyze
the responses of neurons in a small patch of the retina to naturalistic stimuli,
and find that the implied thermodynamics is very close to an unusual critical
point, in which the entropy (in proper units) is exactly equal to the energy.
© 2013 IOP Publishing Ltd and SISSA Medialab srl.\r\n"
acknowledgement: "his work was supported in part by NSF Grants IIS-0613435 and PHY-0957573,
by NIH Grants R01 EY14196 and P50 GM071508, by the Fannie and John Hertz Foundation,
by the Human Frontiers Science Program, by the Swartz Foundation, and by the WM
Keck Foundation.\r\n"
article_number: P03011
article_processing_charge: No
article_type: original
arxiv: 1
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: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. The simplest maximum
entropy model for collective behavior in a neural network. Journal of Statistical
Mechanics Theory and Experiment. 2013;2013(3). doi:10.1088/1742-5468/2013/03/P03011
apa: Tkačik, G., Marre, O., Mora, T., Amodei, D., Berry, M., & Bialek, W. (2013).
The simplest maximum entropy model for collective behavior in a neural network.
Journal of Statistical Mechanics Theory and Experiment. IOP Publishing
Ltd. https://doi.org/10.1088/1742-5468/2013/03/P03011
chicago: Tkačik, Gašper, Olivier Marre, Thierry Mora, Dario Amodei, Michael Berry,
and William Bialek. “The Simplest Maximum Entropy Model for Collective Behavior
in a Neural Network.” Journal of Statistical Mechanics Theory and Experiment.
IOP Publishing Ltd., 2013. https://doi.org/10.1088/1742-5468/2013/03/P03011.
ieee: G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, and W. Bialek, “The simplest
maximum entropy model for collective behavior in a neural network,” Journal
of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing
Ltd., 2013.
ista: Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. 2013. The simplest
maximum entropy model for collective behavior in a neural network. Journal of
Statistical Mechanics Theory and Experiment. 2013(3), P03011.
mla: Tkačik, Gašper, et al. “The Simplest Maximum Entropy Model for Collective Behavior
in a Neural Network.” Journal of Statistical Mechanics Theory and Experiment,
vol. 2013, no. 3, P03011, IOP Publishing Ltd., 2013, doi:10.1088/1742-5468/2013/03/P03011.
short: G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek, Journal of
Statistical Mechanics Theory and Experiment 2013 (2013).
date_created: 2018-12-11T11:59:55Z
date_published: 2013-03-12T00:00:00Z
date_updated: 2021-01-12T07:00:14Z
day: '12'
department:
- _id: GaTk
doi: 10.1088/1742-5468/2013/03/P03011
external_id:
arxiv:
- '1207.6319'
intvolume: ' 2013'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1207.6319
month: '03'
oa: 1
oa_version: Preprint
publication: Journal of Statistical Mechanics Theory and Experiment
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '3942'
quality_controlled: '1'
scopus_import: 1
status: public
title: The simplest maximum entropy model for collective behavior in a neural network
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2013
year: '2013'
...
---
_id: '2851'
abstract:
- lang: eng
text: The number of possible activity patterns in a population of neurons grows
exponentially with the size of the population. Typical experiments explore only
a tiny fraction of the large space of possible activity patterns in the case of
populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled
regime, to estimate the probabilities with which most of the activity patterns
occur. As a result, the corresponding entropy - which is a measure of the computational
power of the neural population - cannot be estimated directly. We propose a simple
scheme for estimating the entropy in the undersampled regime, which bounds its
value from both below and above. The lower bound is the usual 'naive' entropy
of the experimental frequencies. The upper bound results from a hybrid approximation
of the entropy which makes use of the naive estimate, a maximum entropy fit, and
a coverage adjustment. We apply our simple scheme to artificial data, in order
to check their accuracy; we also compare its performance to those of several previously
defined entropy estimators. We then apply it to actual measurements of neural
activity in populations with up to 100 cells. Finally, we discuss the similarities
and differences between the proposed simple estimation scheme and various earlier
methods. © 2013 IOP Publishing Ltd and SISSA Medialab srl.
article_number: P03015
author:
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Julien
full_name: Dubuis, Julien
last_name: Dubuis
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Ravá
full_name: Da Silveira, Ravá
last_name: Da Silveira
citation:
ama: Berry M, Tkačik G, Dubuis J, Marre O, Da Silveira R. A simple method for estimating
the entropy of neural activity. Journal of Statistical Mechanics Theory and
Experiment. 2013;2013(3). doi:10.1088/1742-5468/2013/03/P03015
apa: Berry, M., Tkačik, G., Dubuis, J., Marre, O., & Da Silveira, R. (2013).
A simple method for estimating the entropy of neural activity. Journal of Statistical
Mechanics Theory and Experiment. IOP Publishing Ltd. https://doi.org/10.1088/1742-5468/2013/03/P03015
chicago: Berry, Michael, Gašper Tkačik, Julien Dubuis, Olivier Marre, and Ravá Da
Silveira. “A Simple Method for Estimating the Entropy of Neural Activity.” Journal
of Statistical Mechanics Theory and Experiment. IOP Publishing Ltd., 2013.
https://doi.org/10.1088/1742-5468/2013/03/P03015.
ieee: M. Berry, G. Tkačik, J. Dubuis, O. Marre, and R. Da Silveira, “A simple method
for estimating the entropy of neural activity,” Journal of Statistical Mechanics
Theory and Experiment, vol. 2013, no. 3. IOP Publishing Ltd., 2013.
ista: Berry M, Tkačik G, Dubuis J, Marre O, Da Silveira R. 2013. A simple method
for estimating the entropy of neural activity. Journal of Statistical Mechanics
Theory and Experiment. 2013(3), P03015.
mla: Berry, Michael, et al. “A Simple Method for Estimating the Entropy of Neural
Activity.” Journal of Statistical Mechanics Theory and Experiment, vol.
2013, no. 3, P03015, IOP Publishing Ltd., 2013, doi:10.1088/1742-5468/2013/03/P03015.
short: M. Berry, G. Tkačik, J. Dubuis, O. Marre, R. Da Silveira, Journal of Statistical
Mechanics Theory and Experiment 2013 (2013).
date_created: 2018-12-11T11:59:56Z
date_published: 2013-03-12T00:00:00Z
date_updated: 2021-01-12T07:00:14Z
day: '12'
department:
- _id: GaTk
doi: 10.1088/1742-5468/2013/03/P03015
intvolume: ' 2013'
issue: '3'
language:
- iso: eng
month: '03'
oa_version: None
publication: Journal of Statistical Mechanics Theory and Experiment
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '3941'
quality_controlled: '1'
scopus_import: 1
status: public
title: A simple method for estimating the entropy of neural activity
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2013
year: '2013'
...
---
_id: '2861'
abstract:
- lang: eng
text: We consider a two-parameter family of piecewise linear maps in which the moduli
of the two slopes take different values. We provide numerical evidence of the
existence of some parameter regions in which the Lyapunov exponent and the topological
entropy remain constant. Analytical proof of this phenomenon is also given for
certain cases. Surprisingly however, the systems with that property are not conjugate
as we prove by using kneading theory.
article_number: '125101'
author:
- first_name: Vicente
full_name: Botella Soler, Vicente
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: José
full_name: Oteo, José
last_name: Oteo
- first_name: Javier
full_name: Ros, Javier
last_name: Ros
- first_name: Paul
full_name: Glendinning, Paul
last_name: Glendinning
citation:
ama: 'Botella Soler V, Oteo J, Ros J, Glendinning P. Lyapunov exponent and topological
entropy plateaus in piecewise linear maps. Journal of Physics A: Mathematical
and Theoretical. 2013;46(12). doi:10.1088/1751-8113/46/12/125101'
apa: 'Botella Soler, V., Oteo, J., Ros, J., & Glendinning, P. (2013). Lyapunov
exponent and topological entropy plateaus in piecewise linear maps. Journal
of Physics A: Mathematical and Theoretical. IOP Publishing Ltd. https://doi.org/10.1088/1751-8113/46/12/125101'
chicago: 'Botella Soler, Vicente, José Oteo, Javier Ros, and Paul Glendinning. “Lyapunov
Exponent and Topological Entropy Plateaus in Piecewise Linear Maps.” Journal
of Physics A: Mathematical and Theoretical. IOP Publishing Ltd., 2013. https://doi.org/10.1088/1751-8113/46/12/125101.'
ieee: 'V. Botella Soler, J. Oteo, J. Ros, and P. Glendinning, “Lyapunov exponent
and topological entropy plateaus in piecewise linear maps,” Journal of Physics
A: Mathematical and Theoretical, vol. 46, no. 12. IOP Publishing Ltd., 2013.'
ista: 'Botella Soler V, Oteo J, Ros J, Glendinning P. 2013. Lyapunov exponent and
topological entropy plateaus in piecewise linear maps. Journal of Physics A: Mathematical
and Theoretical. 46(12), 125101.'
mla: 'Botella Soler, Vicente, et al. “Lyapunov Exponent and Topological Entropy
Plateaus in Piecewise Linear Maps.” Journal of Physics A: Mathematical and
Theoretical, vol. 46, no. 12, 125101, IOP Publishing Ltd., 2013, doi:10.1088/1751-8113/46/12/125101.'
short: 'V. Botella Soler, J. Oteo, J. Ros, P. Glendinning, Journal of Physics A:
Mathematical and Theoretical 46 (2013).'
date_created: 2018-12-11T11:59:59Z
date_published: 2013-03-29T00:00:00Z
date_updated: 2021-01-12T07:00:19Z
day: '29'
department:
- _id: GaTk
doi: 10.1088/1751-8113/46/12/125101
intvolume: ' 46'
issue: '12'
language:
- iso: eng
month: '03'
oa_version: None
publication: 'Journal of Physics A: Mathematical and Theoretical'
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '3928'
quality_controlled: '1'
scopus_import: 1
status: public
title: Lyapunov exponent and topological entropy plateaus in piecewise linear maps
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 46
year: '2013'
...
---
_id: '2863'
abstract:
- lang: eng
text: Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional probability
distribution over neural codewords given the sensory input. For large populations,
direct sampling of these distributions is impossible, and so we must rely on constructing
appropriate models. We show here that in a population of 100 retinal ganglion
cells in the salamander retina responding to temporal white-noise stimuli, dependencies
between cells play an important encoding role. We introduce the stimulus-dependent
maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear
model of a single neuron, to a pairwise-coupled neural population. We find that
the SDME model gives a more accurate account of single cell responses and in particular
significantly outperforms uncoupled models in reproducing the distributions of
population codewords emitted in response to a stimulus. We show how the SDME model,
in conjunction with static maximum entropy models of population vocabulary, can
be used to estimate information-theoretic quantities like average surprise and
information transmission in a neural population.
article_number: e1002922
author:
- first_name: Einat
full_name: Granot Atedgi, Einat
last_name: Granot Atedgi
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Ronen
full_name: Segev, Ronen
last_name: Segev
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
citation:
ama: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum
entropy models of neural population codes. PLoS Computational Biology.
2013;9(3). doi:10.1371/journal.pcbi.1002922
apa: Granot Atedgi, E., Tkačik, G., Segev, R., & Schneidman, E. (2013). Stimulus-dependent
maximum entropy models of neural population codes. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1002922
chicago: Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman.
“Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS
Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1002922.
ieee: E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent
maximum entropy models of neural population codes,” PLoS Computational Biology,
vol. 9, no. 3. Public Library of Science, 2013.
ista: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent
maximum entropy models of neural population codes. PLoS Computational Biology.
9(3), e1002922.
mla: Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of
Neural Population Codes.” PLoS Computational Biology, vol. 9, no. 3, e1002922,
Public Library of Science, 2013, doi:10.1371/journal.pcbi.1002922.
short: E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational
Biology 9 (2013).
date_created: 2018-12-11T12:00:00Z
date_published: 2013-03-01T00:00:00Z
date_updated: 2021-01-12T07:00:20Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1002922
file:
- access_level: open_access
checksum: 5a30876c193209fa05b26db71845dd16
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:45Z
date_updated: 2020-07-14T12:45:52Z
file_id: '5099'
file_name: IST-2013-120-v1+1_journal.pcbi.1002922.pdf
file_size: 1548120
relation: main_file
file_date_updated: 2020-07-14T12:45:52Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '3926'
pubrep_id: '120'
quality_controlled: '1'
scopus_import: 1
status: public
title: Stimulus-dependent maximum entropy models of neural population codes
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2013'
...
---
_id: '2913'
abstract:
- lang: eng
text: 'The ability of an organism to distinguish between various stimuli is limited
by the structure and noise in the population code of its sensory neurons. Here
we infer a distance measure on the stimulus space directly from the recorded activity
of 100 neurons in the salamander retina. In contrast to previously used measures
of stimulus similarity, this "neural metric" tells us how distinguishable
a pair of stimulus clips is to the retina, based on the similarity between the
induced distributions of population responses. We show that the retinal distance
strongly deviates from Euclidean, or any static metric, yet has a simple structure:
we identify the stimulus features that the neural population is jointly sensitive
to, and show the support-vector-machine- like kernel function relating the stimulus
and neural response spaces. We show that the non-Euclidean nature of the retinal
distance has important consequences for neural decoding.'
article_number: '058104'
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Einat
full_name: Granot Atedgi, Einat
last_name: Granot Atedgi
- first_name: Ronen
full_name: Segev, Ronen
last_name: Segev
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
citation:
ama: 'Tkačik G, Granot Atedgi E, Segev R, Schneidman E. Retinal metric: a stimulus
distance measure derived from population neural responses. Physical Review
Letters. 2013;110(5). doi:10.1103/PhysRevLett.110.058104'
apa: 'Tkačik, G., Granot Atedgi, E., Segev, R., & Schneidman, E. (2013). Retinal
metric: a stimulus distance measure derived from population neural responses.
Physical Review Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.110.058104'
chicago: 'Tkačik, Gašper, Einat Granot Atedgi, Ronen Segev, and Elad Schneidman.
“Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses.”
Physical Review Letters. American Physical Society, 2013. https://doi.org/10.1103/PhysRevLett.110.058104.'
ieee: 'G. Tkačik, E. Granot Atedgi, R. Segev, and E. Schneidman, “Retinal metric:
a stimulus distance measure derived from population neural responses,” Physical
Review Letters, vol. 110, no. 5. American Physical Society, 2013.'
ista: 'Tkačik G, Granot Atedgi E, Segev R, Schneidman E. 2013. Retinal metric: a
stimulus distance measure derived from population neural responses. Physical Review
Letters. 110(5), 058104.'
mla: 'Tkačik, Gašper, et al. “Retinal Metric: A Stimulus Distance Measure Derived
from Population Neural Responses.” Physical Review Letters, vol. 110, no.
5, 058104, American Physical Society, 2013, doi:10.1103/PhysRevLett.110.058104.'
short: G. Tkačik, E. Granot Atedgi, R. Segev, E. Schneidman, Physical Review Letters
110 (2013).
date_created: 2018-12-11T12:00:18Z
date_published: 2013-01-28T00:00:00Z
date_updated: 2021-01-12T07:00:39Z
day: '28'
department:
- _id: GaTk
doi: 10.1103/PhysRevLett.110.058104
intvolume: ' 110'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1205.6598
month: '01'
oa: 1
oa_version: Preprint
publication: Physical Review Letters
publication_status: published
publisher: American Physical Society
publist_id: '3830'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Retinal metric: a stimulus distance measure derived from population neural
responses'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2013'
...
---
_id: '2914'
abstract:
- lang: eng
text: The scale invariance of natural images suggests an analogy to the statistical
mechanics of physical systems at a critical point. Here we examine the distribution
of pixels in small image patches and show how to construct the corresponding thermodynamics.
We find evidence for criticality in a diverging specific heat, which corresponds
to large fluctuations in how "surprising" we find individual images,
and in the quantitative form of the entropy vs energy. We identify special image
configurations as local energy minima and show that average patches within each
basin are interpretable as lines and edges in all orientations.
acknowledgement: "This work was supported in part by NSF Grants No. IIS-0613435, No.
IBN-0344678, and No. PHY-0957573, by NIH Grant No. T32 MH065214, by the Human Frontier
Science Program, and by the Swartz Foundation.\r\nCC BY 3.0\r\n"
article_number: '018701'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Greg
full_name: Stephens, Greg
last_name: Stephens
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Stephens G, Mora T, Tkačik G, Bialek W. Statistical thermodynamics of natural
images. Physical Review Letters. 2013;110(1). doi:10.1103/PhysRevLett.110.018701
apa: Stephens, G., Mora, T., Tkačik, G., & Bialek, W. (2013). Statistical thermodynamics
of natural images. Physical Review Letters. American Physical Society.
https://doi.org/10.1103/PhysRevLett.110.018701
chicago: Stephens, Greg, Thierry Mora, Gašper Tkačik, and William Bialek. “Statistical
Thermodynamics of Natural Images.” Physical Review Letters. American Physical
Society, 2013. https://doi.org/10.1103/PhysRevLett.110.018701.
ieee: G. Stephens, T. Mora, G. Tkačik, and W. Bialek, “Statistical thermodynamics
of natural images,” Physical Review Letters, vol. 110, no. 1. American
Physical Society, 2013.
ista: Stephens G, Mora T, Tkačik G, Bialek W. 2013. Statistical thermodynamics of
natural images. Physical Review Letters. 110(1), 018701.
mla: Stephens, Greg, et al. “Statistical Thermodynamics of Natural Images.” Physical
Review Letters, vol. 110, no. 1, 018701, American Physical Society, 2013,
doi:10.1103/PhysRevLett.110.018701.
short: G. Stephens, T. Mora, G. Tkačik, W. Bialek, Physical Review Letters 110 (2013).
date_created: 2018-12-11T12:00:19Z
date_published: 2013-01-02T00:00:00Z
date_updated: 2023-09-04T11:47:51Z
day: '02'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1103/PhysRevLett.110.018701
external_id:
arxiv:
- '0806.2694'
file:
- access_level: open_access
checksum: 72bfbc2094c4680e8a8a6bed668cd06d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:44Z
date_updated: 2020-07-14T12:45:53Z
file_id: '5366'
file_name: IST-2016-401-v1+1_1281.full.pdf
file_size: 416965
relation: main_file
file_date_updated: 2020-07-14T12:45:53Z
has_accepted_license: '1'
intvolume: ' 110'
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
publication: Physical Review Letters
publication_status: published
publisher: American Physical Society
publist_id: '3829'
pubrep_id: '401'
quality_controlled: '1'
status: public
title: Statistical thermodynamics of natural images
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: 110
year: '2013'
...
---
_id: '3261'
abstract:
- lang: eng
text: Cells in a developing embryo have no direct way of "measuring" their
physical position. Through a variety of processes, however, the expression levels
of multiple genes come to be correlated with position, and these expression levels
thus form a code for "positional information." We show how to measure
this information, in bits, using the gap genes in the Drosophila embryo as an
example. Individual genes carry nearly two bits of information, twice as much
as expected if the expression patterns consisted only of on/off domains separated
by sharp boundaries. Taken together, four gap genes carry enough information to
define a cell's location with an error bar of ~1% along the anterior-posterior
axis of the embryo. This precision is nearly enough for each cell to have a unique
identity, which is the maximum information the system can use, and is nearly constant
along the length of the embryo. We argue that this constancy is a signature of
optimality in the transmission of information from primary morphogen inputs to
the output of the gap gene network.
author:
- first_name: Julien
full_name: Dubuis, Julien
last_name: Dubuis
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Eric
full_name: Wieschaus, Eric
last_name: Wieschaus
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. Positional information,
in bits. PNAS. 2013;110(41):16301-16308. doi:10.1073/pnas.1315642110
apa: Dubuis, J., Tkačik, G., Wieschaus, E., Gregor, T., & Bialek, W. (2013).
Positional information, in bits. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1315642110
chicago: Dubuis, Julien, Gašper Tkačik, Eric Wieschaus, Thomas Gregor, and William
Bialek. “Positional Information, in Bits.” PNAS. National Academy of Sciences,
2013. https://doi.org/10.1073/pnas.1315642110.
ieee: J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, and W. Bialek, “Positional
information, in bits,” PNAS, vol. 110, no. 41. National Academy of Sciences,
pp. 16301–16308, 2013.
ista: Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. 2013. Positional information,
in bits. PNAS. 110(41), 16301–16308.
mla: Dubuis, Julien, et al. “Positional Information, in Bits.” PNAS, vol.
110, no. 41, National Academy of Sciences, 2013, pp. 16301–08, doi:10.1073/pnas.1315642110.
short: J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, W. Bialek, PNAS 110 (2013)
16301–16308.
date_created: 2018-12-11T12:02:19Z
date_published: 2013-10-08T00:00:00Z
date_updated: 2021-01-12T07:42:13Z
day: '08'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1073/pnas.1315642110
external_id:
pmid:
- '24089448'
file:
- access_level: open_access
checksum: ecd859fe52a562193027d428b5524a8d
content_type: application/pdf
creator: dernst
date_created: 2019-01-22T13:53:23Z
date_updated: 2020-07-14T12:46:06Z
file_id: '5873'
file_name: 2013_PNAS_Dubuis.pdf
file_size: 1670548
relation: main_file
file_date_updated: 2020-07-14T12:46:06Z
has_accepted_license: '1'
intvolume: ' 110'
issue: '41'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 16301 - 16308
pmid: 1
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '3387'
quality_controlled: '1'
scopus_import: 1
status: public
title: Positional information, in bits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2013'
...
---
_id: '2277'
abstract:
- lang: eng
text: Redundancies and correlations in the responses of sensory neurons may seem
to waste neural resources, but they can also carry cues about structured stimuli
and may help the brain to correct for response errors. To investigate the effect
of stimulus structure on redundancy in retina, we measured simultaneous responses
from populations of retinal ganglion cells presented with natural and artificial
stimuli that varied greatly in correlation structure; these stimuli and recordings
are publicly available online. Responding to spatio-temporally structured stimuli
such as natural movies, pairs of ganglion cells were modestly more correlated
than in response to white noise checkerboards, but they were much less correlated
than predicted by a non-adapting functional model of retinal response. Meanwhile,
responding to stimuli with purely spatial correlations, pairs of ganglion cells
showed increased correlations consistent with a static, non-adapting receptive
field and nonlinearity. We found that in response to spatio-temporally correlated
stimuli, ganglion cells had faster temporal kernels and tended to have stronger
surrounds. These properties of individual cells, along with gain changes that
opposed changes in effective contrast at the ganglion cell input, largely explained
the pattern of pairwise correlations across stimuli where receptive field measurements
were possible.
article_number: e1003344
author:
- first_name: Kristina
full_name: Simmons, Kristina
last_name: Simmons
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Jan
full_name: Homann, Jan
last_name: Homann
- first_name: Heather
full_name: Yee, Heather
last_name: Yee
- first_name: Stephanie
full_name: Palmer, Stephanie
last_name: Palmer
- first_name: Philip
full_name: Nelson, Philip
last_name: Nelson
- first_name: Vijay
full_name: Balasubramanian, Vijay
last_name: Balasubramanian
citation:
ama: Simmons K, Prentice J, Tkačik G, et al. Transformation of stimulus correlations
by the retina. PLoS Computational Biology. 2013;9(12). doi:10.1371/journal.pcbi.1003344
apa: Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian,
V. (2013). Transformation of stimulus correlations by the retina. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003344
chicago: Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather Yee,
Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Transformation of
Stimulus Correlations by the Retina.” PLoS Computational Biology. Public
Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1003344.
ieee: K. Simmons et al., “Transformation of stimulus correlations by the
retina,” PLoS Computational Biology, vol. 9, no. 12. Public Library of
Science, 2013.
ista: Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian
V. 2013. Transformation of stimulus correlations by the retina. PLoS Computational
Biology. 9(12), e1003344.
mla: Simmons, Kristina, et al. “Transformation of Stimulus Correlations by the Retina.”
PLoS Computational Biology, vol. 9, no. 12, e1003344, Public Library of
Science, 2013, doi:10.1371/journal.pcbi.1003344.
short: K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson,
V. Balasubramanian, PLoS Computational Biology 9 (2013).
date_created: 2018-12-11T11:56:43Z
date_published: 2013-12-05T00:00:00Z
date_updated: 2023-02-23T14:07:04Z
day: '05'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1003344
file:
- access_level: open_access
checksum: 46722afc4f7eabb0831165d9c1b171ad
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:36Z
date_updated: 2020-07-14T12:45:36Z
file_id: '5089'
file_name: IST-2016-410-v1+1_journal.pcbi.1003344.pdf
file_size: 3115568
relation: main_file
file_date_updated: 2020-07-14T12:45:36Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '4667'
pubrep_id: '410'
quality_controlled: '1'
related_material:
record:
- id: '9752'
relation: research_data
status: public
scopus_import: 1
status: public
title: Transformation of stimulus correlations by the retina
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: 9
year: '2013'
...
---
_id: '2413'
abstract:
- lang: eng
text: 'Progress in understanding the global brain dynamics has remained slow to
date in large part because of the highly multiscale nature of brain activity.
Indeed, normal brain dynamics is characterized by complex interactions between
multiple levels: from the microscopic scale of single neurons to the mesoscopic
level of local groups of neurons, and finally to the macroscopic level of the
whole brain. Among the most difficult tasks are those of identifying which scales
are significant for a given particular function and describing how the scales
affect each other. It is important to realize that the scales of time and space
are linked together, or even intertwined, and that causal inference is far more
ambiguous between than within levels. We approach this problem from the perspective
of our recent work on simultaneous recording from micro- and macroelectrodes in
the human brain. We propose a physiological description of these multilevel interactions,
based on phase–amplitude coupling of neuronal oscillations that operate at multiple
frequencies and on different spatial scales. Specifically, the amplitude of the
oscillations on a particular spatial scale is modulated by phasic variations in
neuronal excitability induced by lower frequency oscillations that emerge on a
larger spatial scale. Following this general principle, it is possible to scale
up or scale down the multiscale brain dynamics. It is expected that large-scale
network oscillations in the low-frequency range, mediating downward effects, may
play an important role in attention and consciousness.'
alternative_title:
- Reviews of Nonlinear Dynamics and Complexity
author:
- first_name: Mario
full_name: Valderrama, Mario
last_name: Valderrama
- first_name: Vicente
full_name: Botella Soler, Vicente
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: Michel
full_name: Le Van Quyen, Michel
last_name: Le Van Quyen
citation:
ama: 'Valderrama M, Botella Soler V, Le Van Quyen M. Neuronal oscillations scale
up and scale down the brain dynamics . In: Meyer M, Pesenson Z, eds. Multiscale
Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH; 2013.
doi:10.1002/9783527671632.ch08'
apa: 'Valderrama, M., Botella Soler, V., & Le Van Quyen, M. (2013). Neuronal
oscillations scale up and scale down the brain dynamics . In M. Meyer & Z.
Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to
the Brain. Wiley-VCH. https://doi.org/10.1002/9783527671632.ch08'
chicago: 'Valderrama, Mario, Vicente Botella Soler, and Michel Le Van Quyen. “Neuronal
Oscillations Scale up and Scale down the Brain Dynamics .” In Multiscale Analysis
and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and
Z. Pesenson. Wiley-VCH, 2013. https://doi.org/10.1002/9783527671632.ch08.'
ieee: 'M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations
scale up and scale down the brain dynamics ,” in Multiscale Analysis and Nonlinear
Dynamics: From Genes to the Brain, M. Meyer and Z. Pesenson, Eds. Wiley-VCH,
2013.'
ista: 'Valderrama M, Botella Soler V, Le Van Quyen M. 2013.Neuronal oscillations
scale up and scale down the brain dynamics . In: Multiscale Analysis and Nonlinear
Dynamics: From Genes to the Brain. Reviews of Nonlinear Dynamics and Complexity,
.'
mla: 'Valderrama, Mario, et al. “Neuronal Oscillations Scale up and Scale down the
Brain Dynamics .” Multiscale Analysis and Nonlinear Dynamics: From Genes to
the Brain, edited by Misha Meyer and Z. Pesenson, Wiley-VCH, 2013, doi:10.1002/9783527671632.ch08.'
short: 'M. Valderrama, V. Botella Soler, M. Le Van Quyen, in:, M. Meyer, Z. Pesenson
(Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley-VCH,
2013.'
date_created: 2018-12-11T11:57:31Z
date_published: 2013-08-01T00:00:00Z
date_updated: 2021-01-12T06:57:20Z
day: '01'
department:
- _id: GaTk
doi: 10.1002/9783527671632.ch08
editor:
- first_name: Misha
full_name: Meyer, Misha
last_name: Meyer
- first_name: Z.
full_name: Pesenson, Z.
last_name: Pesenson
language:
- iso: eng
month: '08'
oa_version: None
publication: 'Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain'
publication_identifier:
eisbn:
- '9783527671632'
isbn:
- '9783527411986 '
publication_status: published
publisher: Wiley-VCH
publist_id: '4513'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Neuronal oscillations scale up and scale down the brain dynamics '
type: book_chapter
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '499'
abstract:
- lang: eng
text: Exposure of an isogenic bacterial population to a cidal antibiotic typically
fails to eliminate a small fraction of refractory cells. Historically, fractional
killing has been attributed to infrequently dividing or nondividing "persisters."
Using microfluidic cultures and time-lapse microscopy, we found that Mycobacterium
smegmatis persists by dividing in the presence of the drug isoniazid (INH). Although
persistence in these studies was characterized by stable numbers of cells, this
apparent stability was actually a dynamic state of balanced division and death.
Single cells expressed catalase-peroxidase (KatG), which activates INH, in stochastic
pulses that were negatively correlated with cell survival. These behaviors may
reflect epigenetic effects, because KatG pulsing and death were correlated between
sibling cells. Selection of lineages characterized by infrequent KatG pulsing
could allow nonresponsive adaptation during prolonged drug exposure.
author:
- first_name: Yurichi
full_name: Wakamoto, Yurichi
last_name: Wakamoto
- first_name: Neraaj
full_name: Dhar, Neraaj
last_name: Dhar
- 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: Katrin
full_name: Schneider, Katrin
last_name: Schneider
- first_name: François
full_name: Signorino Gelo, François
last_name: Signorino Gelo
- first_name: Stanislas
full_name: Leibler, Stanislas
last_name: Leibler
- first_name: John
full_name: Mckinney, John
last_name: Mckinney
citation:
ama: Wakamoto Y, Dhar N, Chait RP, et al. Dynamic persistence of antibiotic-stressed
mycobacteria. Science. 2013;339(6115):91-95. doi:10.1126/science.1229858
apa: Wakamoto, Y., Dhar, N., Chait, R. P., Schneider, K., Signorino Gelo, F., Leibler,
S., & Mckinney, J. (2013). Dynamic persistence of antibiotic-stressed mycobacteria.
Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.1229858
chicago: Wakamoto, Yurichi, Neraaj Dhar, Remy P Chait, Katrin Schneider, François
Signorino Gelo, Stanislas Leibler, and John Mckinney. “Dynamic Persistence of
Antibiotic-Stressed Mycobacteria.” Science. American Association for the
Advancement of Science, 2013. https://doi.org/10.1126/science.1229858.
ieee: Y. Wakamoto et al., “Dynamic persistence of antibiotic-stressed mycobacteria,”
Science, vol. 339, no. 6115. American Association for the Advancement of
Science, pp. 91–95, 2013.
ista: Wakamoto Y, Dhar N, Chait RP, Schneider K, Signorino Gelo F, Leibler S, Mckinney
J. 2013. Dynamic persistence of antibiotic-stressed mycobacteria. Science. 339(6115),
91–95.
mla: Wakamoto, Yurichi, et al. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.”
Science, vol. 339, no. 6115, American Association for the Advancement of
Science, 2013, pp. 91–95, doi:10.1126/science.1229858.
short: Y. Wakamoto, N. Dhar, R.P. Chait, K. Schneider, F. Signorino Gelo, S. Leibler,
J. Mckinney, Science 339 (2013) 91–95.
date_created: 2018-12-11T11:46:48Z
date_published: 2013-01-04T00:00:00Z
date_updated: 2021-01-12T08:01:06Z
day: '04'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1126/science.1229858
intvolume: ' 339'
issue: '6115'
language:
- iso: eng
month: '01'
oa_version: None
page: 91 - 95
publication: Science
publication_status: published
publisher: American Association for the Advancement of Science
publist_id: '7321'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamic persistence of antibiotic-stressed mycobacteria
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 339
year: '2013'
...
---
_id: '3262'
abstract:
- lang: eng
text: Living cells must control the reading out or "expression" of information
encoded in their genomes, and this regulation often is mediated by transcription
factors--proteins that bind to DNA and either enhance or repress the expression
of nearby genes. But the expression of transcription factor proteins is itself
regulated, and many transcription factors regulate their own expression in addition
to responding to other input signals. Here we analyze the simplest of such self-regulatory
circuits, asking how parameters can be chosen to optimize information transmission
from inputs to outputs in the steady state. Some nonzero level of self-regulation
is almost always optimal, with self-activation dominant when transcription factor
concentrations are low and self-repression dominant when concentrations are high.
In steady state the optimal self-activation is never strong enough to induce bistability,
although there is a limit in which the optimal parameters are very close to the
critical point.
acknowledgement: "We thank T. Gregor, E. F. Wieschaus, and, especially, C. G. Callan
for helpful discussions.\r\nWork at Princeton was supported in part by NSF Grants
No. PHY–0957573 and No. CCF–0939370, by NIH Grant No. R01 GM077599, and by the W.
M. Keck Foundation. For part of this work, G.T. was supported in part by NSF Grant
No. EF–0928048 and by the Vice Provost for Research at the University of Pennsylvania."
article_number: '041903'
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Aleksandra
full_name: Walczak, Aleksandra
last_name: Walczak
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Walczak A, Bialek W. Optimizing information flow in small genetic
networks. III. A self-interacting gene. Physical Review E statistical nonlinear
and soft matter physics . 2012;85(4). doi:10.1103/PhysRevE.85.041903
apa: Tkačik, G., Walczak, A., & Bialek, W. (2012). Optimizing information flow
in small genetic networks. III. A self-interacting gene. Physical Review E
Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.
https://doi.org/10.1103/PhysRevE.85.041903
chicago: Tkačik, Gašper, Aleksandra Walczak, and William Bialek. “Optimizing Information
Flow in Small Genetic Networks. III. A Self-Interacting Gene.” Physical Review
E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics,
2012. https://doi.org/10.1103/PhysRevE.85.041903.
ieee: G. Tkačik, A. Walczak, and W. Bialek, “Optimizing information flow in small
genetic networks. III. A self-interacting gene,” Physical Review E statistical
nonlinear and soft matter physics , vol. 85, no. 4. American Institute of
Physics, 2012.
ista: Tkačik G, Walczak A, Bialek W. 2012. Optimizing information flow in small
genetic networks. III. A self-interacting gene. Physical Review E statistical
nonlinear and soft matter physics . 85(4), 041903.
mla: Tkačik, Gašper, et al. “Optimizing Information Flow in Small Genetic Networks.
III. A Self-Interacting Gene.” Physical Review E Statistical Nonlinear and
Soft Matter Physics , vol. 85, no. 4, 041903, American Institute of Physics,
2012, doi:10.1103/PhysRevE.85.041903.
short: G. Tkačik, A. Walczak, W. Bialek, Physical Review E Statistical Nonlinear
and Soft Matter Physics 85 (2012).
date_created: 2018-12-11T12:02:20Z
date_published: 2012-04-01T00:00:00Z
date_updated: 2021-01-12T07:42:14Z
day: '01'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.85.041903
intvolume: ' 85'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1112.5026
month: '04'
oa: 1
oa_version: Preprint
publication: ' Physical Review E statistical nonlinear and soft matter physics '
publication_status: published
publisher: American Institute of Physics
publist_id: '3386'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimizing information flow in small genetic networks. III. A self-interacting
gene
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 85
year: '2012'
...
---
_id: '3274'
abstract:
- lang: eng
text: A boundary element model of a tunnel running through horizontally layered
soil with anisotropic material properties is presented. Since there is no analytical
fundamental solution for wave propagation inside a layered orthotropic medium
in 3D, the fundamental displacements and stresses have to be calculated numerically.
In our model this is done in the Fourier domain with respect to space and time.
The assumption of a straight tunnel with infinite extension in the x direction
makes it possible to decouple the system for every wave number kx, leading to
a 2.5D-problem, which is suited for parallel computation. The special form of
the fundamental solution, resulting from our Fourier ansatz, and the fact, that
the calculation of the boundary integral equation is performed in the Fourier
domain, enhances the stability and efficiency of the numerical calculations.
acknowledgement: This work was supported by the Austrian Federal Ministry of Transport,
Innovation and Technology under the Grant Bmvit-isb2 and the FFG under the project
Pr. Nr. 809089.
author:
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
- first_name: Wolfgang
full_name: Kreuzer, Wolfgang
last_name: Kreuzer
- first_name: Holger
full_name: Waubke, Holger
last_name: Waubke
- first_name: Peter
full_name: Balazs, Peter
last_name: Balazs
citation:
ama: Rieckh G, Kreuzer W, Waubke H, Balazs P. A 2.5D-Fourier-BEM model for vibrations
in a tunnel running through layered anisotropic soil. Engineering Analysis
with Boundary Elements. 2012;36(6):960-967. doi:10.1016/j.enganabound.2011.12.014
apa: Rieckh, G., Kreuzer, W., Waubke, H., & Balazs, P. (2012). A 2.5D-Fourier-BEM
model for vibrations in a tunnel running through layered anisotropic soil.
Engineering Analysis with Boundary Elements. Elsevier. https://doi.org/10.1016/j.enganabound.2011.12.014
chicago: Rieckh, Georg, Wolfgang Kreuzer, Holger Waubke, and Peter Balazs. “A 2.5D-Fourier-BEM
Model for Vibrations in a Tunnel Running through Layered Anisotropic Soil.”
Engineering Analysis with Boundary Elements. Elsevier, 2012. https://doi.org/10.1016/j.enganabound.2011.12.014.
ieee: G. Rieckh, W. Kreuzer, H. Waubke, and P. Balazs, “A 2.5D-Fourier-BEM model
for vibrations in a tunnel running through layered anisotropic soil,” Engineering
Analysis with Boundary Elements, vol. 36, no. 6. Elsevier, pp. 960–967, 2012.
ista: Rieckh G, Kreuzer W, Waubke H, Balazs P. 2012. A 2.5D-Fourier-BEM model for
vibrations in a tunnel running through layered anisotropic soil. Engineering
Analysis with Boundary Elements. 36(6), 960–967.
mla: Rieckh, Georg, et al. “A 2.5D-Fourier-BEM Model for Vibrations in a Tunnel
Running through Layered Anisotropic Soil.” Engineering Analysis with Boundary
Elements, vol. 36, no. 6, Elsevier, 2012, pp. 960–67, doi:10.1016/j.enganabound.2011.12.014.
short: G. Rieckh, W. Kreuzer, H. Waubke, P. Balazs, Engineering Analysis with Boundary
Elements 36 (2012) 960–967.
date_created: 2018-12-11T12:02:24Z
date_published: 2012-06-01T00:00:00Z
date_updated: 2021-01-12T07:42:19Z
day: '01'
department:
- _id: GaTk
doi: 10.1016/j.enganabound.2011.12.014
intvolume: ' 36'
issue: '6'
language:
- iso: eng
month: '06'
oa_version: None
page: 960 - 967
publication: ' Engineering Analysis with Boundary Elements'
publication_status: published
publisher: Elsevier
publist_id: '3372'
quality_controlled: '1'
scopus_import: 1
status: public
title: A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered
anisotropic soil
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2012'
...