---
_id: '1394'
abstract:
- lang: eng
text: "The solution space of genome-scale models of cellular metabolism provides
a map between physically\r\nviable flux configurations and cellular metabolic
phenotypes described, at the most basic level, by the\r\ncorresponding growth
rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat
empirical growth rate distributions recently obtained in experiments at single-cell
resolution can\r\nbe explained in terms of a trade-off between the higher fitness
of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based
on this, we propose a minimal model for the evolution of\r\na large bacterial
population that captures this trade-off. The scaling relationships observed in\r\nexperiments
encode, in such frameworks, for the same distance from the maximum achievable
growth\r\nrate, the same degree of growth rate maximization, and/or the same rate
of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions,
these results allow for multiple\r\nimplications and extensions in spite of the
underlying conceptual simplicity."
acknowledgement: "The research leading to these results has received funding from
the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038."
article_number: '036005'
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Fabrizio
full_name: Capuani, Fabrizio
last_name: Capuani
- first_name: Andrea
full_name: De Martino, Andrea
last_name: De Martino
citation:
ama: 'De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005'
apa: 'De Martino, D., Capuani, F., & De Martino, A. (2016). Growth against entropy
in bacterial metabolism: the phenotypic trade-off behind empirical growth rate
distributions in E. coli. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/3/036005'
chicago: 'De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth
against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical
Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing
Ltd., 2016. https://doi.org/10.1088/1478-3975/13/3/036005.'
ieee: 'D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in
bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.'
ista: 'De Martino D, Capuani F, De Martino A. 2016. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 13(3), 036005.'
mla: 'De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism:
The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.”
Physical Biology, vol. 13, no. 3, 036005, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/3/036005.'
short: D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).
date_created: 2018-12-11T11:51:46Z
date_published: 2016-05-27T00:00:00Z
date_updated: 2021-01-12T06:50:23Z
day: '27'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/3/036005
ec_funded: 1
intvolume: ' 13'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1601.03243
month: '05'
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 Biology
publication_status: published
publisher: IOP Publishing Ltd.
publist_id: '5815'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Growth against entropy in bacterial metabolism: the phenotypic trade-off behind
empirical growth rate distributions in E. coli'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2016'
...
---
_id: '1420'
abstract:
- lang: eng
text: 'Selection, mutation, and random drift affect the dynamics of allele frequencies
and consequently of quantitative traits. While the macroscopic dynamics of quantitative
traits can be measured, the underlying allele frequencies are typically unobserved.
Can we understand how the macroscopic observables evolve without following these
microscopic processes? This problem has been studied previously by analogy with
statistical mechanics: the allele frequency distribution at each time point is
approximated by the stationary form, which maximizes entropy. We explore the limitations
of this method when mutation is small (4Nμ < 1) so that populations are typically
close to fixation, and we extend the theory in this regime to account for changes
in mutation strength. We consider a single diallelic locus either under directional
selection or with overdominance and then generalize to multiple unlinked biallelic
loci with unequal effects. We find that the maximum-entropy approximation is remarkably
accurate, even when mutation and selection change rapidly. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of
quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127
apa: Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation
for the dynamics of quantitative traits. Genetics. Genetics Society of
America. https://doi.org/10.1534/genetics.115.184127
chicago: Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation
for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of
America, 2016. https://doi.org/10.1534/genetics.115.184127.
ieee: K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics
of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of
America, pp. 1523–1548, 2016.
ista: Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics
of quantitative traits. Genetics. 202(4), 1523–1548.
mla: Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative
Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016,
pp. 1523–48, doi:10.1534/genetics.115.184127.
short: K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.
date_created: 2018-12-11T11:51:55Z
date_published: 2016-04-06T00:00:00Z
date_updated: 2025-05-28T11:42:47Z
day: '06'
department:
- _id: GaTk
- _id: NiBa
doi: 10.1534/genetics.115.184127
ec_funded: 1
external_id:
arxiv:
- '1510.08344'
intvolume: ' 202'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1510.08344
month: '04'
oa: 1
oa_version: Preprint
page: 1523 - 1548
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '5787'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A general approximation for the dynamics of quantitative traits
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2016'
...
---
_id: '948'
abstract:
- lang: eng
text: Experience constantly shapes neural circuits through a variety of plasticity
mechanisms. While the functional roles of some plasticity mechanisms are well-understood,
it remains unclear how changes in neural excitability contribute to learning.
Here, we develop a normative interpretation of intrinsic plasticity (IP) as a
key component of unsupervised learning. We introduce a novel generative mixture
model that accounts for the class-specific statistics of stimulus intensities,
and we derive a neural circuit that learns the input classes and their intensities.
We will analytically show that inference and learning for our generative model
can be achieved by a neural circuit with intensity-sensitive neurons equipped
with a specific form of IP. Numerical experiments verify our analytical derivations
and show robust behavior for artificial and natural stimuli. Our results link
IP to non-trivial input statistics, in particular the statistics of stimulus intensities
for classes to which a neuron is sensitive. More generally, our work paves the
way toward new classification algorithms that are robust to intensity variations.
acknowledgement: DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1
(JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement
no. 291734 (CS)
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Travis
full_name: Monk, Travis
last_name: Monk
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Jörg
full_name: Lücke, Jörg
last_name: Lücke
citation:
ama: 'Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn
stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems;
2016:4285-4293.'
apa: 'Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic
plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented
at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural
Information Processing Systems.'
chicago: Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic
Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information
Processing Systems, 2016.
ieee: 'T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics,” presented at the NIPS: Neural Information
Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.'
ista: 'Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 29, 4285–4293.'
mla: Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus
Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016,
pp. 4285–93.
short: T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems,
2016, pp. 4285–4293.
conference:
end_date: 2016-12-10
location: Barcelona, Spaine
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:49:21Z
date_published: 2016-01-01T00:00:00Z
date_updated: 2021-01-12T08:22:08Z
day: '01'
department:
- _id: GaTk
ec_funded: 1
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- url: https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics
month: '01'
oa_version: None
page: 4285 - 4293
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6469'
quality_controlled: '1'
scopus_import: 1
status: public
title: Neurons equipped with intrinsic plasticity learn stimulus intensity statistics
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '9869'
abstract:
- lang: eng
text: A lower bound on the error of a positional estimator with limited positional
information is derived.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position.
2016. doi:10.1371/journal.pone.0163628.s001
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator
of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on
an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of
position.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position,
Public Library of Science, 10.1371/journal.pone.0163628.s001.
mla: Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position.
Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T08:53:48Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s001
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Error bound on an estimator of position
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9870'
abstract:
- lang: eng
text: The effect of noise in the input field on an Ising model is approximated.
Furthermore, methods to compute positional information in an Ising model by transfer
matrices and Monte Carlo sampling are outlined.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in an Ising model.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in an
Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:23:45Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s002
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in an Ising model
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9871'
abstract:
- lang: eng
text: The positional information in a discrete morphogen field with Gaussian noise
is computed.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in a Discrete Morphogen Field.” Public Library of Science,
2016. https://doi.org/10.1371/journal.pone.0163628.s003.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in a discrete morphogen field.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in a
Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:27:35Z
date_updated: 2023-02-21T16:56:40Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s003
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in a discrete morphogen field
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '1082'
abstract:
- lang: eng
text: In many applications, it is desirable to extract only the relevant aspects
of data. A principled way to do this is the information bottleneck (IB) method,
where one seeks a code that maximises information about a relevance variable,
Y, while constraining the information encoded about the original data, X. Unfortunately
however, the IB method is computationally demanding when data are high-dimensional
and/or non-gaussian. Here we propose an approximate variational scheme for maximising
a lower bound on the IB objective, analogous to variational EM. Using this method,
we derive an IB algorithm to recover features that are both relevant and sparse.
Finally, we demonstrate how kernelised versions of the algorithm can be used to
address a broad range of problems with non-linear relation between X and Y.
alternative_title:
- Advances in Neural Information Processing Systems
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: 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: 'Chalk MJ, Marre O, Tkačik G. Relevant sparse codes with variational information
bottleneck. In: Vol 29. Neural Information Processing Systems; 2016:1965-1973.'
apa: 'Chalk, M. J., Marre, O., & Tkačik, G. (2016). Relevant sparse codes with
variational information bottleneck (Vol. 29, pp. 1965–1973). Presented at the
NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information
Processing Systems.'
chicago: Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Relevant Sparse Codes
with Variational Information Bottleneck,” 29:1965–73. Neural Information Processing
Systems, 2016.
ieee: 'M. J. Chalk, O. Marre, and G. Tkačik, “Relevant sparse codes with variational
information bottleneck,” presented at the NIPS: Neural Information Processing
Systems, Barcelona, Spain, 2016, vol. 29, pp. 1965–1973.'
ista: 'Chalk MJ, Marre O, Tkačik G. 2016. Relevant sparse codes with variational
information bottleneck. NIPS: Neural Information Processing Systems, Advances
in Neural Information Processing Systems, vol. 29, 1965–1973.'
mla: Chalk, Matthew J., et al. Relevant Sparse Codes with Variational Information
Bottleneck. Vol. 29, Neural Information Processing Systems, 2016, pp. 1965–73.
short: M.J. Chalk, O. Marre, G. Tkačik, in:, Neural Information Processing Systems,
2016, pp. 1965–1973.
conference:
end_date: 2016-12-10
location: Barcelona, Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:03Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:09Z
day: '01'
department:
- _id: GaTk
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1605.07332
month: '12'
oa: 1
oa_version: Preprint
page: 1965-1973
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6298'
quality_controlled: '1'
related_material:
link:
- relation: other
url: https://papers.nips.cc/paper/6101-relevant-sparse-codes-with-variational-information-bottleneck
scopus_import: 1
status: public
title: Relevant sparse codes with variational information bottleneck
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1105'
abstract:
- lang: eng
text: Jointly characterizing neural responses in terms of several external variables
promises novel insights into circuit function, but remains computationally prohibitive
in practice. Here we use gaussian process (GP) priors and exploit recent advances
in fast GP inference and learning based on Kronecker methods, to efficiently estimate
multidimensional nonlinear tuning functions. Our estimator require considerably
less data than traditional methods and further provides principled uncertainty
estimates. We apply these tools to hippocampal recordings during open field exploration
and use them to characterize the joint dependence of CA1 responses on the position
of the animal and several other variables, including the animal\'s speed, direction
of motion, and network oscillations.Our results provide an unprecedentedly detailed
quantification of the tuning of hippocampal neurons. The model\'s generality suggests
that our approach can be used to estimate neural response properties in other
brain regions.
acknowledgement: "We thank Jozsef Csicsvari for kindly sharing the CA1 data.\r\nThis
work was supported by the People Programme (Marie Curie Actions) of the European
Union’s Seventh Framework Programme(FP7/2007-2013) under REA grant agreement no.
291734."
alternative_title:
- Advances in Neural Information Processing Systems
author:
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: 'Savin C, Tkačik G. Estimating nonlinear neural response functions using GP
priors and Kronecker methods. In: Vol 29. Neural Information Processing Systems;
2016:3610-3618.'
apa: 'Savin, C., & Tkačik, G. (2016). Estimating nonlinear neural response functions
using GP priors and Kronecker methods (Vol. 29, pp. 3610–3618). Presented at the
NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information
Processing Systems.'
chicago: Savin, Cristina, and Gašper Tkačik. “Estimating Nonlinear Neural Response
Functions Using GP Priors and Kronecker Methods,” 29:3610–18. Neural Information
Processing Systems, 2016.
ieee: 'C. Savin and G. Tkačik, “Estimating nonlinear neural response functions using
GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing
Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.'
ista: 'Savin C, Tkačik G. 2016. Estimating nonlinear neural response functions using
GP priors and Kronecker methods. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 29, 3610–3618.'
mla: Savin, Cristina, and Gašper Tkačik. Estimating Nonlinear Neural Response
Functions Using GP Priors and Kronecker Methods. Vol. 29, Neural Information
Processing Systems, 2016, pp. 3610–18.
short: C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp.
3610–3618.
conference:
end_date: 2016-12-10
location: Barcelona; Spain
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:50:10Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:48:19Z
day: '01'
department:
- _id: GaTk
ec_funded: 1
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- url: http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods
month: '12'
oa_version: None
page: 3610-3618
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '6265'
quality_controlled: '1'
scopus_import: 1
status: public
title: Estimating nonlinear neural response functions using GP priors and Kronecker
methods
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '1128'
abstract:
- lang: eng
text: "The process of gene expression is central to the modern understanding of
how cellular systems\r\nfunction. In this process, a special kind of regulatory
proteins, called transcription factors,\r\nare important to determine how much
protein is produced from a given gene. As biological\r\ninformation is transmitted
from transcription factor concentration to mRNA levels to amounts of\r\nprotein,
various sources of noise arise and pose limits to the fidelity of intracellular
signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene
expression: (i) the mathematical\r\ndescription of complex promoters responsible
for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information
processing the cell faces due to the interference from multiple\r\nfluctuating
signals, (iii) how the presence of gene expression noise influences the evolution\r\nof
regulatory sequences, (iv) and tools for the experimental study of origins and
consequences\r\nof cell-cell heterogeneity, including an application to bacterial
stress response systems."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
citation:
ama: Rieckh G. Studying the complexities of transcriptional regulation. 2016.
apa: Rieckh, G. (2016). Studying the complexities of transcriptional regulation.
Institute of Science and Technology Austria.
chicago: Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.”
Institute of Science and Technology Austria, 2016.
ieee: G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute
of Science and Technology Austria, 2016.
ista: Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute
of Science and Technology Austria.
mla: Rieckh, Georg. Studying the Complexities of Transcriptional Regulation.
Institute of Science and Technology Austria, 2016.
short: G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute
of Science and Technology Austria, 2016.
date_created: 2018-12-11T11:50:18Z
date_published: 2016-08-01T00:00:00Z
date_updated: 2023-09-07T11:44:34Z
day: '01'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GaTk
file:
- access_level: closed
checksum: ec453918c3bf8e6f460fd1156ef7b493
content_type: application/pdf
creator: dernst
date_created: 2019-08-13T11:46:25Z
date_updated: 2019-08-13T11:46:25Z
file_id: '6815'
file_name: Thesis_Georg_Rieckh_w_signature_page.pdf
file_size: 2614660
relation: main_file
- access_level: open_access
checksum: 51ae398166370d18fd22478b6365c4da
content_type: application/pdf
creator: dernst
date_created: 2020-09-21T11:30:40Z
date_updated: 2020-09-21T11:30:40Z
file_id: '8542'
file_name: Thesis_Georg_Rieckh.pdf
file_size: 6096178
relation: main_file
success: 1
file_date_updated: 2020-09-21T11:30:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: '114'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6232'
status: public
supervisor:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
title: Studying the complexities of transcriptional regulation
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2016'
...
---
_id: '1148'
abstract:
- lang: eng
text: Continuous-time Markov chain (CTMC) models have become a central tool for
understanding the dynamics of complex reaction networks and the importance of
stochasticity in the underlying biochemical processes. When such models are employed
to answer questions in applications, in order to ensure that the model provides
a sufficiently accurate representation of the real system, it is of vital importance
that the model parameters are inferred from real measured data. This, however,
is often a formidable task and all of the existing methods fail in one case or
the other, usually because the underlying CTMC model is high-dimensional and computationally
difficult to analyze. The parameter inference methods that tend to scale best
in the dimension of the CTMC are based on so-called moment closure approximations.
However, there exists a large number of different moment closure approximations
and it is typically hard to say a priori which of the approximations is the most
suitable for the inference procedure. Here, we propose a moment-based parameter
inference method that automatically chooses the most appropriate moment closure
method. Accordingly, contrary to existing methods, the user is not required to
be experienced in moment closure techniques. In addition to that, our method adaptively
changes the approximation during the parameter inference to ensure that always
the best approximation is used, even in cases where different approximations are
best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd
acknowledgement: This work is based on the CMSB 2015 paper “Adaptive moment closure
for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015).
The work was partly supported by the German Research Foundation (DFG) as part of
the Transregional Collaborative Research Center “Automatic Verification and Analysis
of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under
grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23
(RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People
Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme
(FP7/2007-2013) under REA grant agreement no. 291734.
author:
- first_name: Christian
full_name: Schilling, Christian
last_name: Schilling
- first_name: Sergiy
full_name: Bogomolov, Sergiy
id: 369D9A44-F248-11E8-B48F-1D18A9856A87
last_name: Bogomolov
orcid: 0000-0002-0686-0365
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Andreas
full_name: Podelski, Andreas
last_name: Podelski
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment
closure for parameter inference of biochemical reaction networks. Biosystems.
2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005
apa: Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess,
J. (2016). Adaptive moment closure for parameter inference of biochemical reaction
networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005
chicago: Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski,
and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical
Reaction Networks.” Biosystems. Elsevier, 2016. https://doi.org/10.1016/j.biosystems.2016.07.005.
ieee: C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive
moment closure for parameter inference of biochemical reaction networks,” Biosystems,
vol. 149. Elsevier, pp. 15–25, 2016.
ista: Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive
moment closure for parameter inference of biochemical reaction networks. Biosystems.
149, 15–25.
mla: Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference
of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016,
pp. 15–25, doi:10.1016/j.biosystems.2016.07.005.
short: C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems
149 (2016) 15–25.
date_created: 2018-12-11T11:50:24Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2023-02-23T10:08:46Z
day: '01'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1016/j.biosystems.2016.07.005
ec_funded: 1
intvolume: ' 149'
language:
- iso: eng
month: '11'
oa_version: None
page: 15 - 25
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Biosystems
publication_status: published
publisher: Elsevier
publist_id: '6210'
quality_controlled: '1'
related_material:
record:
- id: '1658'
relation: earlier_version
status: public
scopus_import: 1
status: public
title: Adaptive moment closure for parameter inference of biochemical reaction networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 149
year: '2016'
...
---
_id: '1170'
abstract:
- lang: eng
text: The increasing complexity of dynamic models in systems and synthetic biology
poses computational challenges especially for the identification of model parameters.
While modularization of the corresponding optimization problems could help reduce
the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit
a simple decomposition of most biomolecular networks into subnetworks, or modules.
Drawing on ideas from network modularization and multiple-shooting optimization,
we present here a modular parameter identification approach that explicitly allows
for such interdependencies. Interfaces between our modules are given by the experimentally
measured molecular species. This definition allows deriving good (initial) estimates
for the inter-module communication directly from the experimental data. Given
these estimates, the states and parameter sensitivities of different modules can
be integrated independently. To achieve consistency between modules, we iteratively
adjust the estimates for inter-module communication while optimizing the parameters.
After convergence to an optimal parameter set---but not during earlier iterations---the
intermodule communication as well as the individual modules\' state dynamics agree
with the dynamics of the nonmodularized network. Our modular parameter identification
approach allows for easy parallelization; it can reduce the computational complexity
for larger networks and decrease the probability to converge to suboptimal local
minima. We demonstrate the algorithm\'s performance in parameter estimation for
two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling
pathway.
author:
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Jörg
full_name: Stelling, Jörg
last_name: Stelling
citation:
ama: Lang M, Stelling J. Modular parameter identification of biomolecular networks.
SIAM Journal on Scientific Computing. 2016;38(6):B988-B1008. doi:10.1137/15M103306X
apa: Lang, M., & Stelling, J. (2016). Modular parameter identification of biomolecular
networks. SIAM Journal on Scientific Computing. Society for Industrial
and Applied Mathematics . https://doi.org/10.1137/15M103306X
chicago: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing. Society for Industrial
and Applied Mathematics , 2016. https://doi.org/10.1137/15M103306X.
ieee: M. Lang and J. Stelling, “Modular parameter identification of biomolecular
networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society
for Industrial and Applied Mathematics , pp. B988–B1008, 2016.
ista: Lang M, Stelling J. 2016. Modular parameter identification of biomolecular
networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008.
mla: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing, vol. 38, no. 6, Society
for Industrial and Applied Mathematics , 2016, pp. B988–1008, doi:10.1137/15M103306X.
short: M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.
date_created: 2018-12-11T11:50:31Z
date_published: 2016-11-15T00:00:00Z
date_updated: 2021-01-12T06:48:49Z
day: '15'
ddc:
- '003'
- '518'
- '570'
- '621'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1137/15M103306X
file:
- access_level: local
checksum: 781bc3ffd30b2dd65b7727c5a285fc78
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:41Z
date_updated: 2020-07-14T12:44:37Z
file_id: '5095'
file_name: IST-2017-811-v1+1_modular_parameter_identification.pdf
file_size: 871964
relation: main_file
file_date_updated: 2020-07-14T12:44:37Z
has_accepted_license: '1'
intvolume: ' 38'
issue: '6'
language:
- iso: eng
month: '11'
oa_version: Submitted Version
page: B988 - B1008
publication: SIAM Journal on Scientific Computing
publication_status: published
publisher: 'Society for Industrial and Applied Mathematics '
publist_id: '6186'
pubrep_id: '811'
quality_controlled: '1'
scopus_import: 1
status: public
title: Modular parameter identification of biomolecular networks
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2016'
...
---
_id: '1171'
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: 'Tkačik G. Understanding regulatory networks requires more than computing a
multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. 2016;17:166-167. doi:10.1016/j.plrev.2016.06.005'
apa: 'Tkačik, G. (2016). Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. Elsevier. https://doi.org/10.1016/j.plrev.2016.06.005'
chicago: 'Tkačik, Gašper. “Understanding Regulatory Networks Requires More than
Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural
Features in Gene Regulatory Networks: From Biophysical Constraints to Biological
Function" by O. C. Martin et Al.” Physics of Life Reviews. Elsevier,
2016. https://doi.org/10.1016/j.plrev.2016.06.005.'
ieee: 'G. Tkačik, “Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al.,” Physics of Life Reviews, vol. 17. Elsevier, pp.
166–167, 2016.'
ista: 'Tkačik G. 2016. Understanding regulatory networks requires more than computing
a multitude of graph statistics: Comment on "Drivers of structural features
in gene regulatory networks: From biophysical constraints to biological function"
by O. C. Martin et al. Physics of Life Reviews. 17, 166–167.'
mla: 'Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing
a Multitude of Graph Statistics: Comment on "Drivers of Structural Features
in Gene Regulatory Networks: From Biophysical Constraints to Biological Function"
by O. C. Martin et Al.” Physics of Life Reviews, vol. 17, Elsevier, 2016,
pp. 166–67, doi:10.1016/j.plrev.2016.06.005.'
short: G. Tkačik, Physics of Life Reviews 17 (2016) 166–167.
date_created: 2018-12-11T11:50:32Z
date_published: 2016-07-01T00:00:00Z
date_updated: 2021-01-12T06:48:50Z
day: '01'
department:
- _id: GaTk
doi: 10.1016/j.plrev.2016.06.005
intvolume: ' 17'
language:
- iso: eng
month: '07'
oa_version: None
page: 166 - 167
publication: Physics of Life Reviews
publication_status: published
publisher: Elsevier
publist_id: '6185'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Understanding regulatory networks requires more than computing a multitude
of graph statistics: Comment on "Drivers of structural features in gene regulatory
networks: From biophysical constraints to biological function" by O. C. Martin
et al.'
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2016'
...
---
_id: '1188'
abstract:
- lang: eng
text: "We consider a population dynamics model coupling cell growth to a diffusion
in the space of metabolic phenotypes as it can be obtained from realistic constraints-based
modelling. \r\nIn the asymptotic regime of slow\r\ndiffusion, that coincides with
the relevant experimental range, the resulting\r\nnon-linear Fokker–Planck equation
is solved for the steady state in the WKB\r\napproximation that maps it into the
ground state of a quantum particle in an\r\nAiry potential plus a centrifugal
term. We retrieve scaling laws for growth rate\r\nfluctuations and time response
with respect to the distance from the maximum\r\ngrowth rate suggesting that suboptimal
populations can have a faster response\r\nto perturbations."
acknowledgement: D De Martino is supported by the People Programme (Marie Curie Actions)
of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant
agreement no. [291734]. D Masoero is supported by the FCT scholarship, number SFRH/BPD/75908/2011.
D De Martino thanks the Grupo de Física Matemática of the Universidade de Lisboa
for the kind hospitality. We also wish to thank Matteo Osella, Vincenzo Vitagliano
and Vera Luz Masoero for useful discussions, also late at night.
article_number: '123502'
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Davide
full_name: Masoero, Davide
last_name: Masoero
citation:
ama: 'De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth. Journal of Statistical Mechanics:
Theory and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f'
apa: 'De Martino, D., & Masoero, D. (2016). Asymptotic analysis of noisy fitness
maximization, applied to metabolism & growth. Journal of Statistical
Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa4e8f'
chicago: 'De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy
Fitness Maximization, Applied to Metabolism & Growth.” Journal of
Statistical Mechanics: Theory and Experiment. IOPscience, 2016. https://doi.org/10.1088/1742-5468/aa4e8f.'
ieee: 'D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth,” Journal of Statistical Mechanics:
Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.'
ista: 'De Martino D, Masoero D. 2016. Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth. Journal of Statistical Mechanics: Theory
and Experiment. 2016(12), 123502.'
mla: 'De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness
Maximization, Applied to Metabolism & Growth.” Journal of Statistical
Mechanics: Theory and Experiment, vol. 2016, no. 12, 123502, IOPscience, 2016,
doi:10.1088/1742-5468/aa4e8f.'
short: 'D. De Martino, D. Masoero, Journal of Statistical Mechanics: Theory and
Experiment 2016 (2016).'
date_created: 2018-12-11T11:50:37Z
date_published: 2016-12-30T00:00:00Z
date_updated: 2021-01-12T06:48:57Z
day: '30'
department:
- _id: GaTk
doi: 10.1088/1742-5468/aa4e8f
ec_funded: 1
intvolume: ' 2016'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1606.09048
month: '12'
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: ' Journal of Statistical Mechanics: Theory and Experiment'
publication_status: published
publisher: IOPscience
publist_id: '6165'
quality_controlled: '1'
scopus_import: 1
status: public
title: Asymptotic analysis of noisy fitness maximization, applied to metabolism &
growth
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016
year: '2016'
...
---
_id: '1197'
abstract:
- lang: eng
text: Across the nervous system, certain population spiking patterns are observed
far more frequently than others. A hypothesis about this structure is that these
collective activity patterns function as population codewords–collective modes–carrying
information distinct from that of any single cell. We investigate this phenomenon
in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop
a novel statistical model that decomposes the population response into modes;
it predicts the distribution of spiking activity in the ganglion cell population
with high accuracy. We found that the modes represent localized features of the
visual stimulus that are distinct from the features represented by single neurons.
Modes form clusters of activity states that are readily discriminated from one
another. When we repeated the same visual stimulus, we found that the same mode
was robustly elicited. These results suggest that retinal ganglion cells’ collective
signaling is endowed with a form of error-correcting code–a principle that may
hold in brain areas beyond retina.
acknowledgement: JSP was supported by a C.V. Starr Fellowship from the Starr Foundation
(http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation
(https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National
Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science
Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler
for helpful comments on the manuscript.
article_number: e1005855
author:
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Mark
full_name: Ioffe, Mark
last_name: Ioffe
- first_name: Adrianna
full_name: Loback, Adrianna
last_name: Loback
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes
of the retinal population code. PLoS Computational Biology. 2016;12(11).
doi:10.1371/journal.pcbi.1005148
apa: Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M.
(2016). Error-robust modes of the retinal population code. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148
chicago: Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik,
and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS
Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148.
ieee: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust
modes of the retinal population code,” PLoS Computational Biology, vol.
12, no. 11. Public Library of Science, 2016.
ista: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust
modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.
mla: Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.”
PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of
Science, 2016, doi:10.1371/journal.pcbi.1005148.
short: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational
Biology 12 (2016).
date_created: 2018-12-11T11:50:40Z
date_published: 2016-11-17T00:00:00Z
date_updated: 2023-02-23T14:05:40Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005148
file:
- access_level: open_access
checksum: 47b08cbd4dbf32b25ba161f5f4b262cc
content_type: application/pdf
creator: kschuh
date_created: 2019-01-25T10:35:00Z
date_updated: 2020-07-14T12:44:38Z
file_id: '5884'
file_name: 2016_PLOS_Prentice.pdf
file_size: 4492021
relation: main_file
file_date_updated: 2020-07-14T12:44:38Z
has_accepted_license: '1'
intvolume: ' 12'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _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_status: published
publisher: Public Library of Science
publist_id: '6153'
quality_controlled: '1'
related_material:
record:
- id: '9709'
relation: research_data
status: public
scopus_import: 1
status: public
title: Error-robust modes of the retinal population code
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: 12
year: '2016'
...
---
_id: '1203'
abstract:
- lang: eng
text: Haemophilus haemolyticus has been recently discovered to have the potential
to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae
(NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified
because none of the existing tests targeting the known phenotypes of H. haemolyticus
are able to specifically identify H. haemolyticus. Through comparative genomic
analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to
H. haemolyticus that can be used as targets for the identification of H. haemolyticus.
A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase
2 in the purine synthesis pathway) was developed and evaluated. The lower limit
of detection was 40 genomes/PCR; the sensitivity and specificity in detecting
H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination
of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets
(H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was
also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification
of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification
of H. influenzae, respectively. The dual-target testing scheme can be used for
the diagnosis and surveillance of infection and disease caused by H. haemolyticus
and NT H. influenzae.
acknowledgement: We are grateful to ABCs for providing strains and the Bacterial Meningitis
Laboratory for technical support.
author:
- first_name: Fang
full_name: Hu, Fang
last_name: Hu
- first_name: Lavanya
full_name: Rishishwar, Lavanya
last_name: Rishishwar
- first_name: Ambily
full_name: Sivadas, Ambily
last_name: Sivadas
- first_name: Gabriel
full_name: Mitchell, Gabriel
id: 315BCD80-F248-11E8-B48F-1D18A9856A87
last_name: Mitchell
- first_name: Jordan
full_name: King, Jordan
last_name: King
- first_name: Timothy
full_name: Murphy, Timothy
last_name: Murphy
- first_name: Janet
full_name: Gilsdorf, Janet
last_name: Gilsdorf
- first_name: Leonard
full_name: Mayer, Leonard
last_name: Mayer
- first_name: Xin
full_name: Wang, Xin
last_name: Wang
citation:
ama: Hu F, Rishishwar L, Sivadas A, et al. Comparative genomic analysis of Haemophilus
haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for
their discrimination. Journal of Clinical Microbiology. 2016;54(12):3010-3017.
doi:10.1128/JCM.01511-16
apa: Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T., …
Wang, X. (2016). Comparative genomic analysis of Haemophilus haemolyticus and
nontypeable Haemophilus influenzae and a new testing scheme for their discrimination.
Journal of Clinical Microbiology. American Society for Microbiology. https://doi.org/10.1128/JCM.01511-16
chicago: Hu, Fang, Lavanya Rishishwar, Ambily Sivadas, Gabriel Mitchell, Jordan
King, Timothy Murphy, Janet Gilsdorf, Leonard Mayer, and Xin Wang. “Comparative
Genomic Analysis of Haemophilus Haemolyticus and Nontypeable Haemophilus Influenzae
and a New Testing Scheme for Their Discrimination.” Journal of Clinical Microbiology.
American Society for Microbiology, 2016. https://doi.org/10.1128/JCM.01511-16.
ieee: F. Hu et al., “Comparative genomic analysis of Haemophilus haemolyticus
and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination,”
Journal of Clinical Microbiology, vol. 54, no. 12. American Society for
Microbiology, pp. 3010–3017, 2016.
ista: Hu F, Rishishwar L, Sivadas A, Mitchell G, King J, Murphy T, Gilsdorf J, Mayer
L, Wang X. 2016. Comparative genomic analysis of Haemophilus haemolyticus and
nontypeable Haemophilus influenzae and a new testing scheme for their discrimination.
Journal of Clinical Microbiology. 54(12), 3010–3017.
mla: Hu, Fang, et al. “Comparative Genomic Analysis of Haemophilus Haemolyticus
and Nontypeable Haemophilus Influenzae and a New Testing Scheme for Their Discrimination.”
Journal of Clinical Microbiology, vol. 54, no. 12, American Society for
Microbiology, 2016, pp. 3010–17, doi:10.1128/JCM.01511-16.
short: F. Hu, L. Rishishwar, A. Sivadas, G. Mitchell, J. King, T. Murphy, J. Gilsdorf,
L. Mayer, X. Wang, Journal of Clinical Microbiology 54 (2016) 3010–3017.
date_created: 2018-12-11T11:50:41Z
date_published: 2016-12-01T00:00:00Z
date_updated: 2021-01-12T06:49:04Z
day: '01'
department:
- _id: GaTk
doi: 10.1128/JCM.01511-16
intvolume: ' 54'
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5121393/
month: '12'
oa: 1
oa_version: Submitted Version
page: 3010 - 3017
publication: Journal of Clinical Microbiology
publication_status: published
publisher: American Society for Microbiology
publist_id: '6146'
quality_controlled: '1'
scopus_import: 1
status: public
title: Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus
influenzae and a new testing scheme for their discrimination
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 54
year: '2016'
...
---
_id: '1214'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. While very successful with classical robots, these methods run into
severe difficulties when applied to soft robots, a new field of robotics with
large interest for human-robot interaction. We claim that a novel controller paradigm
opens new perspective for this field. This paper applies a recently developed
neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon
driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we
observe a vast variety of self-organized behavior patterns: when left alone, the
arm realizes pseudo-random sequences of different poses. By applying physical
forces, the system can be entrained into definite motion patterns like wiping
a table. Most interestingly, after attaching an object, the controller gets in
a functional resonance with the object''s internal dynamics, starting to shake
spontaneously bottles half-filled with water or sensitively driving an attached
pendulum into a circular mode. When attached to the crank of a wheel the neural
system independently develops to rotate it. In this way, the robot discovers affordances
of objects its body is interacting with.'
acknowledgement: RD thanks for the hospitality at the Max-Planck-Institute and for
helpful discussions with Nihat Ay and Keyan Zahedi.
article_number: '7759138'
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Raphael
full_name: Hostettler, Raphael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots:
Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November.
IEEE; 2016. doi:10.1109/IROS.2016.7759138'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant
control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on
Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138'
chicago: 'Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant
Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic
Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.'
ieee: 'G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for
soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented
at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS
, Daejeon, Korea, 2016, vol. 2016–November.'
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft
robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International
Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.'
mla: 'Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior
of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE,
2016, doi:10.1109/IROS.2016.7759138.'
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.
conference:
end_date: 2016-09-14
location: Daejeon, Korea
name: 'IEEE RSJ International Conference on Intelligent Robots and Systems IROS '
start_date: 2016-09-09
date_created: 2018-12-11T11:50:45Z
date_published: 2016-11-28T00:00:00Z
date_updated: 2021-01-12T06:49:08Z
day: '28'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/IROS.2016.7759138
language:
- iso: eng
month: '11'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '6121'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-November
year: '2016'
...
---
_id: '1538'
abstract:
- lang: eng
text: Systems biology rests on the idea that biological complexity can be better
unraveled through the interplay of modeling and experimentation. However, the
success of this approach depends critically on the informativeness of the chosen
experiments, which is usually unknown a priori. Here, we propose a systematic
scheme based on iterations of optimal experiment design, flow cytometry experiments,
and Bayesian parameter inference to guide the discovery process in the case of
stochastic biochemical reaction networks. To illustrate the benefit of our methodology,
we apply it to the characterization of an engineered light-inducible gene expression
circuit in yeast and compare the performance of the resulting model with models
identified from nonoptimal experiments. In particular, we compare the parameter
posterior distributions and the precision to which the outcome of future experiments
can be predicted. Moreover, we illustrate how the identified stochastic model
can be used to determine light induction patterns that make either the average
amount of protein or the variability in a population of cells follow a desired
profile. Our results show that optimal experiment design allows one to derive
models that are accurate enough to precisely predict and regulate the protein
expression in heterogeneous cell populations over extended periods of time.
acknowledgement: 'J.R., F.P., and J.L. acknowledge support from the European Commission
under the Network of Excellence HYCON2 (highly-complex and networked control systems)
and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People
Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme
FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges
support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). '
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Francesca
full_name: Parise, Francesca
last_name: Parise
- first_name: Andreas
full_name: Milias Argeitis, Andreas
last_name: Milias Argeitis
- first_name: Mustafa
full_name: Khammash, Mustafa
last_name: Khammash
- first_name: John
full_name: Lygeros, John
last_name: Lygeros
citation:
ama: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment
design guides the characterization of a light-inducible gene expression circuit.
PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112
apa: Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J.
(2015). Iterative experiment design guides the characterization of a light-inducible
gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112
chicago: Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash,
and John Lygeros. “Iterative Experiment Design Guides the Characterization of
a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1423947112.
ieee: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153,
2015.
ista: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit. PNAS. 112(26), 8148–8153.
mla: Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization
of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26,
National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112.
short: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112
(2015) 8148–8153.
date_created: 2018-12-11T11:52:36Z
date_published: 2015-06-30T00:00:00Z
date_updated: 2021-01-12T06:51:27Z
day: '30'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1073/pnas.1423947112
ec_funded: 1
external_id:
pmid:
- '26085136'
intvolume: ' 112'
issue: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/
month: '06'
oa: 1
oa_version: Submitted Version
page: 8148 - 8153
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5633'
quality_controlled: '1'
scopus_import: 1
status: public
title: Iterative experiment design guides the characterization of a light-inducible
gene expression circuit
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1539'
abstract:
- lang: eng
text: 'Many stochastic models of biochemical reaction networks contain some chemical
species for which the number of molecules that are present in the system can only
be finite (for instance due to conservation laws), but also other species that
can be present in arbitrarily large amounts. The prime example of such networks
are models of gene expression, which typically contain a small and finite number
of possible states for the promoter but an infinite number of possible states
for the amount of mRNA and protein. One of the main approaches to analyze such
models is through the use of equations for the time evolution of moments of the
chemical species. Recently, a new approach based on conditional moments of the
species with infinite state space given all the different possible states of the
finite species has been proposed. It was argued that this approach allows one
to capture more details about the full underlying probability distribution with
a smaller number of equations. Here, I show that the result that less moments
provide more information can only stem from an unnecessarily complicated description
of the system in the classical formulation. The foundation of this argument will
be the derivation of moment equations that describe the complete probability distribution
over the finite state space but only low-order moments over the infinite state
space. I will show that the number of equations that is needed is always less
than what was previously claimed and always less than the number of conditional
moment equations up to the same order. To support these arguments, a symbolic
algorithm is provided that can be used to derive minimal systems of unconditional
moment equations for models with partially finite state space. '
article_number: '244103'
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Ruess J. Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space. Journal of Chemical Physics.
2015;143(24). doi:10.1063/1.4937937
apa: Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
American Institute of Physics. https://doi.org/10.1063/1.4937937
chicago: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics.
American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937.
ieee: J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space,” Journal of Chemical Physics,
vol. 143, no. 24. American Institute of Physics, 2015.
ista: Ruess J. 2015. Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
143(24), 244103.
mla: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics,
vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937.
short: J. Ruess, Journal of Chemical Physics 143 (2015).
date_created: 2018-12-11T11:52:36Z
date_published: 2015-12-22T00:00:00Z
date_updated: 2021-01-12T06:51:28Z
day: '22'
ddc:
- '000'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1063/1.4937937
ec_funded: 1
file:
- access_level: open_access
checksum: 838657118ae286463a2b7737319f35ce
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:07:43Z
date_updated: 2020-07-14T12:45:01Z
file_id: '4641'
file_name: IST-2016-593-v1+1_Minimal_moment_equations.pdf
file_size: 605355
relation: main_file
file_date_updated: 2020-07-14T12:45:01Z
has_accepted_license: '1'
intvolume: ' 143'
issue: '24'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Journal of Chemical Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5632'
pubrep_id: '593'
quality_controlled: '1'
scopus_import: 1
status: public
title: Minimal moment equations for stochastic models of biochemical reaction networks
with partially finite state space
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 143
year: '2015'
...
---
_id: '1564'
article_number: '145'
author:
- first_name: Matthieu
full_name: Gilson, Matthieu
last_name: Gilson
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
citation:
ama: 'Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the
interaction of different forms of plasticity. Frontiers in Computational Neuroscience.
2015;9(11). doi:10.3389/fncom.2015.00145'
apa: 'Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural
computation from the interaction of different forms of plasticity. Frontiers
in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145'
chicago: 'Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent
Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers
in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.'
ieee: 'M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation
from the interaction of different forms of plasticity,” Frontiers in Computational
Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.'
ista: 'Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation
from the interaction of different forms of plasticity. Frontiers in Computational
Neuroscience. 9(11), 145.'
mla: 'Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the
Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience,
vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.'
short: M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9
(2015).
date_created: 2018-12-11T11:52:45Z
date_published: 2015-11-30T00:00:00Z
date_updated: 2021-01-12T06:51:37Z
day: '30'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.3389/fncom.2015.00145
ec_funded: 1
file:
- access_level: open_access
checksum: cea73b6d3ef1579f32da10b82f4de4fd
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:09Z
date_updated: 2020-07-14T12:45:02Z
file_id: '4927'
file_name: IST-2016-479-v1+1_fncom-09-00145.pdf
file_size: 187038
relation: main_file
file_date_updated: 2020-07-14T12:45:02Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Frontiers in Computational Neuroscience
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '5607'
pubrep_id: '479'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Editorial: Emergent neural computation from the interaction of different forms
of plasticity'
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: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, self-organized behavioral development provides
more questions than answers. Are there special functional units for curiosity,
motivation, and creativity? This paper argues that these features can be grounded
in synaptic plasticity itself, without requiring any higher-level constructs.
We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
self-learning systems and apply it to a number of complex robotic systems as a
test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
pmid:
- '26504200'
intvolume: ' 112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...