---
_id: '14515'
abstract:
- lang: eng
text: Most natural and engineered information-processing systems transmit information
via signals that vary in time. Computing the information transmission rate or
the information encoded in the temporal characteristics of these signals requires
the mutual information between the input and output signals as a function of time,
i.e., between the input and output trajectories. Yet, this is notoriously difficult
because of the high-dimensional nature of the trajectory space, and all existing
techniques require approximations. We present an exact Monte Carlo technique called
path weight sampling (PWS) that, for the first time, makes it possible to compute
the mutual information between input and output trajectories for any stochastic
system that is described by a master equation. The principal idea is to use the
master equation to evaluate the exact conditional probability of an individual
output trajectory for a given input trajectory and average this via Monte Carlo
sampling in trajectory space to obtain the mutual information. We present three
variants of PWS, which all generate the trajectories using the standard stochastic
simulation algorithm. While direct PWS is a brute-force method, Rosenbluth-Rosenbluth
PWS exploits the analogy between signal trajectory sampling and polymer sampling,
and thermodynamic integration PWS is based on a reversible work calculation in
trajectory space. PWS also makes it possible to compute the mutual information
between input and output trajectories for systems with hidden internal states
as well as systems with feedback from output to input. Applying PWS to the bacterial
chemotaxis system, consisting of 182 coupled chemical reactions, demonstrates
not only that the scheme is highly efficient but also that the number of receptor
clusters is much smaller than hitherto believed, while their size is much larger.
acknowledgement: "We thank Bela Mulder, Tom Shimizu, Fotios Avgidis, Peter Bolhuis,
and Daan Frenkel for useful discussions and a careful reading of the manuscript,
and we thank Age Tjalma for support with obtaining the Gaussian approximation of
the chemotaxis system. This work is part of the Dutch Research Council (NWO) and
was performed at the research institute AMOLF. This project has received funding
from the European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation program (Grant Agreement No. 885065) and was\r\nfinancially
supported by NWO through the “Building a Synthetic Cell (BaSyC)” Gravitation Grant
(024.003.019)."
article_number: '041017'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Manuel
full_name: Reinhardt, Manuel
last_name: Reinhardt
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Pieter Rein
full_name: Ten Wolde, Pieter Rein
last_name: Ten Wolde
citation:
ama: 'Reinhardt M, Tkačik G, Ten Wolde PR. Path weight sampling: Exact Monte Carlo
computation of the mutual information between stochastic trajectories. Physical
Review X. 2023;13(4). doi:10.1103/PhysRevX.13.041017'
apa: 'Reinhardt, M., Tkačik, G., & Ten Wolde, P. R. (2023). Path weight sampling:
Exact Monte Carlo computation of the mutual information between stochastic trajectories.
Physical Review X. American Physical Society. https://doi.org/10.1103/PhysRevX.13.041017'
chicago: 'Reinhardt, Manuel, Gašper Tkačik, and Pieter Rein Ten Wolde. “Path Weight
Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic
Trajectories.” Physical Review X. American Physical Society, 2023. https://doi.org/10.1103/PhysRevX.13.041017.'
ieee: 'M. Reinhardt, G. Tkačik, and P. R. Ten Wolde, “Path weight sampling: Exact
Monte Carlo computation of the mutual information between stochastic trajectories,”
Physical Review X, vol. 13, no. 4. American Physical Society, 2023.'
ista: 'Reinhardt M, Tkačik G, Ten Wolde PR. 2023. Path weight sampling: Exact Monte
Carlo computation of the mutual information between stochastic trajectories. Physical
Review X. 13(4), 041017.'
mla: 'Reinhardt, Manuel, et al. “Path Weight Sampling: Exact Monte Carlo Computation
of the Mutual Information between Stochastic Trajectories.” Physical Review
X, vol. 13, no. 4, 041017, American Physical Society, 2023, doi:10.1103/PhysRevX.13.041017.'
short: M. Reinhardt, G. Tkačik, P.R. Ten Wolde, Physical Review X 13 (2023).
date_created: 2023-11-12T23:00:55Z
date_published: 2023-10-26T00:00:00Z
date_updated: 2023-11-13T09:03:30Z
day: '26'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1103/PhysRevX.13.041017
external_id:
arxiv:
- '2203.03461'
file:
- access_level: open_access
checksum: 32574aeebcca7347a4152c611b66b3d5
content_type: application/pdf
creator: dernst
date_created: 2023-11-13T09:00:19Z
date_updated: 2023-11-13T09:00:19Z
file_id: '14522'
file_name: 2023_PhysReviewX_Reinhardt.pdf
file_size: 1595223
relation: main_file
success: 1
file_date_updated: 2023-11-13T09:00:19Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '4'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
publication: Physical Review X
publication_identifier:
eissn:
- 2160-3308
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Path weight sampling: Exact Monte Carlo computation of the mutual information
between stochastic trajectories'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2023'
...
---
_id: '14656'
abstract:
- lang: eng
text: Although much is known about how single neurons in the hippocampus represent
an animal's position, how circuit interactions contribute to spatial coding is
less well understood. Using a novel statistical estimator and theoretical modeling,
both developed in the framework of maximum entropy models, we reveal highly structured
CA1 cell-cell interactions in male rats during open field exploration. The statistics
of these interactions depend on whether the animal is in a familiar or novel environment.
In both conditions the circuit interactions optimize the encoding of spatial information,
but for regimes that differ in the informativeness of their spatial inputs. This
structure facilitates linear decodability, making the information easy to read
out by downstream circuits. Overall, our findings suggest that the efficient coding
hypothesis is not only applicable to individual neuron properties in the sensory
periphery, but also to neural interactions in the central brain.
acknowledgement: M.N. was supported by the European Union Horizon 2020 Grant 665385.
J.C. was supported by the European Research Council Consolidator Grant 281511. G.T.
was supported by the Austrian Science Fund (FWF) Grant P34015. C.S. was supported
by an Institute of Science and Technology fellow award and by the National Science
Foundation (NSF) Award No. 1922658. We thank Peter Baracskay, Karola Kaefer, and
Hugo Malagon-Vina for the acquisition of the data. We also thank Federico Stella,
Wiktor Młynarski, Dori Derdikman, Colin Bredenberg, Roman Huszar, Heloisa Chiossi,
Lorenzo Posani, and Mohamady El-Gaby for comments on an earlier version of the manuscript.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Michele
full_name: Nardin, Michele
id: 30BD0376-F248-11E8-B48F-1D18A9856A87
last_name: Nardin
orcid: 0000-0001-8849-6570
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
citation:
ama: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1
interactions optimizes spatial coding across experience. The Journal of Neuroscience.
2023;43(48):8140-8156. doi:10.1523/JNEUROSCI.0194-23.2023
apa: Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (2023). The structure
of hippocampal CA1 interactions optimizes spatial coding across experience. The
Journal of Neuroscience. Society of Neuroscience. https://doi.org/10.1523/JNEUROSCI.0194-23.2023
chicago: Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin.
“The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across
Experience.” The Journal of Neuroscience. Society of Neuroscience, 2023.
https://doi.org/10.1523/JNEUROSCI.0194-23.2023.
ieee: M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal
CA1 interactions optimizes spatial coding across experience,” The Journal of
Neuroscience, vol. 43, no. 48. Society of Neuroscience, pp. 8140–8156, 2023.
ista: Nardin M, Csicsvari JL, Tkačik G, Savin C. 2023. The structure of hippocampal
CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience.
43(48), 8140–8156.
mla: Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes
Spatial Coding across Experience.” The Journal of Neuroscience, vol. 43,
no. 48, Society of Neuroscience, 2023, pp. 8140–56, doi:10.1523/JNEUROSCI.0194-23.2023.
short: M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, The Journal of Neuroscience
43 (2023) 8140–8156.
date_created: 2023-12-10T23:00:58Z
date_published: 2023-11-29T00:00:00Z
date_updated: 2023-12-11T11:37:20Z
day: '29'
ddc:
- '570'
department:
- _id: JoCs
- _id: GaTk
doi: 10.1523/JNEUROSCI.0194-23.2023
ec_funded: 1
external_id:
pmid:
- '37758476'
file:
- access_level: closed
checksum: e2503c8f84be1050e28f64320f1d5bd2
content_type: application/pdf
creator: dernst
date_created: 2023-12-11T11:30:37Z
date_updated: 2023-12-11T11:30:37Z
embargo: 2024-06-01
embargo_to: open_access
file_id: '14674'
file_name: 2023_JourNeuroscience_Nardin.pdf
file_size: 2280632
relation: main_file
file_date_updated: 2023-12-11T11:30:37Z
has_accepted_license: '1'
intvolume: ' 43'
issue: '48'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1523/JNEUROSCI.0194-23.2023
month: '11'
oa: 1
oa_version: Published Version
page: 8140-8156
pmid: 1
project:
- _id: 257A4776-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '281511'
name: Memory-related information processing in neuronal circuits of the hippocampus
and entorhinal cortex
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: The Journal of Neuroscience
publication_identifier:
eissn:
- 1529-2401
publication_status: published
publisher: Society of Neuroscience
quality_controlled: '1'
scopus_import: '1'
status: public
title: The structure of hippocampal CA1 interactions optimizes spatial coding across
experience
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: 43
year: '2023'
...
---
_id: '13127'
abstract:
- lang: eng
text: Cooperative disease defense emerges as group-level collective behavior, yet
how group members make the underlying individual decisions is poorly understood.
Using garden ants and fungal pathogens as an experimental model, we derive the
rules governing individual ant grooming choices and show how they produce colony-level
hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic
modeling reveal that ants increase grooming and preferentially target highly-infectious
individuals when perceiving high pathogen load, but transiently suppress grooming
after having been groomed by nestmates. Ants thus react to both, the infectivity
of others and the social feedback they receive on their own contagiousness. While
inferred solely from momentary ant decisions, these behavioral rules quantitatively
predict hour-long experimental dynamics, and synergistically combine into efficient
colony-wide pathogen removal. Our analyses show that noisy individual decisions
based on only local, incomplete, yet dynamically-updated information on pathogen
threat and social feedback can lead to potent collective disease defense.
acknowledged_ssus:
- _id: LifeSc
acknowledgement: We thank Mike Bidochka for the fungal strains, the ISTA Social Immunity
Team for ant collection, Hanna Leitner for experimental and molecular support, Jennifer
Robb and Lukas Lindorfer for microscopy, and the LabSupport Facility at ISTA for
general laboratory support. We further thank Victor Mireles, Iain Couzin, Fabian
Theis and the Social Immunity Team for continued feedback throughout, and Michael
Sixt, Yuko Ulrich, Koos Boomsma, Erika Dawson, Megan Kutzer and Hinrich Schulenburg
for comments on the manuscript. This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
program (Grant No. 771402; EPIDEMICSonCHIP) to SC, from the Scientific Grant Agency
of the Slovak Republic (Grant No. 1/0521/20) to KB, and the Human Frontier Science
Program (Grant No. RGP0065/2012) to GT.
article_number: '3232'
article_processing_charge: Yes
article_type: original
author:
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- 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: Anna V
full_name: Grasse, Anna V
id: 406F989C-F248-11E8-B48F-1D18A9856A87
last_name: Grasse
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
citation:
ama: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. Dynamic pathogen
detection and social feedback shape collective hygiene in ants. Nature Communications.
2023;14. doi:10.1038/s41467-023-38947-y
apa: Casillas Perez, B. E., Bodova, K., Grasse, A. V., Tkačik, G., & Cremer,
S. (2023). Dynamic pathogen detection and social feedback shape collective hygiene
in ants. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-38947-y
chicago: Casillas Perez, Barbara E, Katarina Bodova, Anna V Grasse, Gašper Tkačik,
and Sylvia Cremer. “Dynamic Pathogen Detection and Social Feedback Shape Collective
Hygiene in Ants.” Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-38947-y.
ieee: B. E. Casillas Perez, K. Bodova, A. V. Grasse, G. Tkačik, and S. Cremer, “Dynamic
pathogen detection and social feedback shape collective hygiene in ants,” Nature
Communications, vol. 14. Springer Nature, 2023.
ista: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. 2023. Dynamic
pathogen detection and social feedback shape collective hygiene in ants. Nature
Communications. 14, 3232.
mla: Casillas Perez, Barbara E., et al. “Dynamic Pathogen Detection and Social Feedback
Shape Collective Hygiene in Ants.” Nature Communications, vol. 14, 3232,
Springer Nature, 2023, doi:10.1038/s41467-023-38947-y.
short: B.E. Casillas Perez, K. Bodova, A.V. Grasse, G. Tkačik, S. Cremer, Nature
Communications 14 (2023).
date_created: 2023-06-11T22:00:40Z
date_published: 2023-06-03T00:00:00Z
date_updated: 2023-08-07T13:09:09Z
day: '03'
ddc:
- '570'
department:
- _id: SyCr
- _id: GaTk
doi: 10.1038/s41467-023-38947-y
ec_funded: 1
external_id:
isi:
- '001002562700005'
pmid:
- '37270641'
file:
- access_level: open_access
checksum: 4af0393e3ed47b3fc46e68b81c3c1007
content_type: application/pdf
creator: dernst
date_created: 2023-06-13T08:05:46Z
date_updated: 2023-06-13T08:05:46Z
file_id: '13132'
file_name: 2023_NatureComm_CasillasPerez.pdf
file_size: 2358167
relation: main_file
success: 1
file_date_updated: 2023-06-13T08:05:46Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2649B4DE-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771402'
name: Epidemics in ant societies on a chip
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12945'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Dynamic pathogen detection and social feedback shape collective hygiene in
ants
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: 14
year: '2023'
...
---
_id: '12762'
abstract:
- lang: eng
text: Neurons in the brain are wired into adaptive networks that exhibit collective
dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches.
Although existing models account for oscillations and avalanches separately, they
typically do not explain both phenomena, are too complex to analyze analytically
or intractable to infer from data rigorously. Here we propose a feedback-driven
Ising-like class of neural networks that captures avalanches and oscillations
simultaneously and quantitatively. In the simplest yet fully microscopic model
version, we can analytically compute the phase diagram and make direct contact
with human brain resting-state activity recordings via tractable inference of
the model’s two essential parameters. The inferred model quantitatively captures
the dynamics over a broad range of scales, from single sensor oscillations to
collective behaviors of extreme events and neuronal avalanches. Importantly, the
inferred parameters indicate that the co-existence of scale-specific (oscillations)
and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical
point at the onset of self-sustained oscillations.
acknowledgement: This research was funded in whole, or in part, by the Austrian Science
Fund (FWF) (grant no. PT1013M03318 to F.L. and no. P34015 to G.T.). For the purpose
of open access, the author has applied a CC BY public copyright licence to any Author
Accepted Manuscript version arising from this submission. The study was supported
by the European Union Horizon 2020 research and innovation program under the Marie
Sklodowska-Curie action (grant agreement No. 754411 to F.L.).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Selver
full_name: Pepic, Selver
id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425
last_name: Pepic
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
citation:
ama: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Statistical modeling
of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain. Nature Computational Science. 2023;3:254-263. doi:10.1038/s43588-023-00410-9
apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (2023).
Statistical modeling of adaptive neural networks explains co-existence of avalanches
and oscillations in resting human brain. Nature Computational Science.
Springer Nature. https://doi.org/10.1038/s43588-023-00410-9
chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele
De Martino. “Statistical Modeling of Adaptive Neural Networks Explains Co-Existence
of Avalanches and Oscillations in Resting Human Brain.” Nature Computational
Science. Springer Nature, 2023. https://doi.org/10.1038/s43588-023-00410-9.
ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Statistical
modeling of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain,” Nature Computational Science, vol. 3. Springer
Nature, pp. 254–263, 2023.
ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. 2023. Statistical modeling
of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain. Nature Computational Science. 3, 254–263.
mla: Lombardi, Fabrizio, et al. “Statistical Modeling of Adaptive Neural Networks
Explains Co-Existence of Avalanches and Oscillations in Resting Human Brain.”
Nature Computational Science, vol. 3, Springer Nature, 2023, pp. 254–63,
doi:10.1038/s43588-023-00410-9.
short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, Nature Computational
Science 3 (2023) 254–263.
date_created: 2023-03-26T22:01:08Z
date_published: 2023-03-20T00:00:00Z
date_updated: 2023-08-16T12:41:53Z
day: '20'
ddc:
- '570'
department:
- _id: GaTk
- _id: GradSch
doi: 10.1038/s43588-023-00410-9
ec_funded: 1
external_id:
arxiv:
- '2108.06686'
file:
- access_level: open_access
checksum: 7c63b2b2edfd68aaffe96d70ca6a865a
content_type: application/pdf
creator: dernst
date_created: 2023-08-16T12:39:57Z
date_updated: 2023-08-16T12:39:57Z
file_id: '14073'
file_name: 2023_NatureCompScience_Lombardi.pdf
file_size: 4474284
relation: main_file
success: 1
file_date_updated: 2023-08-16T12:39:57Z
has_accepted_license: '1'
intvolume: ' 3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 254-263
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: eb943429-77a9-11ec-83b8-9f471cdf5c67
grant_number: M03318
name: Functional Advantages of Critical Brain Dynamics
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication: Nature Computational Science
publication_identifier:
eissn:
- 2662-8457
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Statistical modeling of adaptive neural networks explains co-existence of avalanches
and oscillations in resting human brain
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: 3
year: '2023'
...
---
_id: '10736'
abstract:
- lang: eng
text: Predicting function from sequence is a central problem of biology. Currently,
this is possible only locally in a narrow mutational neighborhood around a wildtype
sequence rather than globally from any sequence. Using random mutant libraries,
we developed a biophysical model that accounts for multiple features of σ70 binding
bacterial promoters to predict constitutive gene expression levels from any sequence.
We experimentally and theoretically estimated that 10–20% of random sequences
lead to expression and ~80% of non-expressing sequences are one mutation away
from a functional promoter. The potential for generating expression from random
sequences is so pervasive that selection acts against σ70-RNA polymerase binding
sites even within inter-genic, promoter-containing regions. This pervasiveness
of σ70-binding sites implies that emergence of promoters is not the limiting step
in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter
function into a mechanistic model enabled not only more accurate predictions of
gene expression levels, but also identified that promoters evolve more rapidly
than previously thought.
acknowledgement: 'We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao,
Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on
the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded
by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML;
IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme
7 (2007–2013, grant agreement number 648440) to JPB.'
article_number: e64543
article_processing_charge: No
article_type: original
author:
- first_name: Mato
full_name: Lagator, Mato
id: 345D25EC-F248-11E8-B48F-1D18A9856A87
last_name: Lagator
- first_name: Srdjan
full_name: Sarikas, Srdjan
id: 35F0286E-F248-11E8-B48F-1D18A9856A87
last_name: Sarikas
- first_name: Magdalena
full_name: Steinrueck, Magdalena
last_name: Steinrueck
- first_name: David
full_name: Toledo-Aparicio, David
last_name: Toledo-Aparicio
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function
and evolution from random sequences. eLife. 2022;11. doi:10.7554/eLife.64543
apa: Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J.
P., Guet, C. C., & Tkačik, G. (2022). Predicting bacterial promoter function
and evolution from random sequences. ELife. eLife Sciences Publications.
https://doi.org/10.7554/eLife.64543
chicago: Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio,
Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter
Function and Evolution from Random Sequences.” ELife. eLife Sciences Publications,
2022. https://doi.org/10.7554/eLife.64543.
ieee: M. Lagator et al., “Predicting bacterial promoter function and evolution
from random sequences,” eLife, vol. 11. eLife Sciences Publications, 2022.
ista: Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC,
Tkačik G. 2022. Predicting bacterial promoter function and evolution from random
sequences. eLife. 11, e64543.
mla: Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution
from Random Sequences.” ELife, vol. 11, e64543, eLife Sciences Publications,
2022, doi:10.7554/eLife.64543.
short: M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback,
C.C. Guet, G. Tkačik, ELife 11 (2022).
date_created: 2022-02-06T23:01:32Z
date_published: 2022-01-26T00:00:00Z
date_updated: 2023-08-02T14:09:02Z
day: '26'
ddc:
- '576'
department:
- _id: CaGu
- _id: GaTk
- _id: NiBa
doi: 10.7554/eLife.64543
ec_funded: 1
external_id:
isi:
- '000751104400001'
pmid:
- '35080492'
file:
- access_level: open_access
checksum: decdcdf600ff51e9a9703b49ca114170
content_type: application/pdf
creator: cchlebak
date_created: 2022-02-07T07:14:09Z
date_updated: 2022-02-07T07:14:09Z
file_id: '10739'
file_name: 2022_ELife_Lagator.pdf
file_size: 5604343
relation: main_file
success: 1
file_date_updated: 2022-02-07T07:14:09Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2578D616-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '648440'
name: Selective Barriers to Horizontal Gene Transfer
publication: eLife
publication_identifier:
eissn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predicting bacterial promoter function and evolution from random sequences
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2022'
...
---
_id: '12081'
abstract:
- lang: eng
text: 'Selection accumulates information in the genome—it guides stochastically
evolving populations toward states (genotype frequencies) that would be unlikely
under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence
between the actual distribution of genotype frequencies and the corresponding
neutral distribution. First, we show that this population-level information sets
an upper bound on the information at the level of genotype and phenotype, limiting
how precisely they can be specified by selection. Next, we study how the accumulation
and maintenance of information is limited by the cost of selection, measured as
the genetic load or the relative fitness variance, both of which we connect to
the control-theoretic KL cost of control. The information accumulation rate is
upper bounded by the population size times the cost of selection. This bound is
very general, and applies across models (Wright–Fisher, Moran, diffusion) and
to arbitrary forms of selection, mutation, and recombination. Finally, the cost
of maintaining information depends on how it is encoded: Specifying a single allele
out of two is expensive, but one bit encoded among many weakly specified loci
(as in a polygenic trait) is cheap.'
acknowledgement: We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and
two anonymous reviewers for discussions and comments on the manuscript. G.T. and
M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018.
N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”
article_number: e2123152119
article_processing_charge: No
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 2022;119(36).
doi:10.1073/pnas.2123152119
apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance
of information in evolution. Proceedings of the National Academy of Sciences.
Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119
chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and
Maintenance of Information in Evolution.” Proceedings of the National Academy
of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119.
ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information
in evolution,” Proceedings of the National Academy of Sciences, vol. 119,
no. 36. Proceedings of the National Academy of Sciences, 2022.
ista: Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.
mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.”
Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119,
Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119.
short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of
Sciences 119 (2022).
date_created: 2022-09-11T22:01:55Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '29'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1073/pnas.2123152119
ec_funded: 1
external_id:
isi:
- '000889278400014'
pmid:
- '36037343'
file:
- access_level: open_access
checksum: 6dec51f6567da9039982a571508a8e4d
content_type: application/pdf
creator: dernst
date_created: 2022-09-12T08:08:12Z
date_updated: 2022-09-12T08:08:12Z
file_id: '12091'
file_name: 2022_PNAS_Hledik.pdf
file_size: 2165752
relation: main_file
success: 1
file_date_updated: 2022-09-12T08:08:12Z
has_accepted_license: '1'
intvolume: ' 119'
isi: 1
issue: '36'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
issn:
- 0027-8424
publication_status: published
publisher: Proceedings of the National Academy of Sciences
quality_controlled: '1'
related_material:
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Accumulation and maintenance of information in evolution
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 119
year: '2022'
...
---
_id: '12156'
abstract:
- lang: eng
text: Models of transcriptional regulation that assume equilibrium binding of transcription
factors have been less successful at predicting gene expression from sequence
in eukaryotes than in bacteria. This could be due to the non-equilibrium nature
of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium
mechanisms is vast and predominantly uninteresting. The key question is therefore
how this space can be navigated efficiently, to focus on mechanisms and models
that are biologically relevant. In this review, we advocate for the normative
role of theory—theory that prescribes rather than just describes—in providing
such a focus. Theory should expand its remit beyond inferring mechanistic models
from data, towards identifying non-equilibrium gene regulatory schemes that may
have been evolutionarily selected, despite their energy consumption, because they
are precise, reliable, fast, or otherwise outperform regulation at equilibrium.
We illustrate our reasoning by toy examples for which we provide simulation code.
acknowledgement: 'This work was supported through the Center for the Physics of Biological
Function (PHYe1734030) and by National Institutes of Health Grants R01GM097275 and
U01DK127429 (TG). GT acknowledges the support of the Austrian Science Fund grant
FWF P28844 and the Human Frontiers Science Program. '
article_number: '100435'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Benjamin
full_name: Zoller, Benjamin
last_name: Zoller
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Zoller B, Gregor T, Tkačik G. Eukaryotic gene regulation at equilibrium, or
non? Current Opinion in Systems Biology. 2022;31(9). doi:10.1016/j.coisb.2022.100435
apa: Zoller, B., Gregor, T., & Tkačik, G. (2022). Eukaryotic gene regulation
at equilibrium, or non? Current Opinion in Systems Biology. Elsevier. https://doi.org/10.1016/j.coisb.2022.100435
chicago: Zoller, Benjamin, Thomas Gregor, and Gašper Tkačik. “Eukaryotic Gene Regulation
at Equilibrium, or Non?” Current Opinion in Systems Biology. Elsevier,
2022. https://doi.org/10.1016/j.coisb.2022.100435.
ieee: B. Zoller, T. Gregor, and G. Tkačik, “Eukaryotic gene regulation at equilibrium,
or non?,” Current Opinion in Systems Biology, vol. 31, no. 9. Elsevier,
2022.
ista: Zoller B, Gregor T, Tkačik G. 2022. Eukaryotic gene regulation at equilibrium,
or non? Current Opinion in Systems Biology. 31(9), 100435.
mla: Zoller, Benjamin, et al. “Eukaryotic Gene Regulation at Equilibrium, or Non?”
Current Opinion in Systems Biology, vol. 31, no. 9, 100435, Elsevier, 2022,
doi:10.1016/j.coisb.2022.100435.
short: B. Zoller, T. Gregor, G. Tkačik, Current Opinion in Systems Biology 31 (2022).
date_created: 2023-01-12T12:08:51Z
date_published: 2022-09-01T00:00:00Z
date_updated: 2023-02-13T09:20:34Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1016/j.coisb.2022.100435
file:
- access_level: open_access
checksum: 97ef01e0cc60cdc84f45640a0f248fb0
content_type: application/pdf
creator: dernst
date_created: 2023-01-24T12:14:10Z
date_updated: 2023-01-24T12:14:10Z
file_id: '12362'
file_name: 2022_CurrentBiology_Zoller.pdf
file_size: 2214944
relation: main_file
success: 1
file_date_updated: 2023-01-24T12:14:10Z
has_accepted_license: '1'
intvolume: ' 31'
issue: '9'
keyword:
- Applied Mathematics
- Computer Science Applications
- Drug Discovery
- General Biochemistry
- Genetics and Molecular Biology
- Modeling and Simulation
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Current Opinion in Systems Biology
publication_identifier:
issn:
- 2452-3100
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Eukaryotic gene regulation at equilibrium, or non?
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2022'
...
---
_id: '12332'
abstract:
- lang: eng
text: Activity of sensory neurons is driven not only by external stimuli but also
by feedback signals from higher brain areas. Attention is one particularly important
internal signal whose presumed role is to modulate sensory representations such
that they only encode information currently relevant to the organism at minimal
cost. This hypothesis has, however, not yet been expressed in a normative computational
framework. Here, by building on normative principles of probabilistic inference
and efficient coding, we developed a model of dynamic population coding in the
visual cortex. By continuously adapting the sensory code to changing demands of
the perceptual observer, an attention-like modulation emerges. This modulation
can dramatically reduce the amount of neural activity without deteriorating the
accuracy of task-specific inferences. Our results suggest that a range of seemingly
disparate cortical phenomena such as intrinsic gain modulation, attention-related
tuning modulation, and response variability could be manifestations of the same
underlying principles, which combine efficient sensory coding with optimal probabilistic
inference in dynamic environments.
acknowledgement: "We thank Robbe Goris for generously providing figures from his work
and Ann M. Hermundstad for helpful discussions.\r\nGT & WM were supported by the
Austrian Science Fund Standalone Grant P 34015 \"Efficient Coding with Biophysical
Realism\" (https://pf.fwf.ac.at/) WM was additionally supported by the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 754411 (https://ec.europa.eu/research/mariecurieactions/). The
funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript."
article_processing_charge: No
article_type: original
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Mlynarski WF, Tkačik G. Efficient coding theory of dynamic attentional modulation.
PLoS Biology. 2022;20(12):e3001889. doi:10.1371/journal.pbio.3001889
apa: Mlynarski, W. F., & Tkačik, G. (2022). Efficient coding theory of dynamic
attentional modulation. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.3001889
chicago: Mlynarski, Wiktor F, and Gašper Tkačik. “Efficient Coding Theory of Dynamic
Attentional Modulation.” PLoS Biology. Public Library of Science, 2022.
https://doi.org/10.1371/journal.pbio.3001889.
ieee: W. F. Mlynarski and G. Tkačik, “Efficient coding theory of dynamic attentional
modulation,” PLoS Biology, vol. 20, no. 12. Public Library of Science,
p. e3001889, 2022.
ista: Mlynarski WF, Tkačik G. 2022. Efficient coding theory of dynamic attentional
modulation. PLoS Biology. 20(12), e3001889.
mla: Mlynarski, Wiktor F., and Gašper Tkačik. “Efficient Coding Theory of Dynamic
Attentional Modulation.” PLoS Biology, vol. 20, no. 12, Public Library
of Science, 2022, p. e3001889, doi:10.1371/journal.pbio.3001889.
short: W.F. Mlynarski, G. Tkačik, PLoS Biology 20 (2022) e3001889.
date_created: 2023-01-22T23:00:55Z
date_published: 2022-12-21T00:00:00Z
date_updated: 2023-08-03T14:23:49Z
day: '21'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pbio.3001889
ec_funded: 1
external_id:
isi:
- '000925192000001'
file:
- access_level: open_access
checksum: 5d7f1111a87e5f2c1bf92f8886738894
content_type: application/pdf
creator: dernst
date_created: 2023-01-23T08:46:40Z
date_updated: 2023-01-23T08:46:40Z
file_id: '12337'
file_name: 2022_PloSBiology_Mlynarski.pdf
file_size: 4248838
relation: main_file
success: 1
file_date_updated: 2023-01-23T08:46:40Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: e3001889
project:
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: PLoS Biology
publication_identifier:
eissn:
- 1545-7885
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient coding theory of dynamic attentional modulation
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 20
year: '2022'
...
---
_id: '10912'
abstract:
- lang: eng
text: Brain dynamics display collective phenomena as diverse as neuronal oscillations
and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic
scale, whereas avalanches are scale-free cascades of neural activity. Here we
show that such antithetic features can coexist in a very generic class of adaptive
neural networks. In the most simple yet fully microscopic model from this class
we make direct contact with human brain resting-state activity recordings via
tractable inference of the model's two essential parameters. The inferred model
quantitatively captures the dynamics over a broad range of scales, from single
sensor fluctuations, collective behaviors of nearly-synchronous extreme events
on multiple sensors, to neuronal avalanches unfolding over multiple sensors across
multiple time-bins. Importantly, the inferred parameters correlate with model-independent
signatures of "closeness to criticality", suggesting that the coexistence of scale-specific
(neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity
occurs close to a non-equilibrium critical point at the onset of self-sustained
oscillations.
acknowledgement: "FL acknowledges support from the European Union’s Horizon 2020 research
and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411.
GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone
Grant\r\nNo. P34015."
article_processing_charge: No
arxiv: 1
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Selver
full_name: Pepic, Selver
id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425
last_name: Pepic
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Daniele
full_name: De Martino, Daniele
last_name: De Martino
citation:
ama: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. doi:10.48550/ARXIV.2108.06686
apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (n.d.).
Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. https://doi.org/10.48550/ARXIV.2108.06686
chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele
De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.”
arXiv, n.d. https://doi.org/10.48550/ARXIV.2108.06686.
ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying
the coexistence of neuronal oscillations and avalanches.” arXiv.
ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence
of neuronal oscillations and avalanches. 10.48550/ARXIV.2108.06686.
mla: Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations
and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686.
short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).
date_created: 2022-03-21T11:41:28Z
date_published: 2021-08-17T00:00:00Z
date_updated: 2022-03-22T07:53:18Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.48550/ARXIV.2108.06686
ec_funded: 1
external_id:
arxiv:
- '2108.06686'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2108.06686
month: '08'
oa: 1
oa_version: Preprint
page: '37'
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication_status: submitted
publisher: arXiv
status: public
title: Quantifying the coexistence of neuronal oscillations and avalanches
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '8997'
abstract:
- lang: eng
text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable
history in physics but are still scarce in biology. This situation restrains predictive
theory. Here, we build on bacterial “growth laws,” which capture physiological
feedback between translation and cell growth, to construct a minimal biophysical
model for the combined action of ribosome-targeting antibiotics. Our model predicts
drug interactions like antagonism or synergy solely from responses to individual
drugs. We provide analytical results for limiting cases, which agree well with
numerical results. We systematically refine the model by including direct physical
interactions of different antibiotics on the ribosome. In a limiting case, our
model provides a mechanistic underpinning for recent predictions of higher-order
interactions that were derived using entropy maximization. We further refine the
model to include the effects of antibiotics that mimic starvation and the presence
of resistance genes. We describe the impact of a starvation-mimicking antibiotic
on drug interactions analytically and verify it experimentally. Our extended model
suggests a change in the type of drug interaction that depends on the strength
of resistance, which challenges established rescaling paradigms. We experimentally
show that the presence of unregulated resistance genes can lead to altered drug
interaction, which agrees with the prediction of the model. While minimal, the
model is readily adaptable and opens the door to predicting interactions of second
and higher-order in a broad range of biological systems.
acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation
(to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and
P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation
(DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)
Collaborative Research Centre (SFB) 1310 (to T.B.). '
article_number: e1008529
article_processing_charge: Yes
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic
action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical
model of combined antibiotic action. PLOS Computational Biology. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1008529
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical
Model of Combined Antibiotic Action.” PLOS Computational Biology. Public
Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of
combined antibiotic action,” PLOS Computational Biology, vol. 17. Public
Library of Science, 2021.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined
antibiotic action. PLOS Computational Biology. 17, e1008529.
mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.”
PLOS Computational Biology, vol. 17, e1008529, Public Library of Science,
2021, doi:10.1371/journal.pcbi.1008529.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).
date_created: 2021-01-08T07:16:18Z
date_published: 2021-01-07T00:00:00Z
date_updated: 2024-02-21T12:41:41Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1008529
external_id:
isi:
- '000608045000010'
file:
- access_level: open_access
checksum: e29f2b42651bef8e034781de8781ffac
content_type: application/pdf
creator: dernst
date_created: 2021-02-04T12:30:48Z
date_updated: 2021-02-04T12:30:48Z
file_id: '9092'
file_name: 2021_PlosComBio_Kavcic.pdf
file_size: 3690053
relation: main_file
success: 1
file_date_updated: 2021-02-04T12:30:48Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
keyword:
- Modelling and Simulation
- Genetics
- Molecular Biology
- Antibiotics
- Drug interactions
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: PLOS Computational Biology
publication_identifier:
issn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
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status: public
status: public
title: Minimal biophysical model of combined antibiotic action
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 17
year: '2021'
...
---
_id: '7553'
abstract:
- lang: eng
text: Normative theories and statistical inference provide complementary approaches
for the study of biological systems. A normative theory postulates that organisms
have adapted to efficiently solve essential tasks, and proceeds to mathematically
work out testable consequences of such optimality; parameters that maximize the
hypothesized organismal function can be derived ab initio, without reference to
experimental data. In contrast, statistical inference focuses on efficient utilization
of data to learn model parameters, without reference to any a priori notion of
biological function, utility, or fitness. Traditionally, these two approaches
were developed independently and applied separately. Here we unify them in a coherent
Bayesian framework that embeds a normative theory into a family of maximum-entropy
“optimization priors.” This family defines a smooth interpolation between a data-rich
inference regime (characteristic of “bottom-up” statistical models), and a data-limited
ab inito prediction regime (characteristic of “top-down” normative theory). We
demonstrate the applicability of our framework using data from the visual cortex,
and argue that the flexibility it affords is essential to address a number of
fundamental challenges relating to inference and prediction in complex, high-dimensional
biological problems.
acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields
and Olivier Marre for providing the retinal receptive fields. W.M. was funded by
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers
Science grant no. HFSP RGP0032/2018.
article_processing_charge: No
author:
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Thomas R
full_name: Sokolowski, Thomas R
id: 3E999752-F248-11E8-B48F-1D18A9856A87
last_name: Sokolowski
orcid: 0000-0002-1287-3779
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality
of neural systems. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020
apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical
analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020
chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik.
“Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press,
2021. https://doi.org/10.1016/j.neuron.2021.01.020.
ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical
analysis and optimality of neural systems,” Neuron, vol. 109, no. 7. Cell
Press, p. 1227–1241.e5, 2021.
ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis
and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.
mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural
Systems.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020.
short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021)
1227–1241.e5.
date_created: 2020-02-28T11:00:12Z
date_published: 2021-04-07T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '07'
department:
- _id: GaTk
doi: 10.1016/j.neuron.2021.01.020
ec_funded: 1
external_id:
isi:
- '000637809600006'
intvolume: ' 109'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/848374
month: '04'
oa: 1
oa_version: Preprint
page: 1227-1241.e5
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neuron
publication_status: published
publisher: Cell Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/can-evolution-be-predicted/
record:
- id: '15020'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Statistical analysis and optimality of neural systems
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
year: '2021'
...
---
_id: '9226'
abstract:
- lang: eng
text: 'Half a century after Lewis Wolpert''s seminal conceptual advance on how cellular
fates distribute in space, we provide a brief historical perspective on how the
concept of positional information emerged and influenced the field of developmental
biology and beyond. We focus on a modern interpretation of this concept in terms
of information theory, largely centered on its application to cell specification
in the early Drosophila embryo. We argue that a true physical variable (position)
is encoded in local concentrations of patterning molecules, that this mapping
is stochastic, and that the processes by which positions and corresponding cell
fates are determined based on these concentrations need to take such stochasticity
into account. With this approach, we shift the focus from biological mechanisms,
molecules, genes and pathways to quantitative systems-level questions: where does
positional information reside, how it is transformed and accessed during development,
and what fundamental limits it is subject to?'
acknowledgement: This work was supported in part by the National Science Foundation,
through the Center for the Physics of Biological Function (PHY-1734030), by the
National Institutes of Health (R01GM097275) and by the Fonds zur Förderung der wissenschaftlichen
Forschung (FWF P28844). Deposited in PMC for release after 12 months.
article_number: dev176065
article_processing_charge: No
article_type: original
author:
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Thomas
full_name: Gregor, Thomas
last_name: Gregor
citation:
ama: Tkačik G, Gregor T. The many bits of positional information. Development.
2021;148(2). doi:10.1242/dev.176065
apa: Tkačik, G., & Gregor, T. (2021). The many bits of positional information.
Development. The Company of Biologists. https://doi.org/10.1242/dev.176065
chicago: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.”
Development. The Company of Biologists, 2021. https://doi.org/10.1242/dev.176065.
ieee: G. Tkačik and T. Gregor, “The many bits of positional information,” Development,
vol. 148, no. 2. The Company of Biologists, 2021.
ista: Tkačik G, Gregor T. 2021. The many bits of positional information. Development.
148(2), dev176065.
mla: Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.”
Development, vol. 148, no. 2, dev176065, The Company of Biologists, 2021,
doi:10.1242/dev.176065.
short: G. Tkačik, T. Gregor, Development 148 (2021).
date_created: 2021-03-07T23:01:25Z
date_published: 2021-02-01T00:00:00Z
date_updated: 2023-08-07T13:57:30Z
day: '01'
department:
- _id: GaTk
doi: 10.1242/dev.176065
external_id:
isi:
- '000613906000007'
pmid:
- '33526425'
intvolume: ' 148'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1242/dev.176065
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Development
publication_identifier:
eissn:
- 1477-9129
publication_status: published
publisher: The Company of Biologists
quality_controlled: '1'
scopus_import: '1'
status: public
title: The many bits of positional information
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 148
year: '2021'
...
---
_id: '9362'
abstract:
- lang: eng
text: A central goal in systems neuroscience is to understand the functions performed
by neural circuits. Previous top-down models addressed this question by comparing
the behaviour of an ideal model circuit, optimised to perform a given function,
with neural recordings. However, this requires guessing in advance what function
is being performed, which may not be possible for many neural systems. To address
this, we propose an inverse reinforcement learning (RL) framework for inferring
the function performed by a neural network from data. We assume that the responses
of each neuron in a network are optimised so as to drive the network towards ‘rewarded’
states, that are desirable for performing a given function. We then show how one
can use inverse RL to infer the reward function optimised by the network from
observing its responses. This inferred reward function can be used to predict
how the neural network should adapt its dynamics to perform the same function
when the external environment or network structure changes. This could lead to
theoretical predictions about how neural network dynamics adapt to deal with cell
death and/or varying sensory stimulus statistics.
acknowledgement: The authors would like to thank Ulisse Ferrari for useful discussions
and feedback.
article_number: e0248940
article_processing_charge: No
article_type: original
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: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
citation:
ama: Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent
neural network. PLoS ONE. 2021;16(4). doi:10.1371/journal.pone.0248940
apa: Chalk, M. J., Tkačik, G., & Marre, O. (2021). Inferring the function performed
by a recurrent neural network. PLoS ONE. Public Library of Science. https://doi.org/10.1371/journal.pone.0248940
chicago: Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function
Performed by a Recurrent Neural Network.” PLoS ONE. Public Library of Science,
2021. https://doi.org/10.1371/journal.pone.0248940.
ieee: M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by
a recurrent neural network,” PLoS ONE, vol. 16, no. 4. Public Library of
Science, 2021.
ista: Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent
neural network. PLoS ONE. 16(4), e0248940.
mla: Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent
Neural Network.” PLoS ONE, vol. 16, no. 4, e0248940, Public Library of
Science, 2021, doi:10.1371/journal.pone.0248940.
short: M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).
date_created: 2021-05-02T22:01:28Z
date_published: 2021-04-15T00:00:00Z
date_updated: 2023-10-18T08:17:42Z
day: '15'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0248940
external_id:
isi:
- '000641474900072'
pmid:
- '33857170'
file:
- access_level: open_access
checksum: c52da133850307d2031f552d998f00e8
content_type: application/pdf
creator: kschuh
date_created: 2021-05-04T13:22:19Z
date_updated: 2021-05-04T13:22:19Z
file_id: '9371'
file_name: 2021_pone_Chalk.pdf
file_size: 2768282
relation: main_file
success: 1
file_date_updated: 2021-05-04T13:22:19Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '4'
language:
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month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS ONE
publication_identifier:
eissn:
- '19326203'
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Inferring the function performed by a recurrent neural network
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2021'
...
---
_id: '10077'
abstract:
- lang: eng
text: Although much is known about how single neurons in the hippocampus represent
an animal’s position, how cell-cell interactions contribute to spatial coding
remains poorly understood. Using a novel statistical estimator and theoretical
modeling, both developed in the framework of maximum entropy models, we reveal
highly structured cell-to-cell interactions whose statistics depend on familiar
vs. novel environment. In both conditions the circuit interactions optimize the
encoding of spatial information, but for regimes that differ in the signal-to-noise
ratio of their spatial inputs. Moreover, the topology of the interactions facilitates
linear decodability, making the information easy to read out by downstream circuits.
These findings suggest that the efficient coding hypothesis is not applicable
only to individual neuron properties in the sensory periphery, but also to neural
interactions in the central brain.
acknowledgement: We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for
the acquisition of the data. We thank Federico Stella for comments on an earlier
version of the manuscript. MN was supported by European Union Horizon 2020 grant
665385, JC was supported by European Research Council consolidator grant 281511,
GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported
by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01,
by the National Science Foundation under NSF Award No. 1922658 and a Google faculty
award.
article_processing_charge: No
author:
- first_name: Michele
full_name: Nardin, Michele
id: 30BD0376-F248-11E8-B48F-1D18A9856A87
last_name: Nardin
orcid: 0000-0001-8849-6570
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
citation:
ama: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1
interactions optimizes spatial coding across experience. bioRxiv. doi:10.1101/2021.09.28.460602
apa: Nardin, M., Csicsvari, J. L., Tkačik, G., & Savin, C. (n.d.). The structure
of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv.
Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.09.28.460602
chicago: Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin.
“The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across
Experience.” BioRxiv. Cold Spring Harbor Laboratory, n.d. https://doi.org/10.1101/2021.09.28.460602.
ieee: M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal
CA1 interactions optimizes spatial coding across experience,” bioRxiv.
Cold Spring Harbor Laboratory.
ista: Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1
interactions optimizes spatial coding across experience. bioRxiv, 10.1101/2021.09.28.460602.
mla: Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes
Spatial Coding across Experience.” BioRxiv, Cold Spring Harbor Laboratory,
doi:10.1101/2021.09.28.460602.
short: M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).
date_created: 2021-10-04T06:23:34Z
date_published: 2021-09-29T00:00:00Z
date_updated: 2024-03-25T23:30:09Z
day: '29'
department:
- _id: GradSch
- _id: JoCs
- _id: GaTk
doi: 10.1101/2021.09.28.460602
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.biorxiv.org/content/10.1101/2021.09.28.460602
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 257A4776-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '281511'
name: Memory-related information processing in neuronal circuits of the hippocampus
and entorhinal cortex
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication: bioRxiv
publication_status: submitted
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '11932'
relation: dissertation_contains
status: public
status: public
title: The structure of hippocampal CA1 interactions optimizes spatial coding across
experience
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '10579'
abstract:
- lang: eng
text: 'We consider a totally asymmetric simple exclusion process (TASEP) consisting
of particles on a lattice that require binding by a "token" to move. Using a combination
of theory and simulations, we address the following questions: (i) How token binding
kinetics affects the current-density relation; (ii) How the current-density relation
depends on the scarcity of tokens; (iii) How tokens propagate the effects of the
locally-imposed disorder (such a slow site) over the entire lattice; (iv) How
a shared pool of tokens couples concurrent TASEPs running on multiple lattices;
(v) How our results translate to TASEPs with open boundaries that exchange particles
with the reservoir. Since real particle motion (including in systems that inspired
the standard TASEP model, e.g., protein synthesis or movement of molecular motors)
is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven
TASEP dynamics analyzed in this paper should allow for a better understanding
of real systems and enable a closer match between TASEP theory and experimental
observations.'
acknowledgement: B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for
their help with the usage of the cluster. B.K. additionally thanks Călin Guet and
his group for help and advice. We thank M. Hennessey-Wesen for constructive comments
on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting
a typographical error in Eq. (49) in the preprint version of this paper.
article_number: '2112.13558'
article_processing_charge: No
arxiv: 1
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv. doi:10.48550/arXiv.2112.13558
apa: Kavcic, B., & Tkačik, G. (n.d.). Token-driven totally asymmetric simple
exclusion process. arXiv. https://doi.org/10.48550/arXiv.2112.13558
chicago: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple
Exclusion Process.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2112.13558.
ieee: B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion
process,” arXiv. .
ista: Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process.
arXiv, 2112.13558.
mla: Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion
Process.” ArXiv, 2112.13558, doi:10.48550/arXiv.2112.13558.
short: B. Kavcic, G. Tkačik, ArXiv (n.d.).
date_created: 2021-12-28T06:52:09Z
date_published: 2021-12-27T00:00:00Z
date_updated: 2023-05-03T10:54:05Z
day: '27'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.48550/arXiv.2112.13558
external_id:
arxiv:
- '2112.13558'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2112.13558
month: '12'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Token-driven totally asymmetric simple exclusion process
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '8250'
abstract:
- lang: eng
text: 'Antibiotics that interfere with translation, when combined, interact in diverse
and difficult-to-predict ways. Here, we explain these interactions by “translation
bottlenecks”: points in the translation cycle where antibiotics block ribosomal
progression. To elucidate the underlying mechanisms of drug interactions between
translation inhibitors, we generate translation bottlenecks genetically using
inducible control of translation factors that regulate well-defined translation
cycle steps. These perturbations accurately mimic antibiotic action and drug interactions,
supporting that the interplay of different translation bottlenecks causes these
interactions. We further show that growth laws, combined with drug uptake and
binding kinetics, enable the direct prediction of a large fraction of observed
interactions, yet fail to predict suppression. However, varying two translation
bottlenecks simultaneously supports that dense traffic of ribosomes and competition
for translation factors account for the previously unexplained suppression. These
results highlight the importance of “continuous epistasis” in bacterial physiology.'
acknowledgement: "We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K.
Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive
comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support,
which rendered this\r\nwork possible. B.K. thanks all members of Guet group for
many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges
the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work.
We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A.
Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This
work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P
27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to
T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.),
and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310
(to T.B.). Open access funding provided by\r\nProjekt DEAL."
article_number: '4013'
article_processing_charge: No
article_type: original
author:
- first_name: Bor
full_name: Kavcic, Bor
id: 350F91D2-F248-11E8-B48F-1D18A9856A87
last_name: Kavcic
orcid: 0000-0001-6041-254X
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Tobias
full_name: Bollenbach, Tobias
id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87
last_name: Bollenbach
orcid: 0000-0003-4398-476X
citation:
ama: Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 2020;11. doi:10.1038/s41467-020-17734-z
apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). Mechanisms of drug
interactions between translation-inhibiting antibiotics. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-020-17734-z
chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of
Drug Interactions between Translation-Inhibiting Antibiotics.” Nature Communications.
Springer Nature, 2020. https://doi.org/10.1038/s41467-020-17734-z.
ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions
between translation-inhibiting antibiotics,” Nature Communications, vol.
11. Springer Nature, 2020.
ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between
translation-inhibiting antibiotics. Nature Communications. 11, 4013.
mla: Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting
Antibiotics.” Nature Communications, vol. 11, 4013, Springer Nature, 2020,
doi:10.1038/s41467-020-17734-z.
short: B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).
date_created: 2020-08-12T09:13:50Z
date_published: 2020-08-11T00:00:00Z
date_updated: 2024-03-25T23:30:05Z
day: '11'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1038/s41467-020-17734-z
external_id:
isi:
- '000562769300008'
file:
- access_level: open_access
checksum: 986bebb308850a55850028d3d2b5b664
content_type: application/pdf
creator: dernst
date_created: 2020-08-17T07:36:57Z
date_updated: 2020-08-17T07:36:57Z
file_id: '8275'
file_name: 2020_NatureComm_Kavcic.pdf
file_size: 1965672
relation: main_file
success: 1
file_date_updated: 2020-08-17T07:36:57Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 25E9AF9E-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P27201-B22
name: Revealing the mechanisms underlying drug interactions
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Nature Communications
publication_identifier:
issn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8657'
relation: dissertation_contains
status: public
status: public
title: Mechanisms of drug interactions between translation-inhibiting antibiotics
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11
year: '2020'
...
---
_id: '8698'
abstract:
- lang: eng
text: The brain represents and reasons probabilistically about complex stimuli and
motor actions using a noisy, spike-based neural code. A key building block for
such neural computations, as well as the basis for supervised and unsupervised
learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional
neural activity patterns. Despite progress in statistical modeling of neural responses
and deep learning, current approaches either do not scale to large neural populations
or cannot be implemented using biologically realistic mechanisms. Inspired by
the sparse and random connectivity of real neuronal circuits, we present a model
for neural codes that accurately estimates the likelihood of individual spiking
patterns and has a straightforward, scalable, efficient, learnable, and realistic
neural implementation. This model’s performance on simultaneously recorded spiking
activity of >100 neurons in the monkey visual and prefrontal cortices is comparable
with or better than that of state-of-the-art models. Importantly, the model can
be learned using a small number of samples and using a local learning rule that
utilizes noise intrinsic to neural circuits. Slower, structural changes in random
connectivity, consistent with rewiring and pruning processes, further improve
the efficiency and sparseness of the resulting neural representations. Our results
merge insights from neuroanatomy, machine learning, and theoretical neuroscience
to suggest random sparse connectivity as a key design principle for neuronal computation.
acknowledgement: We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir
Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn
for invaluable discussions of the learning rule. This work was supported by European
Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12
(to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs.
Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to
R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration
on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative
Research in Computational Neuroscience) grant (to R.K. and E.S.).
article_processing_charge: No
article_type: original
author:
- first_name: Ori
full_name: Maoz, Ori
last_name: Maoz
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Mohamad Saleh
full_name: Esteki, Mohamad Saleh
last_name: Esteki
- first_name: Roozbeh
full_name: Kiani, Roozbeh
last_name: Kiani
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
citation:
ama: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic
neural representations with randomly connected circuits. Proceedings of the
National Academy of Sciences of the United States of America. 2020;117(40):25066-25073.
doi:10.1073/pnas.1912804117
apa: Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020).
Learning probabilistic neural representations with randomly connected circuits.
Proceedings of the National Academy of Sciences of the United States of America.
National Academy of Sciences. https://doi.org/10.1073/pnas.1912804117
chicago: Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad
Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected
Circuits.” Proceedings of the National Academy of Sciences of the United States
of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.1912804117.
ieee: O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic
neural representations with randomly connected circuits,” Proceedings of the
National Academy of Sciences of the United States of America, vol. 117, no.
40. National Academy of Sciences, pp. 25066–25073, 2020.
ista: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic
neural representations with randomly connected circuits. Proceedings of the National
Academy of Sciences of the United States of America. 117(40), 25066–25073.
mla: Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly
Connected Circuits.” Proceedings of the National Academy of Sciences of the
United States of America, vol. 117, no. 40, National Academy of Sciences,
2020, pp. 25066–73, doi:10.1073/pnas.1912804117.
short: O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of
the National Academy of Sciences of the United States of America 117 (2020) 25066–25073.
date_created: 2020-10-25T23:01:16Z
date_published: 2020-10-06T00:00:00Z
date_updated: 2023-08-22T12:11:23Z
day: '06'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1073/pnas.1912804117
external_id:
isi:
- '000579045200012'
pmid:
- '32948691'
file:
- access_level: open_access
checksum: c6a24fdecf3f28faf447078e7a274a88
content_type: application/pdf
creator: cziletti
date_created: 2020-10-27T14:57:50Z
date_updated: 2020-10-27T14:57:50Z
file_id: '8713'
file_name: 2020_PNAS_Maoz.pdf
file_size: 1755359
relation: main_file
success: 1
file_date_updated: 2020-10-27T14:57:50Z
has_accepted_license: '1'
intvolume: ' 117'
isi: 1
issue: '40'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 25066-25073
pmid: 1
publication: Proceedings of the National Academy of Sciences of the United States
of America
publication_identifier:
eissn:
- '10916490'
issn:
- '00278424'
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning probabilistic neural representations with randomly connected circuits
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 117
year: '2020'
...
---
_id: '9000'
abstract:
- lang: eng
text: 'In prokaryotes, thermodynamic models of gene regulation provide a highly
quantitative mapping from promoter sequences to gene-expression levels that is
compatible with in vivo and in vitro biophysical measurements. Such concordance
has not been achieved for models of enhancer function in eukaryotes. In equilibrium
models, it is difficult to reconcile the reported short transcription factor (TF)
residence times on the DNA with the high specificity of regulation. In nonequilibrium
models, progress is difficult due to an explosion in the number of parameters.
Here, we navigate this complexity by looking for minimal nonequilibrium enhancer
models that yield desired regulatory phenotypes: low TF residence time, high specificity,
and tunable cooperativity. We find that a single extra parameter, interpretable
as the “linking rate,” by which bound TFs interact with Mediator components, enables
our models to escape equilibrium bounds and access optimal regulatory phenotypes,
while remaining consistent with the reported phenomenology and simple enough to
be inferred from upcoming experiments. We further find that high specificity in
nonequilibrium models is in a trade-off with gene-expression noise, predicting
bursty dynamics—an experimentally observed hallmark of eukaryotic transcription.
By drastically reducing the vast parameter space of nonequilibrium enhancer models
to a much smaller subspace that optimally realizes biological function, we deliver
a rich class of models that could be tractably inferred from data in the near
future.'
acknowledgement: G.T. was supported by Human Frontiers Science Program Grant RGP0034/2018.
R.G. was supported by the Austrian Academy of Sciences DOC Fellowship. R.G. thanks
S. Avvakumov for helpful discussions.
article_processing_charge: No
article_type: original
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Benjamin
full_name: Zoller, Benjamin
last_name: Zoller
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Grah R, Zoller B, Tkačik G. Nonequilibrium models of optimal enhancer function.
PNAS. 2020;117(50):31614-31622. doi:10.1073/pnas.2006731117
apa: Grah, R., Zoller, B., & Tkačik, G. (2020). Nonequilibrium models of optimal
enhancer function. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.2006731117
chicago: Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Nonequilibrium Models of
Optimal Enhancer Function.” PNAS. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.2006731117.
ieee: R. Grah, B. Zoller, and G. Tkačik, “Nonequilibrium models of optimal enhancer
function,” PNAS, vol. 117, no. 50. National Academy of Sciences, pp. 31614–31622,
2020.
ista: Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer
function. PNAS. 117(50), 31614–31622.
mla: Grah, Rok, et al. “Nonequilibrium Models of Optimal Enhancer Function.” PNAS,
vol. 117, no. 50, National Academy of Sciences, 2020, pp. 31614–22, doi:10.1073/pnas.2006731117.
short: R. Grah, B. Zoller, G. Tkačik, PNAS 117 (2020) 31614–31622.
date_created: 2021-01-10T23:01:17Z
date_published: 2020-12-15T00:00:00Z
date_updated: 2023-08-24T11:10:22Z
day: '15'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1073/pnas.2006731117
external_id:
isi:
- '000600608300015'
pmid:
- '33268497'
file:
- access_level: open_access
checksum: 69039cd402a571983aa6cb4815ffa863
content_type: application/pdf
creator: dernst
date_created: 2021-01-11T08:37:31Z
date_updated: 2021-01-11T08:37:31Z
file_id: '9004'
file_name: 2020_PNAS_Grah.pdf
file_size: 1199247
relation: main_file
success: 1
file_date_updated: 2021-01-11T08:37:31Z
has_accepted_license: '1'
intvolume: ' 117'
isi: 1
issue: '50'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 31614-31622
pmid: 1
project:
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
- _id: 267C84F4-B435-11E9-9278-68D0E5697425
name: Biophysically realistic genotype-phenotype maps for regulatory networks
publication: PNAS
publication_identifier:
eissn:
- '10916490'
issn:
- '00278424'
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/new-compact-model-for-gene-regulation-in-higher-organisms/
scopus_import: '1'
status: public
title: Nonequilibrium models of optimal enhancer function
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 117
year: '2020'
...
---
_id: '7652'
abstract:
- lang: eng
text: Organisms cope with change by taking advantage of transcriptional regulators.
However, when faced with rare environments, the evolution of transcriptional regulators
and their promoters may be too slow. Here, we investigate whether the intrinsic
instability of gene duplication and amplification provides a generic alternative
to canonical gene regulation. Using real-time monitoring of gene-copy-number mutations
in Escherichia coli, we show that gene duplications and amplifications enable
adaptation to fluctuating environments by rapidly generating copy-number and,
therefore, expression-level polymorphisms. This amplification-mediated gene expression
tuning (AMGET) occurs on timescales that are similar to canonical gene regulation
and can respond to rapid environmental changes. Mathematical modelling shows that
amplifications also tune gene expression in stochastic environments in which transcription-factor-based
schemes are hard to evolve or maintain. The fleeting nature of gene amplifications
gives rise to a generic population-level mechanism that relies on genetic heterogeneity
to rapidly tune the expression of any gene, without leaving any genomic signature.
acknowledgement: We thank L. Hurst, N. Barton, M. Pleska, M. Steinrück, B. Kavcic
and A. Staron for input on the manuscript, and To. Bergmiller and R. Chait for help
with microfluidics experiments. I.T. is a recipient the OMV fellowship. R.G. is
a recipient of a DOC (Doctoral Fellowship Programme of the Austrian Academy of Sciences)
Fellowship of the Austrian Academy of Sciences.
article_processing_charge: No
article_type: original
author:
- first_name: Isabella
full_name: Tomanek, Isabella
id: 3981F020-F248-11E8-B48F-1D18A9856A87
last_name: Tomanek
orcid: 0000-0001-6197-363X
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: M.
full_name: Lagator, M.
last_name: Lagator
- first_name: A. M. C.
full_name: Andersson, A. M. C.
last_name: Andersson
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
citation:
ama: Tomanek I, Grah R, Lagator M, et al. Gene amplification as a form of population-level
gene expression regulation. Nature Ecology & Evolution. 2020;4(4):612-625.
doi:10.1038/s41559-020-1132-7
apa: Tomanek, I., Grah, R., Lagator, M., Andersson, A. M. C., Bollback, J. P., Tkačik,
G., & Guet, C. C. (2020). Gene amplification as a form of population-level
gene expression regulation. Nature Ecology & Evolution. Springer Nature.
https://doi.org/10.1038/s41559-020-1132-7
chicago: Tomanek, Isabella, Rok Grah, M. Lagator, A. M. C. Andersson, Jonathan P
Bollback, Gašper Tkačik, and Calin C Guet. “Gene Amplification as a Form of Population-Level
Gene Expression Regulation.” Nature Ecology & Evolution. Springer Nature,
2020. https://doi.org/10.1038/s41559-020-1132-7.
ieee: I. Tomanek et al., “Gene amplification as a form of population-level
gene expression regulation,” Nature Ecology & Evolution, vol. 4, no.
4. Springer Nature, pp. 612–625, 2020.
ista: Tomanek I, Grah R, Lagator M, Andersson AMC, Bollback JP, Tkačik G, Guet CC.
2020. Gene amplification as a form of population-level gene expression regulation.
Nature Ecology & Evolution. 4(4), 612–625.
mla: Tomanek, Isabella, et al. “Gene Amplification as a Form of Population-Level
Gene Expression Regulation.” Nature Ecology & Evolution, vol. 4, no.
4, Springer Nature, 2020, pp. 612–25, doi:10.1038/s41559-020-1132-7.
short: I. Tomanek, R. Grah, M. Lagator, A.M.C. Andersson, J.P. Bollback, G. Tkačik,
C.C. Guet, Nature Ecology & Evolution 4 (2020) 612–625.
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title: Gene amplification as a form of population-level gene expression regulation
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---
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abstract:
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text: 'We propose that correlations among neurons are generically strong enough
to organize neural activity patterns into a discrete set of clusters, which can
each be viewed as a population codeword. Our reasoning starts with the analysis
of retinal ganglion cell data using maximum entropy models, showing that the population
is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads
to an argument that neural populations in many other brain areas might share this
structure. Next, we use latent variable models to show that this glassy state
possesses well-defined clusters of neural activity. Clusters have three appealing
properties: (i) clusters exhibit error correction, i.e., they are reproducibly
elicited by the same stimulus despite variability at the level of constituent
neurons; (ii) clusters encode qualitatively different visual features than their
constituent neurons; and (iii) clusters can be learned by downstream neural circuits
in an unsupervised fashion. We hypothesize that these properties give rise to
a “learnable” neural code which the cortical hierarchy uses to extract increasingly
complex features without supervision or reinforcement.'
article_number: '20'
article_processing_charge: No
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author:
- first_name: Michael J.
full_name: Berry, Michael J.
last_name: Berry
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: 'Berry MJ, Tkačik G. Clustering of neural activity: A design principle for
population codes. Frontiers in Computational Neuroscience. 2020;14. doi:10.3389/fncom.2020.00020'
apa: 'Berry, M. J., & Tkačik, G. (2020). Clustering of neural activity: A design
principle for population codes. Frontiers in Computational Neuroscience.
Frontiers. https://doi.org/10.3389/fncom.2020.00020'
chicago: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity:
A Design Principle for Population Codes.” Frontiers in Computational Neuroscience.
Frontiers, 2020. https://doi.org/10.3389/fncom.2020.00020.'
ieee: 'M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle
for population codes,” Frontiers in Computational Neuroscience, vol. 14.
Frontiers, 2020.'
ista: 'Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle
for population codes. Frontiers in Computational Neuroscience. 14, 20.'
mla: 'Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design
Principle for Population Codes.” Frontiers in Computational Neuroscience,
vol. 14, 20, Frontiers, 2020, doi:10.3389/fncom.2020.00020.'
short: M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).
date_created: 2020-04-12T22:00:40Z
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date_updated: 2023-08-18T10:30:11Z
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department:
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doi: 10.3389/fncom.2020.00020
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publication: Frontiers in Computational Neuroscience
publication_identifier:
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publisher: Frontiers
quality_controlled: '1'
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title: 'Clustering of neural activity: A design principle for population codes'
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...