---
_id: '31'
abstract:
- lang: eng
text: Correlations in sensory neural networks have both extrinsic and intrinsic
origins. Extrinsic or stimulus correlations arise from shared inputs to the network
and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations
reflect biophysical mechanisms of interactions between neurons, which are expected
to be robust to changes in the stimulus ensemble. Despite the importance of this
distinction for understanding how sensory networks encode information collectively,
no method exists to reliably separate intrinsic interactions from extrinsic correlations
in neural activity data, limiting our ability to build predictive models of the
network response. In this paper we introduce a general strategy to infer population
models of interacting neurons that collectively encode stimulus information. The
key to disentangling intrinsic from extrinsic correlations is to infer the couplings
between neurons separately from the encoding model and to combine the two using
corrections calculated in a mean-field approximation. We demonstrate the effectiveness
of this approach in retinal recordings. The same coupling network is inferred
from responses to radically different stimulus ensembles, showing that these couplings
indeed reflect stimulus-independent interactions between neurons. The inferred
model predicts accurately the collective response of retinal ganglion cell populations
as a function of the stimulus.
acknowledgement: This work was supported by ANR Trajectory, the French State program
Investissements d’Avenir managed by the Agence Nationale de la Recherche (LIFESENSES;
ANR-10-LABX-65), EC Grant No. H2020-785907 from the Human Brain Project, NIH Grant
No. U01NS090501, and an AVIESAN-UNADEV grant to O.M. M.C. was supported by the Agence
Nationale de la Recherche Jeune Chercheur/Jeune Chercheuse grant (ANR-17-CE37-0013).
article_number: '042410'
article_processing_charge: No
article_type: original
author:
- first_name: Ulisse
full_name: Ferrari, Ulisse
last_name: Ferrari
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Matthew J
full_name: Chalk, Matthew J
last_name: Chalk
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
citation:
ama: Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. Separating intrinsic
interactions from extrinsic correlations in a network of sensory neurons. Physical
Review E. 2018;98(4). doi:10.1103/PhysRevE.98.042410
apa: Ferrari, U., Deny, S., Chalk, M. J., Tkačik, G., Marre, O., & Mora, T.
(2018). Separating intrinsic interactions from extrinsic correlations in a network
of sensory neurons. Physical Review E. American Physical Society. https://doi.org/10.1103/PhysRevE.98.042410
chicago: Ferrari, Ulisse, Stephane Deny, Matthew J Chalk, Gašper Tkačik, Olivier
Marre, and Thierry Mora. “Separating Intrinsic Interactions from Extrinsic Correlations
in a Network of Sensory Neurons.” Physical Review E. American Physical
Society, 2018. https://doi.org/10.1103/PhysRevE.98.042410.
ieee: U. Ferrari, S. Deny, M. J. Chalk, G. Tkačik, O. Marre, and T. Mora, “Separating
intrinsic interactions from extrinsic correlations in a network of sensory neurons,”
Physical Review E, vol. 98, no. 4. American Physical Society, 2018.
ista: Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. 2018. Separating intrinsic
interactions from extrinsic correlations in a network of sensory neurons. Physical
Review E. 98(4), 042410.
mla: Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations
in a Network of Sensory Neurons.” Physical Review E, vol. 98, no. 4, 042410,
American Physical Society, 2018, doi:10.1103/PhysRevE.98.042410.
short: U. Ferrari, S. Deny, M.J. Chalk, G. Tkačik, O. Marre, T. Mora, Physical Review
E 98 (2018).
date_created: 2018-12-11T11:44:15Z
date_published: 2018-10-17T00:00:00Z
date_updated: 2023-09-18T09:18:44Z
day: '17'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.98.042410
ec_funded: 1
external_id:
isi:
- '000447486100004'
intvolume: ' 98'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.biorxiv.org/content/10.1101/243816v2.full
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 26436750-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '785907'
name: Human Brain Project Specific Grant Agreement 2 (HBP SGA 2)
publication: Physical Review E
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Physical Society
publist_id: '8024'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Separating intrinsic interactions from extrinsic correlations in a network
of sensory neurons
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 98
year: '2018'
...
---
_id: '700'
abstract:
- lang: eng
text: Microtubules provide the mechanical force required for chromosome separation
during mitosis. However, little is known about the dynamic (high-frequency) mechanical
properties of microtubules. Here, we theoretically propose to control the vibrations
of a doubly clamped microtubule by tip electrodes and to detect its motion via
the optomechanical coupling between the vibrational modes of the microtubule and
an optical cavity. In the presence of a red-detuned strong pump laser, this coupling
leads to optomechanical-induced transparency of an optical probe field, which
can be detected with state-of-the art technology. The center frequency and line
width of the transparency peak give the resonance frequency and damping rate of
the microtubule, respectively, while the height of the peak reveals information
about the microtubule-cavity field coupling. Our method opens the new possibilities
to gain information about the physical properties of microtubules, which will
enhance our capability to design physical cancer treatment protocols as alternatives
to chemotherapeutic drugs.
article_number: '012404'
author:
- first_name: Shabir
full_name: Barzanjeh, Shabir
id: 2D25E1F6-F248-11E8-B48F-1D18A9856A87
last_name: Barzanjeh
orcid: 0000-0003-0415-1423
- first_name: Vahid
full_name: Salari, Vahid
last_name: Salari
- first_name: Jack
full_name: Tuszynski, Jack
last_name: Tuszynski
- first_name: Michal
full_name: Cifra, Michal
last_name: Cifra
- first_name: Christoph
full_name: Simon, Christoph
last_name: Simon
citation:
ama: Barzanjeh S, Salari V, Tuszynski J, Cifra M, Simon C. Optomechanical proposal
for monitoring microtubule mechanical vibrations. Physical Review E Statistical
Nonlinear and Soft Matter Physics . 2017;96(1). doi:10.1103/PhysRevE.96.012404
apa: Barzanjeh, S., Salari, V., Tuszynski, J., Cifra, M., & Simon, C. (2017).
Optomechanical proposal for monitoring microtubule mechanical vibrations.
Physical Review E Statistical Nonlinear and Soft Matter Physics . American
Institute of Physics. https://doi.org/10.1103/PhysRevE.96.012404
chicago: Barzanjeh, Shabir, Vahid Salari, Jack Tuszynski, Michal Cifra, and Christoph
Simon. “Optomechanical Proposal for Monitoring Microtubule Mechanical Vibrations.”
Physical Review E Statistical Nonlinear and Soft Matter Physics . American
Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.96.012404.
ieee: S. Barzanjeh, V. Salari, J. Tuszynski, M. Cifra, and C. Simon, “Optomechanical
proposal for monitoring microtubule mechanical vibrations,” Physical Review
E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American
Institute of Physics, 2017.
ista: Barzanjeh S, Salari V, Tuszynski J, Cifra M, Simon C. 2017. Optomechanical
proposal for monitoring microtubule mechanical vibrations. Physical Review E
Statistical Nonlinear and Soft Matter Physics . 96(1), 012404.
mla: Barzanjeh, Shabir, et al. “Optomechanical Proposal for Monitoring Microtubule
Mechanical Vibrations.” Physical Review E Statistical Nonlinear and Soft Matter
Physics , vol. 96, no. 1, 012404, American Institute of Physics, 2017, doi:10.1103/PhysRevE.96.012404.
short: S. Barzanjeh, V. Salari, J. Tuszynski, M. Cifra, C. Simon, Physical Review
E Statistical Nonlinear and Soft Matter Physics 96 (2017).
date_created: 2018-12-11T11:48:00Z
date_published: 2017-07-12T00:00:00Z
date_updated: 2023-02-23T12:56:35Z
day: '12'
department:
- _id: JoFi
doi: 10.1103/PhysRevE.96.012404
ec_funded: 1
intvolume: ' 96'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1612.07061.pdf
month: '07'
oa: 1
oa_version: Submitted Version
project:
- _id: 258047B6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '707438'
name: 'Microwave-to-Optical Quantum Link: Quantum Teleportation and Quantum Illumination
with cavity Optomechanics'
publication: ' Physical Review E Statistical Nonlinear and Soft Matter Physics '
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Institute of Physics
publist_id: '6997'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optomechanical proposal for monitoring microtubule mechanical vibrations
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 96
year: '2017'
...
---
_id: '947'
abstract:
- lang: eng
text: Viewing the ways a living cell can organize its metabolism as the phase space
of a physical system, regulation can be seen as the ability to reduce the entropy
of that space by selecting specific cellular configurations that are, in some
sense, optimal. Here we quantify the amount of regulation required to control
a cell's growth rate by a maximum-entropy approach to the space of underlying
metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern
as described by genome-scale models. We link the mean growth rate achieved by
a population of cells to the minimal amount of metabolic regulation needed to
achieve it through a phase diagram that highlights how growth suppression can
be as costly (in regulatory terms) as growth enhancement. Moreover, we provide
an interpretation of the inverse temperature β controlling maximum-entropy distributions
based on the underlying growth dynamics. Specifically, we show that the asymptotic
value of β for a cell population can be expected to depend on (i) the carrying
capacity of the environment, (ii) the initial size of the colony, and (iii) the
probability distribution from which the inoculum was sampled. Results obtained
for E. coli and human cells are found to be remarkably consistent with empirical
evidence.
article_number: '010401'
article_processing_charge: No
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Fabrizio
full_name: Capuani, Fabrizio
last_name: Capuani
- first_name: Andrea
full_name: De Martino, Andrea
last_name: De Martino
citation:
ama: De Martino D, Capuani F, De Martino A. Quantifying the entropic cost of cellular
growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics
. 2017;96(1). doi:10.1103/PhysRevE.96.010401
apa: De Martino, D., Capuani, F., & De Martino, A. (2017). Quantifying the entropic
cost of cellular growth control. Physical Review E Statistical Nonlinear and
Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.96.010401
chicago: De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Quantifying
the Entropic Cost of Cellular Growth Control.” Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.96.010401.
ieee: D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost
of cellular growth control,” Physical Review E Statistical Nonlinear and Soft
Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.
ista: De Martino D, Capuani F, De Martino A. 2017. Quantifying the entropic cost
of cellular growth control. Physical Review E Statistical Nonlinear and Soft
Matter Physics . 96(1), 010401.
mla: De Martino, Daniele, et al. “Quantifying the Entropic Cost of Cellular Growth
Control.” Physical Review E Statistical Nonlinear and Soft Matter Physics
, vol. 96, no. 1, 010401, American Institute of Physics, 2017, doi:10.1103/PhysRevE.96.010401.
short: D. De Martino, F. Capuani, A. De Martino, Physical Review E Statistical
Nonlinear and Soft Matter Physics 96 (2017).
date_created: 2018-12-11T11:49:21Z
date_published: 2017-07-10T00:00:00Z
date_updated: 2023-09-22T10:03:50Z
day: '10'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.96.010401
ec_funded: 1
external_id:
isi:
- '000405194200002'
intvolume: ' 96'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.00219
month: '07'
oa: 1
oa_version: Submitted Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: ' Physical Review E Statistical Nonlinear and Soft Matter Physics '
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Institute of Physics
publist_id: '6470'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying the entropic cost of cellular growth control
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 96
year: '2017'
...
---
_id: '959'
abstract:
- lang: eng
text: In this work it is shown that scale-free tails in metabolic flux distributions
inferred in stationary models are an artifact due to reactions involved in thermodynamically
unfeasible cycles, unbounded by physical constraints and in principle able to
perform work without expenditure of free energy. After implementing thermodynamic
constraints by removing such loops, metabolic flux distributions scale meaningfully
with the physical limiting factors, acquiring in turn a richer multimodal structure
potentially leading to symmetry breaking while optimizing for objective functions.
article_processing_charge: No
author:
- 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: De Martino D. Scales and multimodal flux distributions in stationary metabolic
network models via thermodynamics. Physical Review E Statistical Nonlinear
and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419
apa: De Martino, D. (2017). Scales and multimodal flux distributions in stationary
metabolic network models via thermodynamics. Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.95.062419
chicago: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary
Metabolic Network Models via Thermodynamics.” Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.95.062419.
ieee: D. De Martino, “Scales and multimodal flux distributions in stationary metabolic
network models via thermodynamics,” Physical Review E Statistical Nonlinear
and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p.
062419, 2017.
ista: De Martino D. 2017. Scales and multimodal flux distributions in stationary
metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear
and Soft Matter Physics . 95(6), 062419.
mla: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary
Metabolic Network Models via Thermodynamics.” Physical Review E Statistical
Nonlinear and Soft Matter Physics , vol. 95, no. 6, American Institute of
Physics, 2017, p. 062419, doi:10.1103/PhysRevE.95.062419.
short: D. De Martino, Physical Review E Statistical Nonlinear and Soft Matter Physics 95
(2017) 062419.
date_created: 2018-12-11T11:49:25Z
date_published: 2017-06-28T00:00:00Z
date_updated: 2023-09-22T09:59:01Z
day: '28'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.95.062419
ec_funded: 1
external_id:
isi:
- '000404546400004'
intvolume: ' 95'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1703.00853.pdf
month: '06'
oa: 1
oa_version: Submitted Version
page: '062419'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: ' Physical Review E Statistical Nonlinear and Soft Matter Physics '
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Institute of Physics
publist_id: '6446'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scales and multimodal flux distributions in stationary metabolic network models
via thermodynamics
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 95
year: '2017'
...