---
_id: '292'
abstract:
- lang: eng
text: 'Retina is a paradigmatic system for studying sensory encoding: the transformation
of light into spiking activity of ganglion cells. The inverse problem, where stimulus
is reconstructed from spikes, has received less attention, especially for complex
stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred
neurons from a dense patch in a rat retina and decoded movies of multiple small
randomly-moving discs. We constructed nonlinear (kernelized and neural network)
decoders that improved significantly over linear results. An important contribution
to this was the ability of nonlinear decoders to reliably separate between neural
responses driven by locally fluctuating light signals, and responses at locally
constant light driven by spontaneous-like activity. This improvement crucially
depended on the precise, non-Poisson temporal structure of individual spike trains,
which originated in the spike-history dependence of neural responses. We propose
a general principle by which downstream circuitry could discriminate between spontaneous
and stimulus-driven activity based solely on higher-order statistical structure
in the incoming spike trains.'
article_number: e1006057
article_processing_charge: Yes
article_type: original
author:
- first_name: Vicent
full_name: Botella Soler, Vicent
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Georg S
full_name: Martius, Georg S
last_name: Martius
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. PLoS Computational Biology.
2018;14(5). doi:10.1371/journal.pcbi.1006057
apa: Botella Soler, V., Deny, S., Martius, G. S., Marre, O., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1006057
chicago: Botella Soler, Vicente, Stephane Deny, Georg S Martius, Olivier Marre,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
PLoS Computational Biology. Public Library of Science, 2018. https://doi.org/10.1371/journal.pcbi.1006057.
ieee: V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina,” PLoS Computational
Biology, vol. 14, no. 5. Public Library of Science, 2018.
ista: Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina. PLoS Computational Biology. 14(5),
e1006057.
mla: Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from
the Mammalian Retina.” PLoS Computational Biology, vol. 14, no. 5, e1006057,
Public Library of Science, 2018, doi:10.1371/journal.pcbi.1006057.
short: V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational
Biology 14 (2018).
date_created: 2018-12-11T11:45:39Z
date_published: 2018-05-10T00:00:00Z
date_updated: 2024-02-21T13:45:25Z
day: '10'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1006057
ec_funded: 1
external_id:
isi:
- '000434012100002'
file:
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checksum: 3026f94d235219e15514505fdbadf34e
content_type: application/pdf
creator: dernst
date_created: 2019-02-13T11:07:15Z
date_updated: 2020-07-14T12:45:53Z
file_id: '5974'
file_name: 2018_Plos_Botella_Soler.pdf
file_size: 3460786
relation: main_file
file_date_updated: 2020-07-14T12:45:53Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
issue: '5'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 25CBA828-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '720270'
name: Human Brain Project Specific Grant Agreement 1 (HBP SGA 1)
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/video-of-moving-discs-reconstructed-from-rat-retinal-neuron-signals/
record:
- id: '5584'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 14
year: '2018'
...
---
_id: '543'
abstract:
- lang: eng
text: A central goal in theoretical neuroscience is to predict the response properties
of sensory neurons from first principles. To this end, “efficient coding” posits
that sensory neurons encode maximal information about their inputs given internal
constraints. There exist, however, many variants of efficient coding (e.g., redundancy
reduction, different formulations of predictive coding, robust coding, sparse
coding, etc.), differing in their regimes of applicability, in the relevance of
signals to be encoded, and in the choice of constraints. It is unclear how these
types of efficient coding relate or what is expected when different coding objectives
are combined. Here we present a unified framework that encompasses previously
proposed efficient coding models and extends to unique regimes. We show that optimizing
neural responses to encode predictive information can lead them to either correlate
or decorrelate their inputs, depending on the stimulus statistics; in contrast,
at low noise, efficiently encoding the past always predicts decorrelation. Later,
we investigate coding of naturalistic movies and show that qualitatively different
types of visual motion tuning and levels of response sparsity are predicted, depending
on whether the objective is to recover the past or predict the future. Our approach
promises a way to explain the observed diversity of sensory neural responses,
as due to multiple functional goals and constraints fulfilled by different cell
types and/or circuits.
article_processing_charge: No
author:
- first_name: Matthew J
full_name: Chalk, Matthew J
id: 2BAAC544-F248-11E8-B48F-1D18A9856A87
last_name: Chalk
orcid: 0000-0001-7782-4436
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive,
and sparse coding. PNAS. 2018;115(1):186-191. doi:10.1073/pnas.1711114115
apa: Chalk, M. J., Marre, O., & Tkačik, G. (2018). Toward a unified theory of
efficient, predictive, and sparse coding. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1711114115
chicago: Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory
of Efficient, Predictive, and Sparse Coding.” PNAS. National Academy of
Sciences, 2018. https://doi.org/10.1073/pnas.1711114115.
ieee: M. J. Chalk, O. Marre, and G. Tkačik, “Toward a unified theory of efficient,
predictive, and sparse coding,” PNAS, vol. 115, no. 1. National Academy
of Sciences, pp. 186–191, 2018.
ista: Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive,
and sparse coding. PNAS. 115(1), 186–191.
mla: Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive,
and Sparse Coding.” PNAS, vol. 115, no. 1, National Academy of Sciences,
2018, pp. 186–91, doi:10.1073/pnas.1711114115.
short: M.J. Chalk, O. Marre, G. Tkačik, PNAS 115 (2018) 186–191.
date_created: 2018-12-11T11:47:04Z
date_published: 2018-01-02T00:00:00Z
date_updated: 2023-09-19T10:16:35Z
day: '02'
department:
- _id: GaTk
doi: 10.1073/pnas.1711114115
external_id:
isi:
- '000419128700049'
intvolume: ' 115'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: 'https://doi.org/10.1101/152660 '
month: '01'
oa: 1
oa_version: Submitted Version
page: 186 - 191
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '7273'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Toward a unified theory of efficient, predictive, and sparse coding
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 115
year: '2018'
...
---
_id: '5584'
abstract:
- lang: eng
text: "This package contains data for the publication \"Nonlinear decoding of a
complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
\r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
cells that pass stability and quality criteria, recorded on the multi-electrode
array, in response to the presentation of the complex movie with many randomly
moving dark discs. The responses are represented as 648000 x 91 binary matrix,
where the first index indicates the timebin of duration 12.5 ms, and the second
index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
in the particular time bin.\r\n(ii) README file and a graphical illustration of
the structure of the experiment, specifying how the 648000 timebins are split
into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments
are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
frame, with time that is locked to the recorded raster. The luminance traces are
produced as described in the manuscript by filtering the raw disc movie with a
small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella-Soler, Vicente
last_name: Botella-Soler
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98
chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:98.
ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina.” Institute of Science and
Technology Austria, 2018.
ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:98.
mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian
Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.
short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2024-02-21T13:45:26Z
day: '29'
ddc:
- '570'
department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
file:
- access_level: open_access
checksum: 6808748837b9afbbbabc2a356ca2b88a
content_type: application/octet-stream
creator: system
date_created: 2018-12-12T13:02:24Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5590'
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file_size: 1142543971
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content_type: application/pdf
creator: system
date_created: 2018-12-12T13:02:25Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5591'
file_name: IST-2018-98-v1+2_ExperimentStructure.pdf
file_size: 702336
relation: main_file
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checksum: 0c9cfb4dab35bb3dc25a04395600b1c8
content_type: application/octet-stream
creator: system
date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5592'
file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat
file_size: 432
relation: main_file
- access_level: open_access
checksum: 2a83b011012e21e934b4596285b1a183
content_type: text/plain
creator: system
date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5593'
file_name: IST-2018-98-v1+4_README.txt
file_size: 986
relation: main_file
file_date_updated: 2020-07-14T12:47:07Z
has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '292'
relation: used_in_publication
status: public
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '1104'
abstract:
- lang: eng
text: In the early visual system, cells of the same type perform the same computation
in different places of the visual field. How these cells code together a complex
visual scene is unclear. A common assumption is that cells of a single-type extract
a single-stimulus feature to form a feature map, but this has rarely been observed
directly. Using large-scale recordings in the rat retina, we show that a homogeneous
population of fast OFF ganglion cells simultaneously encodes two radically different
features of a visual scene. Cells close to a moving object code quasilinearly
for its position, while distant cells remain largely invariant to the object's
position and, instead, respond nonlinearly to changes in the object's speed. We
develop a quantitative model that accounts for this effect and identify a disinhibitory
circuit that mediates it. Ganglion cells of a single type thus do not code for
one, but two features simultaneously. This richer, flexible neural map might also
be present in other sensory systems.
article_number: '1964'
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Ulisse
full_name: Ferrari, Ulisse
last_name: Ferrari
- first_name: Emilie
full_name: Mace, Emilie
last_name: Mace
- first_name: Pierre
full_name: Yger, Pierre
last_name: Yger
- first_name: Romain
full_name: Caplette, Romain
last_name: Caplette
- first_name: Serge
full_name: Picaud, Serge
last_name: Picaud
- 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
citation:
ama: Deny S, Ferrari U, Mace E, et al. Multiplexed computations in retinal ganglion
cells of a single type. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-02159-y
apa: Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S., … Marre,
O. (2017). Multiplexed computations in retinal ganglion cells of a single type.
Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-02159-y
chicago: Deny, Stephane, Ulisse Ferrari, Emilie Mace, Pierre Yger, Romain Caplette,
Serge Picaud, Gašper Tkačik, and Olivier Marre. “Multiplexed Computations in Retinal
Ganglion Cells of a Single Type.” Nature Communications. Nature Publishing
Group, 2017. https://doi.org/10.1038/s41467-017-02159-y.
ieee: S. Deny et al., “Multiplexed computations in retinal ganglion cells
of a single type,” Nature Communications, vol. 8, no. 1. Nature Publishing
Group, 2017.
ista: Deny S, Ferrari U, Mace E, Yger P, Caplette R, Picaud S, Tkačik G, Marre O.
2017. Multiplexed computations in retinal ganglion cells of a single type. Nature
Communications. 8(1), 1964.
mla: Deny, Stephane, et al. “Multiplexed Computations in Retinal Ganglion Cells
of a Single Type.” Nature Communications, vol. 8, no. 1, 1964, Nature Publishing
Group, 2017, doi:10.1038/s41467-017-02159-y.
short: S. Deny, U. Ferrari, E. Mace, P. Yger, R. Caplette, S. Picaud, G. Tkačik,
O. Marre, Nature Communications 8 (2017).
date_created: 2018-12-11T11:50:10Z
date_published: 2017-12-06T00:00:00Z
date_updated: 2023-09-20T11:41:19Z
day: '06'
ddc:
- '571'
department:
- _id: GaTk
doi: 10.1038/s41467-017-02159-y
ec_funded: 1
external_id:
isi:
- '000417241200004'
file:
- access_level: open_access
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:06Z
date_updated: 2018-12-12T10:16:06Z
file_id: '5191'
file_name: IST-2018-921-v1+1_s41467-017-02159-y.pdf
file_size: 2872887
relation: main_file
file_date_updated: 2018-12-12T10:16:06Z
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
issue: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25CD3DD2-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '604102'
name: Localization of ion channels and receptors by two and three-dimensional immunoelectron
microscopic approaches
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: Nature Communications
publication_identifier:
issn:
- '20411723'
publication_status: published
publisher: Nature Publishing Group
publist_id: '6266'
pubrep_id: '921'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Multiplexed computations in retinal ganglion cells of a single type
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 8
year: '2017'
...
---
_id: '720'
abstract:
- lang: eng
text: 'Advances in multi-unit recordings pave the way for statistical modeling of
activity patterns in large neural populations. Recent studies have shown that
the summed activity of all neurons strongly shapes the population response. A
separate recent finding has been that neural populations also exhibit criticality,
an anomalously large dynamic range for the probabilities of different population
activity patterns. Motivated by these two observations, we introduce a class of
probabilistic models which takes into account the prior knowledge that the neural
population could be globally coupled and close to critical. These models consist
of an energy function which parametrizes interactions between small groups of
neurons, and an arbitrary positive, strictly increasing, and twice differentiable
function which maps the energy of a population pattern to its probability. We
show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an
accurate description of the activity of retinal ganglion cells which outperforms
previous models based on the summed activity of neurons; 2) prior knowledge that
the population is critical translates to prior expectations about the shape of
the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous
latent variable globally coupling the system whose distribution we can infer from
data. Our method is independent of the underlying system’s state space; hence,
it can be applied to other systems such as natural scenes or amino acid sequences
of proteins which are also known to exhibit criticality.'
article_number: e1005763
article_processing_charge: Yes
author:
- first_name: Jan
full_name: Humplik, Jan
id: 2E9627A8-F248-11E8-B48F-1D18A9856A87
last_name: Humplik
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Humplik J, Tkačik G. Probabilistic models for neural populations that naturally
capture global coupling and criticality. PLoS Computational Biology. 2017;13(9).
doi:10.1371/journal.pcbi.1005763
apa: Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations
that naturally capture global coupling and criticality. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763
chicago: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763.
ieee: J. Humplik and G. Tkačik, “Probabilistic models for neural populations that
naturally capture global coupling and criticality,” PLoS Computational Biology,
vol. 13, no. 9. Public Library of Science, 2017.
ista: Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that
naturally capture global coupling and criticality. PLoS Computational Biology.
13(9), e1005763.
mla: Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations
That Naturally Capture Global Coupling and Criticality.” PLoS Computational
Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763.
short: J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).
date_created: 2018-12-11T11:48:08Z
date_published: 2017-09-19T00:00:00Z
date_updated: 2021-01-12T08:12:21Z
day: '19'
ddc:
- '530'
- '571'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005763
file:
- access_level: open_access
checksum: 81107096c19771c36ddbe6f0282a3acb
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:30Z
date_updated: 2020-07-14T12:47:53Z
file_id: '5352'
file_name: IST-2017-884-v1+1_journal.pcbi.1005763.pdf
file_size: 14167050
relation: main_file
file_date_updated: 2020-07-14T12:47:53Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_identifier:
issn:
- 1553734X
publication_status: published
publisher: Public Library of Science
publist_id: '6960'
pubrep_id: '884'
quality_controlled: '1'
scopus_import: 1
status: public
title: Probabilistic models for neural populations that naturally capture global coupling
and criticality
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
year: '2017'
...
---
_id: '1197'
abstract:
- lang: eng
text: Across the nervous system, certain population spiking patterns are observed
far more frequently than others. A hypothesis about this structure is that these
collective activity patterns function as population codewords–collective modes–carrying
information distinct from that of any single cell. We investigate this phenomenon
in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop
a novel statistical model that decomposes the population response into modes;
it predicts the distribution of spiking activity in the ganglion cell population
with high accuracy. We found that the modes represent localized features of the
visual stimulus that are distinct from the features represented by single neurons.
Modes form clusters of activity states that are readily discriminated from one
another. When we repeated the same visual stimulus, we found that the same mode
was robustly elicited. These results suggest that retinal ganglion cells’ collective
signaling is endowed with a form of error-correcting code–a principle that may
hold in brain areas beyond retina.
acknowledgement: JSP was supported by a C.V. Starr Fellowship from the Starr Foundation
(http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation
(https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National
Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science
Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler
for helpful comments on the manuscript.
article_number: e1005855
author:
- first_name: Jason
full_name: Prentice, Jason
last_name: Prentice
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Mark
full_name: Ioffe, Mark
last_name: Ioffe
- first_name: Adrianna
full_name: Loback, Adrianna
last_name: Loback
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes
of the retinal population code. PLoS Computational Biology. 2016;12(11).
doi:10.1371/journal.pcbi.1005148
apa: Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M.
(2016). Error-robust modes of the retinal population code. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148
chicago: Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik,
and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS
Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148.
ieee: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust
modes of the retinal population code,” PLoS Computational Biology, vol.
12, no. 11. Public Library of Science, 2016.
ista: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust
modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.
mla: Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.”
PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of
Science, 2016, doi:10.1371/journal.pcbi.1005148.
short: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational
Biology 12 (2016).
date_created: 2018-12-11T11:50:40Z
date_published: 2016-11-17T00:00:00Z
date_updated: 2023-02-23T14:05:40Z
day: '17'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1005148
file:
- access_level: open_access
checksum: 47b08cbd4dbf32b25ba161f5f4b262cc
content_type: application/pdf
creator: kschuh
date_created: 2019-01-25T10:35:00Z
date_updated: 2020-07-14T12:44:38Z
file_id: '5884'
file_name: 2016_PLOS_Prentice.pdf
file_size: 4492021
relation: main_file
file_date_updated: 2020-07-14T12:44:38Z
has_accepted_license: '1'
intvolume: ' 12'
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '6153'
quality_controlled: '1'
related_material:
record:
- id: '9709'
relation: research_data
status: public
scopus_import: 1
status: public
title: Error-robust modes of the retinal population code
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2016'
...
---
_id: '1248'
abstract:
- lang: eng
text: Life depends as much on the flow of information as on the flow of energy.
Here we review the many efforts to make this intuition precise. Starting with
the building blocks of information theory, we explore examples where it has been
possible to measure, directly, the flow of information in biological networks,
or more generally where information-theoretic ideas have been used to guide the
analysis of experiments. Systems of interest range from single molecules (the
sequence diversity in families of proteins) to groups of organisms (the distribution
of velocities in flocks of birds), and all scales in between. Many of these analyses
are motivated by the idea that biological systems may have evolved to optimize
the gathering and representation of information, and we review the experimental
evidence for this optimization, again across a wide range of scales.
acknowledgement: "Our work was supported in part by the US\r\nNational Science Foundation
(PHY–1305525 and CCF–\r\n0939370), by the Austrian Science Foundation (FWF\r\nP25651),
by the Human Frontiers Science Program, and\r\nby the Simons and Swartz Foundations."
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Bialek W. Information processing in living systems. Annual Review
of Condensed Matter Physics. 2016;7:89-117. doi:10.1146/annurev-conmatphys-031214-014803
apa: Tkačik, G., & Bialek, W. (2016). Information processing in living systems.
Annual Review of Condensed Matter Physics. Annual Reviews. https://doi.org/10.1146/annurev-conmatphys-031214-014803
chicago: Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.”
Annual Review of Condensed Matter Physics. Annual Reviews, 2016. https://doi.org/10.1146/annurev-conmatphys-031214-014803.
ieee: G. Tkačik and W. Bialek, “Information processing in living systems,” Annual
Review of Condensed Matter Physics, vol. 7. Annual Reviews, pp. 89–117, 2016.
ista: Tkačik G, Bialek W. 2016. Information processing in living systems. Annual
Review of Condensed Matter Physics. 7, 89–117.
mla: Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.”
Annual Review of Condensed Matter Physics, vol. 7, Annual Reviews, 2016,
pp. 89–117, doi:10.1146/annurev-conmatphys-031214-014803.
short: G. Tkačik, W. Bialek, Annual Review of Condensed Matter Physics 7 (2016)
89–117.
date_created: 2018-12-11T11:50:56Z
date_published: 2016-03-10T00:00:00Z
date_updated: 2021-01-12T06:49:23Z
day: '10'
department:
- _id: GaTk
doi: 10.1146/annurev-conmatphys-031214-014803
intvolume: ' 7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1412.8752
month: '03'
oa: 1
oa_version: Preprint
page: 89 - 117
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: Annual Review of Condensed Matter Physics
publication_status: published
publisher: Annual Reviews
publist_id: '6080'
quality_controlled: '1'
scopus_import: 1
status: public
title: Information processing in living systems
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 7
year: '2016'
...
---
_id: '1697'
abstract:
- lang: eng
text: Motion tracking is a challenge the visual system has to solve by reading out
the retinal population. It is still unclear how the information from different
neurons can be combined together to estimate the position of an object. Here we
recorded a large population of ganglion cells in a dense patch of salamander and
guinea pig retinas while displaying a bar moving diffusively. We show that the
bar’s position can be reconstructed from retinal activity with a precision in
the hyperacuity regime using a linear decoder acting on 100+ cells. We then took
advantage of this unprecedented precision to explore the spatial structure of
the retina’s population code. The classical view would have suggested that the
firing rates of the cells form a moving hill of activity tracking the bar’s position.
Instead, we found that most ganglion cells in the salamander fired sparsely and
idiosyncratically, so that their neural image did not track the bar. Furthermore,
ganglion cell activity spanned an area much larger than predicted by their receptive
fields, with cells coding for motion far in their surround. As a result, population
redundancy was high, and we could find multiple, disjoint subsets of neurons that
encoded the trajectory with high precision. This organization allows for diverse
collections of ganglion cells to represent high-accuracy motion information in
a form easily read out by downstream neural circuits.
acknowledgement: 'This work was supported by grants EY 014196 and EY 017934 to MJB,
ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence
Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the
Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to
VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010-
22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).'
article_number: e1004304
author:
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella Soler, Vicente
id: 421234E8-F248-11E8-B48F-1D18A9856A87
last_name: Botella Soler
orcid: 0000-0002-8790-1914
- first_name: Kristina
full_name: Simmons, Kristina
last_name: Simmons
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Michael
full_name: Berry, Michael
last_name: Berry
citation:
ama: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy
decoding of dynamical motion from a large retinal population. PLoS Computational
Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304
apa: Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry,
M. (2015). High accuracy decoding of dynamical motion from a large retinal population.
PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304
chicago: Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora,
Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion
from a Large Retinal Population.” PLoS Computational Biology. Public Library
of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304.
ieee: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry,
“High accuracy decoding of dynamical motion from a large retinal population,”
PLoS Computational Biology, vol. 11, no. 7. Public Library of Science,
2015.
ista: Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High
accuracy decoding of dynamical motion from a large retinal population. PLoS Computational
Biology. 11(7), e1004304.
mla: Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large
Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304,
Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304.
short: O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS
Computational Biology 11 (2015).
date_created: 2018-12-11T11:53:31Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2021-01-12T06:52:35Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1004304
file:
- access_level: open_access
checksum: 472b979f3f1cffb37b3e503f085115ca
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:25Z
date_updated: 2020-07-14T12:45:12Z
file_id: '5212'
file_name: IST-2016-455-v1+1_journal.pcbi.1004304.pdf
file_size: 4673930
relation: main_file
file_date_updated: 2020-07-14T12:45:12Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '7'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '5447'
pubrep_id: '455'
quality_controlled: '1'
scopus_import: 1
status: public
title: High accuracy decoding of dynamical motion from a large retinal population
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: 11
year: '2015'
...
---
_id: '1701'
abstract:
- lang: eng
text: 'The activity of a neural network is defined by patterns of spiking and silence
from the individual neurons. Because spikes are (relatively) sparse, patterns
of activity with increasing numbers of spikes are less probable, but, with more
spikes, the number of possible patterns increases. This tradeoff between probability
and numerosity is mathematically equivalent to the relationship between entropy
and energy in statistical physics. We construct this relationship for populations
of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination
of direct and model-based analyses of experiments on the response of this network
to naturalistic movies. We see signs of a thermodynamic limit, where the entropy
per neuron approaches a smooth function of the energy per neuron as N increases.
The form of this function corresponds to the distribution of activity being poised
near an unusual kind of critical point. We suggest further tests of criticality,
and give a brief discussion of its functional significance. '
acknowledgement: "Research was supported in part by National Science Foundation Grants
PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant
R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support
was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation,
by the W. M. Keck Foundation, and by the Simons Foundation."
author:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Thierry
full_name: Mora, Thierry
last_name: Mora
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Dario
full_name: Amodei, Dario
last_name: Amodei
- first_name: Stephanie
full_name: Palmer, Stephanie
last_name: Palmer
- first_name: Michael
full_name: Berry Ii, Michael
last_name: Berry Ii
- first_name: William
full_name: Bialek, William
last_name: Bialek
citation:
ama: Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality
in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112
apa: Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &
Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of
neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112
chicago: Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer,
Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality
in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112.
ieee: G. Tkačik et al., “Thermodynamics and signatures of criticality in
a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences,
pp. 11508–11513, 2015.
ista: Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015.
Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37),
11508–11513.
mla: Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network
of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015,
pp. 11508–13, doi:10.1073/pnas.1514188112.
short: G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek,
PNAS 112 (2015) 11508–11513.
date_created: 2018-12-11T11:53:33Z
date_published: 2015-09-15T00:00:00Z
date_updated: 2021-01-12T06:52:37Z
day: '15'
department:
- _id: GaTk
doi: 10.1073/pnas.1514188112
external_id:
pmid:
- '26330611'
intvolume: ' 112'
issue: '37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/
month: '09'
oa: 1
oa_version: Submitted Version
page: 11508 - 11513
pmid: 1
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5440'
quality_controlled: '1'
scopus_import: 1
status: public
title: Thermodynamics and signatures of criticality in a network of neurons
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '1886'
abstract:
- lang: eng
text: 'Information processing in the sensory periphery is shaped by natural stimulus
statistics. In the periphery, a transmission bottleneck constrains performance;
thus efficient coding implies that natural signal components with a predictably
wider range should be compressed. In a different regime—when sampling limitations
constrain performance—efficient coding implies that more resources should be allocated
to informative features that are more variable. We propose that this regime is
relevant for sensory cortex when it extracts complex features from limited numbers
of sensory samples. To test this prediction, we use central visual processing
as a model: we show that visual sensitivity for local multi-point spatial correlations,
described by dozens of independently-measured parameters, can be quantitatively
predicted from the structure of natural images. This suggests that efficient coding
applies centrally, where it extends to higher-order sensory features and operates
in a regime in which sensitivity increases with feature variability.'
article_number: e03722
author:
- first_name: Ann
full_name: Hermundstad, Ann
last_name: Hermundstad
- first_name: John
full_name: Briguglio, John
last_name: Briguglio
- first_name: Mary
full_name: Conte, Mary
last_name: Conte
- first_name: Jonathan
full_name: Victor, Jonathan
last_name: Victor
- first_name: Vijay
full_name: Balasubramanian, Vijay
last_name: Balasubramanian
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G.
Variance predicts salience in central sensory processing. eLife. 2014;(November).
doi:10.7554/eLife.03722
apa: Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V.,
& Tkačik, G. (2014). Variance predicts salience in central sensory processing.
ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.03722
chicago: Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian,
and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.”
ELife. eLife Sciences Publications, 2014. https://doi.org/10.7554/eLife.03722.
ieee: A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and
G. Tkačik, “Variance predicts salience in central sensory processing,” eLife,
no. November. eLife Sciences Publications, 2014.
ista: Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G.
2014. Variance predicts salience in central sensory processing. eLife. (November),
e03722.
mla: Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.”
ELife, no. November, e03722, eLife Sciences Publications, 2014, doi:10.7554/eLife.03722.
short: A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G.
Tkačik, ELife (2014).
date_created: 2018-12-11T11:54:32Z
date_published: 2014-11-14T00:00:00Z
date_updated: 2021-01-12T06:53:50Z
day: '14'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.7554/eLife.03722
file:
- access_level: open_access
checksum: 766ac8999ac6e3364f10065a06024b8f
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:04Z
date_updated: 2020-07-14T12:45:20Z
file_id: '4922'
file_name: IST-2016-420-v1+1_e03722.full.pdf
file_size: 5117086
relation: main_file
file_date_updated: 2020-07-14T12:45:20Z
has_accepted_license: '1'
issue: November
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publication: eLife
publication_status: published
publisher: eLife Sciences Publications
publist_id: '5209'
pubrep_id: '420'
quality_controlled: '1'
scopus_import: 1
status: public
title: Variance predicts salience in central sensory processing
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2014'
...