---
_id: '13230'
abstract:
- lang: eng
text: 'To interpret the sensory environment, the brain combines ambiguous sensory
measurements with knowledge that reflects context-specific prior experience. But
environmental contexts can change abruptly and unpredictably, resulting in uncertainty
about the current context. Here we address two questions: how should context-specific
prior knowledge optimally guide the interpretation of sensory stimuli in changing
environments, and do human decision-making strategies resemble this optimum? We
probe these questions with a task in which subjects report the orientation of
ambiguous visual stimuli that were drawn from three dynamically switching distributions,
representing different environmental contexts. We derive predictions for an ideal
Bayesian observer that leverages knowledge about the statistical structure of
the task to maximize decision accuracy, including knowledge about the dynamics
of the environment. We show that its decisions are biased by the dynamically changing
task context. The magnitude of this decision bias depends on the observer’s continually
evolving belief about the current context. The model therefore not only predicts
that decision bias will grow as the context is indicated more reliably, but also
as the stability of the environment increases, and as the number of trials since
the last context switch grows. Analysis of human choice data validates all three
predictions, suggesting that the brain leverages knowledge of the statistical
structure of environmental change when interpreting ambiguous sensory signals.'
acknowledgement: The authors thank Corey Ziemba and Zoe Boundy-Singer for valuable
discussion and feedback.
article_number: e1011104
article_processing_charge: No
article_type: original
author:
- first_name: Julie A.
full_name: Charlton, Julie A.
last_name: Charlton
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Yoon H.
full_name: Bai, Yoon H.
last_name: Bai
- first_name: Ann M.
full_name: Hermundstad, Ann M.
last_name: Hermundstad
- first_name: Robbe L.T.
full_name: Goris, Robbe L.T.
last_name: Goris
citation:
ama: Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. Environmental
dynamics shape perceptual decision bias. PLoS Computational Biology. 2023;19(6).
doi:10.1371/journal.pcbi.1011104
apa: Charlton, J. A., Mlynarski, W. F., Bai, Y. H., Hermundstad, A. M., & Goris,
R. L. T. (2023). Environmental dynamics shape perceptual decision bias. PLoS
Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1011104
chicago: Charlton, Julie A., Wiktor F Mlynarski, Yoon H. Bai, Ann M. Hermundstad,
and Robbe L.T. Goris. “Environmental Dynamics Shape Perceptual Decision Bias.”
PLoS Computational Biology. Public Library of Science, 2023. https://doi.org/10.1371/journal.pcbi.1011104.
ieee: J. A. Charlton, W. F. Mlynarski, Y. H. Bai, A. M. Hermundstad, and R. L. T.
Goris, “Environmental dynamics shape perceptual decision bias,” PLoS Computational
Biology, vol. 19, no. 6. Public Library of Science, 2023.
ista: Charlton JA, Mlynarski WF, Bai YH, Hermundstad AM, Goris RLT. 2023. Environmental
dynamics shape perceptual decision bias. PLoS Computational Biology. 19(6), e1011104.
mla: Charlton, Julie A., et al. “Environmental Dynamics Shape Perceptual Decision
Bias.” PLoS Computational Biology, vol. 19, no. 6, e1011104, Public Library
of Science, 2023, doi:10.1371/journal.pcbi.1011104.
short: J.A. Charlton, W.F. Mlynarski, Y.H. Bai, A.M. Hermundstad, R.L.T. Goris,
PLoS Computational Biology 19 (2023).
date_created: 2023-07-16T22:01:09Z
date_published: 2023-06-08T00:00:00Z
date_updated: 2023-08-02T06:33:50Z
day: '08'
ddc:
- '570'
department:
- _id: MaJö
doi: 10.1371/journal.pcbi.1011104
external_id:
isi:
- '001003410200003'
pmid:
- '37289753'
file:
- access_level: open_access
checksum: 800761fa2c647fabd6ad034589bc526e
content_type: application/pdf
creator: dernst
date_created: 2023-07-18T08:07:59Z
date_updated: 2023-07-18T08:07:59Z
file_id: '13247'
file_name: 2023_PloSCompBio_Charlton.pdf
file_size: 2281868
relation: main_file
success: 1
file_date_updated: 2023-07-18T08:07:59Z
has_accepted_license: '1'
intvolume: ' 19'
isi: 1
issue: '6'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Environmental dynamics shape perceptual decision bias
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: 19
year: '2023'
...
---
_id: '12349'
abstract:
- lang: eng
text: Statistics of natural scenes are not uniform - their structure varies dramatically
from ground to sky. It remains unknown whether these non-uniformities are reflected
in the large-scale organization of the early visual system and what benefits such
adaptations would confer. Here, by relying on the efficient coding hypothesis,
we predict that changes in the structure of receptive fields across visual space
increase the efficiency of sensory coding. We show experimentally that, in agreement
with our predictions, receptive fields of retinal ganglion cells change their
shape along the dorsoventral retinal axis, with a marked surround asymmetry at
the visual horizon. Our work demonstrates that, according to principles of efficient
coding, the panoramic structure of natural scenes is exploited by the retina across
space and cell-types.
acknowledged_ssus:
- _id: ScienComp
- _id: PreCl
- _id: LifeSc
- _id: Bio
acknowledgement: We thank Hiroki Asari for sharing the dataset of naturalistic images,
Anton Sumser for sharing visual stimulus code, Yoav Ben Simon for initial explorative
work with the generation of AAVs, and Tomas Vega-Zuñiga for help with immunostainings.
We also thank Gasper Tkacik and members of the Neuroethology group for their comments
on the manuscript. This research was supported by the Scientific Service Units of
IST Austria through resources provided by Scientific Computing, the Preclinical
Facility, the Lab Support Facility, and the Imaging and Optics Facility. This work
was supported by European Union Horizon 2020 Marie Skłodowska-Curie grant 665385
(DG), Austrian Science Fund (FWF) stand-alone grant P 34015 (WM), Human Frontiers
Science Program LT000256/2018-L (AS), EMBO ALTF 1098-2017 (AS) and the European
Research Council Starting Grant 756502 (MJ).
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Divyansh
full_name: Gupta, Divyansh
id: 2A485EBE-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
orcid: 0000-0001-7400-6665
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Anton L
full_name: Sumser, Anton L
id: 3320A096-F248-11E8-B48F-1D18A9856A87
last_name: Sumser
orcid: 0000-0002-4792-1881
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
orcid: 0000-0003-2012-9947
- first_name: Jan
full_name: Svaton, Jan
id: f7f724c3-9d6f-11ed-9f44-e5c5f3a5bee2
last_name: Svaton
orcid: 0000-0002-6198-2939
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
citation:
ama: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature
Neuroscience. 2023;26:606-614. doi:10.1038/s41593-023-01280-0
apa: Gupta, D., Mlynarski, W. F., Sumser, A. L., Symonova, O., Svaton, J., &
Jösch, M. A. (2023). Panoramic visual statistics shape retina-wide organization
of receptive fields. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-023-01280-0
chicago: Gupta, Divyansh, Wiktor F Mlynarski, Anton L Sumser, Olga Symonova, Jan
Svaton, and Maximilian A Jösch. “Panoramic Visual Statistics Shape Retina-Wide
Organization of Receptive Fields.” Nature Neuroscience. Springer Nature,
2023. https://doi.org/10.1038/s41593-023-01280-0.
ieee: D. Gupta, W. F. Mlynarski, A. L. Sumser, O. Symonova, J. Svaton, and M. A.
Jösch, “Panoramic visual statistics shape retina-wide organization of receptive
fields,” Nature Neuroscience, vol. 26. Springer Nature, pp. 606–614, 2023.
ista: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. 2023. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature Neuroscience.
26, 606–614.
mla: Gupta, Divyansh, et al. “Panoramic Visual Statistics Shape Retina-Wide Organization
of Receptive Fields.” Nature Neuroscience, vol. 26, Springer Nature, 2023,
pp. 606–14, doi:10.1038/s41593-023-01280-0.
short: D. Gupta, W.F. Mlynarski, A.L. Sumser, O. Symonova, J. Svaton, M.A. Jösch,
Nature Neuroscience 26 (2023) 606–614.
date_created: 2023-01-23T14:14:19Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-10-04T11:41:05Z
day: '01'
ddc:
- '570'
department:
- _id: GradSch
- _id: MaJö
doi: 10.1038/s41593-023-01280-0
ec_funded: 1
external_id:
isi:
- '000955258300002'
pmid:
- '36959418'
file:
- access_level: open_access
checksum: a33d91e398e548f34003170e10988368
content_type: application/pdf
creator: dernst
date_created: 2023-10-04T11:40:51Z
date_updated: 2023-10-04T11:40:51Z
file_id: '14395'
file_name: 2023_NatureNeuroscience_Gupta.pdf
file_size: 6144866
relation: main_file
success: 1
file_date_updated: 2023-10-04T11:40:51Z
has_accepted_license: '1'
intvolume: ' 26'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 606-614
pmid: 1
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 2634E9D2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '756502'
name: Circuits of Visual Attention
- _id: 266D407A-B435-11E9-9278-68D0E5697425
grant_number: LT000256
name: Neuronal networks of salience and spatial detection in the murine superior
colliculus
- _id: 264FEA02-B435-11E9-9278-68D0E5697425
grant_number: ALTF 1098-2017
name: Connecting sensory with motor processing in the superior colliculus
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12370'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Panoramic visual statistics shape retina-wide organization of receptive fields
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: 26
year: '2023'
...
---
_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: '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: '9439'
abstract:
- lang: eng
text: The ability to adapt to changes in stimulus statistics is a hallmark of sensory
systems. Here, we developed a theoretical framework that can account for the dynamics
of adaptation from an information processing perspective. We use this framework
to optimize and analyze adaptive sensory codes, and we show that codes optimized
for stationary environments can suffer from prolonged periods of poor performance
when the environment changes. To mitigate the adversarial effects of these environmental
changes, sensory systems must navigate tradeoffs between the ability to accurately
encode incoming stimuli and the ability to rapidly detect and adapt to changes
in the distribution of these stimuli. We derive families of codes that balance
these objectives, and we demonstrate their close match to experimentally observed
neural dynamics during mean and variance adaptation. Our results provide a unifying
perspective on adaptation across a range of sensory systems, environments, and
sensory tasks.
acknowledgement: We thank D. Kastner and T. Münch for generously providing figures
from their work. We also thank V. Jayaraman, M. Noorman, T. Ma, and K. Krishnamurthy
for useful discussions and feedback on the manuscript. W.F.M. was funded by the
European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie
Grant Agreement No. 754411. A.M.H. was supported by the Howard Hughes Medical Institute.
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: Ann M.
full_name: Hermundstad, Ann M.
last_name: Hermundstad
citation:
ama: Mlynarski WF, Hermundstad AM. Efficient and adaptive sensory codes. Nature
Neuroscience. 2021;24:998-1009. doi:10.1038/s41593-021-00846-0
apa: Mlynarski, W. F., & Hermundstad, A. M. (2021). Efficient and adaptive sensory
codes. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-021-00846-0
chicago: Mlynarski, Wiktor F, and Ann M. Hermundstad. “Efficient and Adaptive Sensory
Codes.” Nature Neuroscience. Springer Nature, 2021. https://doi.org/10.1038/s41593-021-00846-0.
ieee: W. F. Mlynarski and A. M. Hermundstad, “Efficient and adaptive sensory codes,”
Nature Neuroscience, vol. 24. Springer Nature, pp. 998–1009, 2021.
ista: Mlynarski WF, Hermundstad AM. 2021. Efficient and adaptive sensory codes.
Nature Neuroscience. 24, 998–1009.
mla: Mlynarski, Wiktor F., and Ann M. Hermundstad. “Efficient and Adaptive Sensory
Codes.” Nature Neuroscience, vol. 24, Springer Nature, 2021, pp. 998–1009,
doi:10.1038/s41593-021-00846-0.
short: W.F. Mlynarski, A.M. Hermundstad, Nature Neuroscience 24 (2021) 998–1009.
date_created: 2021-05-30T22:01:24Z
date_published: 2021-05-20T00:00:00Z
date_updated: 2023-08-08T13:51:14Z
day: '20'
department:
- _id: GaTk
doi: 10.1038/s41593-021-00846-0
ec_funded: 1
external_id:
isi:
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intvolume: ' 24'
isi: 1
language:
- iso: eng
main_file_link:
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url: 'https://doi.org/10.1101/669200 '
month: '05'
oa: 1
oa_version: Preprint
page: 998-1009
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient and adaptive sensory codes
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 24
year: '2021'
...