---
_id: '2863'
abstract:
- lang: eng
text: Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional probability
distribution over neural codewords given the sensory input. For large populations,
direct sampling of these distributions is impossible, and so we must rely on constructing
appropriate models. We show here that in a population of 100 retinal ganglion
cells in the salamander retina responding to temporal white-noise stimuli, dependencies
between cells play an important encoding role. We introduce the stimulus-dependent
maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear
model of a single neuron, to a pairwise-coupled neural population. We find that
the SDME model gives a more accurate account of single cell responses and in particular
significantly outperforms uncoupled models in reproducing the distributions of
population codewords emitted in response to a stimulus. We show how the SDME model,
in conjunction with static maximum entropy models of population vocabulary, can
be used to estimate information-theoretic quantities like average surprise and
information transmission in a neural population.
article_number: e1002922
author:
- first_name: Einat
full_name: Granot Atedgi, Einat
last_name: Granot Atedgi
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Ronen
full_name: Segev, Ronen
last_name: Segev
- first_name: Elad
full_name: Schneidman, Elad
last_name: Schneidman
citation:
ama: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum
entropy models of neural population codes. PLoS Computational Biology.
2013;9(3). doi:10.1371/journal.pcbi.1002922
apa: Granot Atedgi, E., Tkačik, G., Segev, R., & Schneidman, E. (2013). Stimulus-dependent
maximum entropy models of neural population codes. PLoS Computational Biology.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1002922
chicago: Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman.
“Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS
Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1002922.
ieee: E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent
maximum entropy models of neural population codes,” PLoS Computational Biology,
vol. 9, no. 3. Public Library of Science, 2013.
ista: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent
maximum entropy models of neural population codes. PLoS Computational Biology.
9(3), e1002922.
mla: Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of
Neural Population Codes.” PLoS Computational Biology, vol. 9, no. 3, e1002922,
Public Library of Science, 2013, doi:10.1371/journal.pcbi.1002922.
short: E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational
Biology 9 (2013).
date_created: 2018-12-11T12:00:00Z
date_published: 2013-03-01T00:00:00Z
date_updated: 2021-01-12T07:00:20Z
day: '01'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1002922
file:
- access_level: open_access
checksum: 5a30876c193209fa05b26db71845dd16
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:45Z
date_updated: 2020-07-14T12:45:52Z
file_id: '5099'
file_name: IST-2013-120-v1+1_journal.pcbi.1002922.pdf
file_size: 1548120
relation: main_file
file_date_updated: 2020-07-14T12:45:52Z
has_accepted_license: '1'
intvolume: ' 9'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_status: published
publisher: Public Library of Science
publist_id: '3926'
pubrep_id: '120'
quality_controlled: '1'
scopus_import: 1
status: public
title: Stimulus-dependent maximum entropy models of neural population codes
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9
year: '2013'
...