Evidence map›Paper›PMID 31243531›Full record

ArticleBiological cybernetics2019

Information processing in the LGN: a comparison of neural codes and cell types.

Agnieszka Pregowska, Alex Casti, Ehud Kaplan, Eligiusz Wajnryb, Janusz Szczepanski

Abstract readComparative Study
In one paragraph

Article in Biological cybernetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Agnieszka PregowskaInstitute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106, Warsaw, Poland.
Alex CastiDepartment of Mathematics, Gildart-Haase School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NY, 07666, USA.
Ehud KaplanIcahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Eligiusz WajnrybInstitute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106, Warsaw, Poland.
Janusz SzczepanskiInstitute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106, Warsaw, Poland. jszczepa@ippt.pan.pl.ORCID 0000-0002-7985-8346

Funding

System Biology Center in New YorkP50GM071558 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2007 to 2017
$22.8M
What Determines Thalamic Spatio-Temporal Properties?R01EY016224 · NEI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KAPLAN, EHUD · 2006 to 2010
$2.3M
Modeling Reciprocal Connections in the BrainK25MH067225 · NIMH · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI CASTI, ALEXANDER ROBERT RICHARD · 2003 to 2007
$480k
Dynamics of neural ensembles in emotional stressR21MH093868 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KAPLAN, EHUD · 2011 to 2012
$464k
NEI NIH HHS R01 EY016224NIGMS NIH HHS P50 GM071558NIMH NIH HHS K25 MH067225NIMH NIH HHS R21 MH093868
6 · The paper itself

Abstract

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient "temporal code" is employed, while for X-OFF cells a straightforward "rate code" is used, which is more reliable and is correlated with energy consumption.

Indexed as

Action PotentialsAnimalsCatsGeniculate BodiesMental ProcessesNeuronsPhotic StimulationVisual CortexVisual PathwaysCat LGNEntropyFiring rateNeural codingON–OFF cellsShannon information theory

Identifiers

PMID31243531
PMCPMC6658673

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.