Evidence map›Paper›PMID 21683574›Full record

ReviewCurrent opinion in neurobiology2011

Efficient computation via sparse coding in electrosensory neural networks.

Maurice J Chacron, André Longtin, Leonard Maler

Abstract readReview
In one paragraph

Review in Current opinion in neurobiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Article
  3. Nonresponsive Neurons Improve Population Coding of Object Location.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Encoding and Perception of Electro-communication Signals inFrontiers in integrative neuroscience · 2019
    Review
  10. Novel Functions of Feedback in Electrosensory Processing.Frontiers in integrative neuroscience · 2019
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Optimized Parallel Coding of Second-Order Stimulus Features by Heterogeneous Neural Populations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016
    Article
  17. Article
  18. Article
  19. Article
  20. 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

3 authors.

Maurice J ChacronDepartment of Physiology, McGill University, Canada. maurice.chacron@mcgill.ca
André Longtin
Leonard Maler

Funding

CIHR 82838-1CIHR 83338-1CIHR 83338-2
6 · The paper itself

Abstract

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process.

Indexed as

Computer SimulationModels, NeurologicalSensationAction PotentialsAnimalsElectric FishElectric StimulationHumansNeural PathwaysNeurons

Identifiers

PMID21683574
PMCPMC4529320

What OpenQuestion holds

Textmetadata
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.