Evidence map›Paper›PMID 37428907›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.

Nikolai M Chapochnikov, Cengiz Pehlevan, Dmitri B Chklovskii

Open access · hybridAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

11 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Simultaneous detection and estimation in olfactory sensing.bioRxiv : the preprint server for biology · 2025
    Article
  4. Article
  5. Article
  6. Reply to Castro et al.: Do connectomes possess markers of activity-dependent synaptic plasticity?Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  7. Evaluating traces of Hebbian plasticity in theProceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  8. Article
  9. Article
  10. Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  11. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.Advances in neural information processing systems · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Nikolai M ChapochnikovCenter for Computation Neuroscience, Flatiron Institute, New York, NY 10010.ORCID 0000-0001-8491-0017
Cengiz PehlevanJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.ORCID 0000-0001-9767-6063
Dmitri B ChklovskiiCenter for Computation Neuroscience, Flatiron Institute, New York, NY 10010.
Flatiron Health (United States) · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the

Indexed as

ConnectomeOlfactory Receptor NeuronsAnimalsDrosophilaLarvaSmellclusteringconnectomeencodingnormative approacholfaction

Identifiers

PMID37428907
PMCPMC10629579
OpenAlexW4383752910

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.