Evidence map›Paper›PMID 25144745›Full record

ArticlePLoS computational biology2014

Sparsity and compressed coding in sensory systems.

Victor J Barranca, Gregor Kovačič, Douglas Zhou, David Cai

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.1field-weighted citation impact, top 25% 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

13 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
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  4. Optoelectronic Devices for In-Sensor Computing.Advanced materials (Deerfield Beach, Fla.) · 2025
    Article
  5. Article
  6. Sensory Coding of Limb Kinematics in Motor Cortex across a Key Developmental Transition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Article
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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

4 authors at 4 institutions in 3 countries.

Victor J BarrancaCourant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Gregor KovačičMathematical Sciences Department, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
Douglas ZhouDepartment of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
David CaiCourant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York, United States of America; NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
Courant Institute of Mathematical Sciences · USNew York University Abu Dhabi · AERensselaer Polytechnic Institute · USShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Considering that many natural stimuli are sparse, can a sensory system evolve to take advantage of this sparsity? We explore this question and show that significant downstream reductions in the numbers of neurons transmitting stimuli observed in early sensory pathways might be a consequence of this sparsity. First, we model an early sensory pathway using an idealized neuronal network comprised of receptors and downstream sensory neurons. Then, by revealing a linear structure intrinsic to neuronal network dynamics, our work points to a potential mechanism for transmitting sparse stimuli, related to compressed-sensing (CS) type data acquisition. Through simulation, we examine the characteristics of networks that are optimal in sparsity encoding, and the impact of localized receptive fields beyond conventional CS theory. The results of this work suggest a new network framework of signal sparsity, freeing the notion from any dependence on specific component-space representations. We expect our CS network mechanism to provide guidance for studying sparse stimulus transmission along realistic sensory pathways as well as engineering network designs that utilize sparsity encoding.

Indexed as

Models, NeurologicalAlgorithmsAnimalsCatsComputer SimulationRetinaRetinal Ganglion CellsSignal Transduction

Identifiers

PMID25144745
PMCPMC4140640
OpenAlexW2010419711

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.