Evidence map›Paper›PMID 30392141›Full record

ArticleCognitive processing2019

Cumulative inhibition in neural networks.

Trond A Tjøstheim, Christian Balkenius

Abstract read
In one paragraph

Article in Cognitive processing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Trond A TjøstheimLund University Cognitive Science, Lund University, Box 117, 221 00, Lund, Sweden.
Christian BalkeniusLund University Cognitive Science, Lund University, Box 117, 221 00, Lund, Sweden. christian.balkenius@lucs.lu.se.ORCID http://orcid.org/0000-0002-1478-6329

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We show how a multi-resolution network can model the development of acuity and coarse-to-fine processing in the mammalian visual cortex. The network adapts to input statistics in an unsupervised manner, and learns a coarse-to-fine representation by using cumulative inhibition of nodes within a network layer. We show that a system of such layers can represent input by hierarchically composing larger parts from smaller components. It can also model aspects of top-down processes, such as image regeneration.

Indexed as

Models, BiologicalNeural Networks, ComputerAnimalsBehaviorHumansInterneuronsLearningPyramidal CellsVisual CortexAcuityCoarse-to-fine processingCortical microcolumnCumulative inhibitionMulti-resolutionUnsupervised learningVisual cortex

Identifiers

PMID30392141
PMCPMC6397715

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.