Evidence map›Paper›PMID 38550512›Full record

ArticleFrontiers in computational neuroscience2024

The connectivity degree controls the difficulty in reservoir design of random boolean networks.

Emmanuel Calvet, Bertrand Reulet, Jean Rouat

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in computational neuroscience, 2024. 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. Entropy, Periodicity and the Probability of Primality.Entropy (Basel, Switzerland) · 2025
    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.

Emmanuel CalvetNeurosciences Computationelles et Traitement Intelligent des Signaux (NECOTIS), Faculté de Génie, Génie Électrique et Génie Informatique (GEGI), Université de Sherbrooke, Sherbrooke, QC, Canada.
Bertrand ReuletDépartement de Physique, Institut Quantique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.
Jean RouatNeurosciences Computationelles et Traitement Intelligent des Signaux (NECOTIS), Faculté de Génie, Génie Électrique et Génie Informatique (GEGI), Université de Sherbrooke, Sherbrooke, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance

Indexed as

criticalitymemorypredictionRBNreservoir computingtopology

Identifiers

PMID38550512
PMCPMC10972873

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.