Evidence map›Paper›PMID 35948839›Full record

ArticleJournal of computational neuroscience2022

Dynamic branching in a neural network model for probabilistic prediction of sequences.

Elif Köksal Ersöz, Pascal Chossat, Martin Krupa, Frédéric Lavigne

Abstract read
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In one paragraph

Article in Journal of computational neuroscience, 2022. 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

4 authors.

Elif Köksal ErsözUniv Rennes, INSERM, LTSI - UMR 1099, Campus Beaulieu, Rennes, F-35000, France. elif.koksal-ersoz@inserm.fr.ORCID http://orcid.org/0000-0003-3696-7953
Pascal ChossatProject Team MathNeuro, INRIA-CNRS-UNS, 2004 route des Lucioles-BP 93, Sophia Antipolis, 06902, France.
Martin KrupaProject Team MathNeuro, INRIA-CNRS-UNS, 2004 route des Lucioles-BP 93, Sophia Antipolis, 06902, France.
Frédéric LavigneUniversité Côte d'Azur, CNRS-BCL, Campus Saint Jean d'Angely, Nice, 06300, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.

Indexed as

Models, NeurologicalNeural Networks, ComputerNeural InhibitionNeuronsSynapsesLatching dynamicsLocal inhibitionNeuronal gainShort-term depressionSlow-fast dynamics

Identifiers

PMID35948839

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.