Evidence map›Paper›PMID 32811844›Full record

ArticleScientific reports2020

Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo.

Katsuhiko Hata, Osamu Araki, Osamu Yokoi, Tatsumi Kusakabe, Yoshio Yamamoto, Susumu Ito, Tetsuro Nikuni

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

  1. Uncertainty minimization and pattern recognition inJournal of the Royal Society, Interface · 2025
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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

7 authors.

Katsuhiko HataDepartment of Sports and Medical Science, Kokushikan University, Tokyo, Japan. khata@kokushikan.ac.jp.
Osamu ArakiDepartment of Applied Physics, Faculty of Science Division I, Tokyo University of Science, Tokyo, Japan.
Osamu YokoiDepartment of Neuroscience, Research Center for Mathematical Medicine, Tokyo, Japan.
Tatsumi KusakabeDepartment of Sports and Medical Science, Kokushikan University, Tokyo, Japan.
Yoshio YamamotoLaboratory of Veterinary Biochemistry and Cell Biology, Faculty of Agriculture, Iwate University, Morioka, Japan.
Susumu ItoDepartment of Sports and Medical Science, Kokushikan University, Tokyo, Japan.
Tetsuro NikuniDepartment of Physics, Faculty of Science Division I, Tokyo University of Science, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two elements of neural information processing have primarily been proposed: firing rate and spike timing of neurons. In the case of synaptic plasticity, although spike-timing-dependent plasticity (STDP) depending on presynaptic and postsynaptic spike times had been considered the most common rule, recent studies have shown the inhibitory nature of the brain in vivo for precise spike timing, which is key to the STDP. Thus, the importance of the firing frequency in synaptic plasticity in vivo has been recognized again. However, little is understood about how the frequency-dependent synaptic plasticity (FDP) is regulated in vivo. Here, we focused on the presynaptic input pattern, the intracellular calcium decay time constants, and the background synaptic activity, which vary depending on neuron types and the anatomical and physiological environment in the brain. By analyzing a calcium-based model, we found that the synaptic weight differs depending on these factors characteristic in vivo, even if neurons receive the same input rate. This finding suggests the involvement of multifaceted factors other than input frequency in FDP and even neural coding in vivo.

Indexed as

Action PotentialsBrainComputational BiologyComputer SimulationHumansModels, NeurologicalModels, TheoreticalNeuronal PlasticityNeuronsSynapsesSynaptic Transmission

Identifiers

PMID32811844
PMCPMC7435278

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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.