A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science.

Andrei Velichko, Petr Boriskov, Maksim Belyaev, Vadim Putrolaynen

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Andrei VelichkoInstitute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia.ORCID 0000-0002-9341-1831

Petr BoriskovInstitute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia.ORCID 0000-0002-2904-9612

Maksim BelyaevInstitute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia.

Vadim PutrolaynenInstitute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia.ORCID 0000-0001-5707-7760

Funding

Russian Science Foundation 22-11-00055

6 · The paper itself

Abstract

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R

Indexed as

NeurologyAction PotentialsAnimalsCluster AnalysisMachine LearningNeural Networks, ComputerRatsbiosensorchaos sensorentropyHindmarsh–Rose neuron modelperceptron

Identifiers

PMID37631674

DOI10.3390/s23167137

PMCPMC10458403

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.