{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:25:22Z","timestamp":1760149522940,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["22-11-00055"],"award-info":[{"award-number":["22-11-00055"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 R2~0.9. The Hindmarsh\u2013Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 \u00f7 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.<\/jats:p>","DOI":"10.3390\/s23167137","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:07:10Z","timestamp":1692011230000},"page":"7137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9341-1831","authenticated-orcid":false,"given":"Andrei","family":"Velichko","sequence":"first","affiliation":[{"name":"Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2904-9612","authenticated-orcid":false,"given":"Petr","family":"Boriskov","sequence":"additional","affiliation":[{"name":"Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maksim","family":"Belyaev","sequence":"additional","affiliation":[{"name":"Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-7760","authenticated-orcid":false,"given":"Vadim","family":"Putrolaynen","sequence":"additional","affiliation":[{"name":"Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1002\/1439-7641(20020315)3:3<285::AID-CPHC285>3.0.CO;2-A","article-title":"Stochastic Resonance in Biology: How Noise Can Enhance Detection of Weak Signals and Help Improve Biological Information Processing","volume":"3","year":"2002","journal-title":"ChemPhysChem"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/nrn3061","article-title":"The Benefits of Noise in Neural Systems: Bridging Theory and Experiment","volume":"12","author":"McDonnell","year":"2011","journal-title":"Nat. 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