English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The study presents a bio-inspired chaos sensor model based on the perceptronneural network for the estimation of entropy of spike train in neurodynamicsystems. After training, the sensor on perceptron, having 50 neurons in thehidden layer and 1 neuron at the output, approximates the fuzzy entropy of ashort time series with high accuracy, with a determination coefficient of R2 ~0.9. The Hindmarsh-Rose spike model was used to generate time series of spikeintervals, and datasets for training and testing the perceptron. The selectionof the hyperparameters of the perceptron model and the estimation of the sensoraccuracy were performed using the K-block cross-validation method. Even for ahidden layer with one neuron, the model approximates the fuzzy entropy withgood results and the metric R2 ~ 0.5-0.8. In a simplified model with one neuronand equal weights in the first layer, the principle of approximation is basedon the linear transformation of the average value of the time series into theentropy value. An example of using the chaos sensor on spike train of actionpotential recordings from the L5 dorsal rootlet of rat is provided. Thebio-inspired chaos sensor model based on an ensemble of neurons is able todynamically track the chaotic behavior of a spike signal and transmit thisinformation to other parts of the neurodynamic model for further processing.The study will be useful for specialists in the field of computationalneuroscience, and also to create humanoid and animal robots, and bio-robotswith limited resources.

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