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

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2023 Aug 26

PMID 37631674

Authors

Andrei Velichko

Petr Boriskov

Maksim Belyaev

Vadim Putrolaynen

Affiliations

Soon will be listed here.

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~0.9. The Hindmarsh-Rose 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 R~0.5 ÷ 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.

References

Xu X, Nie X, Zhang J, Xu T . Multi-Level Attention Recognition of EEG Based on Feature Selection. Int J Environ Res Public Health. 2023; 20(4). PMC: 9958593. DOI: 10.3390/ijerph20043487. View

Takahashi T . Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry. 2012; 45:258-66. DOI: 10.1016/j.pnpbp.2012.05.001. View

Moss F, Ward L, Sannita W . Stochastic resonance and sensory information processing: a tutorial and review of application. Clin Neurophysiol. 2004; 115(2):267-81. DOI: 10.1016/j.clinph.2003.09.014. View

Bahmer A, Gupta D, Effenberger F . Modern Artificial Neural Networks: Is Evolution Cleverer?. Neural Comput. 2023; 35(5):763-806. DOI: 10.1162/neco_a_01575. View

Rosenblatt F . The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958; 65(6):386-408. DOI: 10.1037/h0042519. View

Velichko A, Heidari H . A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks. Entropy (Basel). 2021; 23(11). PMC: 8621949. DOI: 10.3390/e23111432. View

Hindmarsh J, Rose R . A model of neuronal bursting using three coupled first order differential equations. Proc R Soc Lond B Biol Sci. 1984; 221(1222):87-102. DOI: 10.1098/rspb.1984.0024. View

Freeman W . Simulation of chaotic EEG patterns with a dynamic model of the olfactory system. Biol Cybern. 1987; 56(2-3):139-50. DOI: 10.1007/BF00317988. View

Rohila A, Sharma A . Phase entropy: a new complexity measure for heart rate variability. Physiol Meas. 2019; 40(10):105006. DOI: 10.1088/1361-6579/ab499e. View

10.

Hindmarsh J, Rose R . A model of the nerve impulse using two first-order differential equations. Nature. 1982; 296(5853):162-4. DOI: 10.1038/296162a0. View

11.

Wu Y, Liu Y, Zhou Y, Man Q, Hu C, Asghar W . A skin-inspired tactile sensor for smart prosthetics. Sci Robot. 2020; 3(22). DOI: 10.1126/scirobotics.aat0429. View

12.

Tan H, Zhou Y, Tao Q, Rosen J, van Dijken S . Bioinspired multisensory neural network with crossmodal integration and recognition. Nat Commun. 2021; 12(1):1120. PMC: 7893014. DOI: 10.1038/s41467-021-21404-z. View

13.

Jung Y, Park B, Kim J, Kim T . Bioinspired Electronics for Artificial Sensory Systems. Adv Mater. 2018; 31(34):e1803637. DOI: 10.1002/adma.201803637. View

14.

van der Groen O, Tang M, Wenderoth N, Mattingley J . Stochastic resonance enhances the rate of evidence accumulation during combined brain stimulation and perceptual decision-making. PLoS Comput Biol. 2018; 14(7):e1006301. PMC: 6066257. DOI: 10.1371/journal.pcbi.1006301. View

15.

Del Valle M . Bioinspired sensor systems. Sensors (Basel). 2012; 11(11):10180-6. PMC: 3274279. DOI: 10.3390/s111110180. View

16.

Hanggi P . Stochastic resonance in biology. How noise can enhance detection of weak signals and help improve biological information processing. Chemphyschem. 2002; 3(3):285-90. DOI: 10.1002/1439-7641(20020315)3:3<285::AID-CPHC285>3.0.CO;2-A. View

17.

Apicella A, Donnarumma F, Isgro F, Prevete R . A survey on modern trainable activation functions. Neural Netw. 2021; 138:14-32. DOI: 10.1016/j.neunet.2021.01.026. View

18.

S A M, A H M . Synchronization of Hindmarsh Rose Neurons. Neural Netw. 2020; 123:372-380. DOI: 10.1016/j.neunet.2019.11.024. View

19.

Lopez-Gonzalez A, Tejada J, Lopez-Romero J . Review and Proposal for a Classification System of Soft Robots Inspired by Animal Morphology. Biomimetics (Basel). 2023; 8(2). PMC: 10204579. DOI: 10.3390/biomimetics8020192. View

20.

Peron S, Gabbiani F . Role of spike-frequency adaptation in shaping neuronal response to dynamic stimuli. Biol Cybern. 2009; 100(6):505-20. PMC: 2854487. DOI: 10.1007/s00422-009-0304-y. View