EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM

Authors

  • S. D. Leoshchenko National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • A. O. Oliinyk National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • S. A. Subbotin National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • Ye. O. Gofman National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • M. B. Ilyashenko National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2022-3-8

Keywords:

spiking neural network, topology, pattern, evolution, synthesis, artificial neural networks, diagnostics.

Abstract

Context. The problem of synthesizing pulsed neural networks based on an evolutionary approach to the synthesis of artificial neural networks using a neuropathic mechanism for constructing diagnostic models with a high level of accuracy is considered. The object of research is the process of synthesis of pulsed neural networks using an evolutionary approach and a neuropathic mechanism.

Objective of the work is to develop a method for synthesizing pulsed neural networks based on an evolutionary approach using a neuropathic mechanism to build diagnostic models with a high level of accuracy of work.

Method. A method for synthesizing pulsed neural networks based on an evolutionary approach is proposed. At the beginning, a population of pulsed neural networks is generated, and a neuropathic mechanism is used for their encoding and further development, which consists in separate encoding of neurons with different activation functions that are determined beforehand. So each pattern with multiple entry points can define the relationship between a pair of points. In the future, this simplifies the evolutionary development of networks. To decipher a pulsed neural network from a pattern, the coordinates for a pair of neurons are passed to the network that creates the pattern. The network output determines the weight and delay of the connection between two neurons in a pulsed neural network. After that, you can evaluate each neuromodel after evolutionary changes and check the criteria for stopping synthesis. This method allows you to reduce the resource intensity during network synthesis by abstracting the evolutionary changes of the network pattern from itself.

Results. The developed method is implemented and investigated on the example of the synthesis of a pulsed neural network for use as a model for technical diagnostics. Using the developed method to increase the accuracy of the neuromodel with a test sample by 20%, depending on the computing resources used.

Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in the synthesis of pulsed neural networks as the basis of diagnostic models for further automation of tasks of diagnostics, forecasting, evaluation and pattern recognition using big data. Prospects for further research may lie in the use of a neuropathic mechanism for indirect encoding of pulsed neural networks, which will provide even more compact data storage and speed up the synthesis process.

Author Biographies

S. D. Leoshchenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Post-graduate student of the Department of Software Tools

A. O. Oliinyk, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Professor of the Department of Software Tools

S. A. Subbotin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Head of the Department of Software Tools

Ye. O. Gofman, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Senior Researcher of the Research Unit

M. B. Ilyashenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Associate Professor, Associate Professor of the Department of Computer Systems and networks

References

Ping D. The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting. Birmingham, Packt Publishing, 2022, 442 p.

Burkov A. Machine Learning Engineering. Quebec City, True Positive Inc., 2020, 310 p.

Alkhalifa S. Machine Learning in Biotechnology and Life Sciences: Build machine learning models using Python and deploy them on the cloud. Birmingham, Packt Publishing, 2022, 408 p.

Aggarwal C. C. Neural Networks and Deep Learning: A Textbook. Berlin, Springer, 2018, 520 p.

Hiesinger P. R. The Self-Assembling Brain: How Neural Networks Grow Smarter. Princeton, Princeton University Press, 2021, 384 p.

Graupe D. Principles of Artificial Neural Networks: Basic Designs to Deep Learning (4th Edition) (Advanced Circuits and Systems). Singapore, WSPC, 2019, 438 p.

Hodgkin A., Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol, 1952, Vol. 117(4), pp. 500–544. DOI: 10.1113/jphysiol.1952.sp004764

Jolivet R., Timothy J., Gerstner W. The Spike Response Model: A Framework to Predict Neuronal Spike Trains, International conference on Artificial neural networks and neural information processing, ICANN/ICONIP'03, Istanbul, 26–29 June 2003, proceedings. Istanbul. ACM Digital Library, 2003, pp. 846– 853. DOI: 10.1007/3-540-44989-2_101

Izhikevich E. Simple model of spiking neurons, Transactions of neural networks, 2003, Vol. 14, pp. 1569–1572. DOI: 10.1109/TNN.2003.820440

Delorme A., Gautrais J., van Rullen R., Thorpe S. SpikeNET: A simulator for modeling large networks of integrate and fire neurons, Neurocomputing, 1999, Vol. 26(7), pp. 989–996. DOI: 10.1016/S0925-2312(99)00095-8

Pfeiffer M., Pfeil T. Deep learning with spiking neurons: Opportunities and challenges, Frontiers in Neuroscience, 2018, Vol. 12: 774, pp. 32–50. DOI: 10.3389/fnins.2018.00774

Maass W. Lower bounds for the computational power of networks of spiking neurons, Neural Computation, 1996, Vol. 8, pp. 1–40.

Alsayaydeh J.A.J., Aziz A., Rahman A.I.A., Salim S.N.S., Zainon M., Baharudin Z.A., Abbasi M.I., Khang A.W.Y. Development of programmable home security using GSM system for early prevention, ARPN Journal of Engineering and Applied Sciences, 2021, Vol. 16(1), pp. 88–97.

Alsayaydeh J.A.J., Indra W.A., Khang W.A.Y., Shkarupylo V., Jkatisan D.A.P.P. Development of vehicle ignition using fingerprint, ARPN Journal of Engineering and Applied Sciences, 2019, Vol. 14(23), pp. 4045–4053.

Tazerart S., Mitchell D. E., Miranda-Rottmann S., Araya R. A spike-timing-dependent plasticity rule for dendritic spines, Nature Communications, 2020, Vol. 11, pp. 12–23. DOI: 10.1038/s41467-020-17861-7

Bohte S.M. Error-backpropagation in temporally encoded networks of spiking neurons, Artificial Neural Network and Machine Learning, ICANN 2011, Espoo, 11–14 June 2011, proceedings. Espoo, ACM Digital Library, 2011, pp. 60–68.

Gerstner W., Kistler W. M. Spiking neuron models: Single neurons, populations, plasticity. Cambridge, Cambridge University Press, 2002, 496 p.

Ponulak F., Kasinski A. Supervised learning in spiking neural networks with ReSuMe: Sequence learning, classification, and spike shifting, Neural Computation, 2009, Vol. 22, pp. 467– 510. DOI: 10.1162/neco.2009.11-08-901

Gütig R., Sompolinsky H. The tempotron: A neuron that learns spike timing-based decisions, Nature Neuroscience, 2006, Vol. 9, pp. 420–428. DOI: 10.1038/nn1643

Serre T. Hierarchical models of the visual system, Encyclopedia of Computation Neuroscience. New York, Springer, 2014, 15 p. DOI: 10.1007/978-1-4614-7320-6_345-1

Leoshchenko S., Oliinyk A., Subbotin S., Gorobii N., Zaiko T. Synthesis of artificial neural networks using a modified genetic algorithm, 1st International Workshop on Informatics & DataDriven Medicine (IDDM 2018), Lviv, 28–30 October, 2018, proceedings. Lviv, CEUR WS, 2018, pp. 1–13.

Leoshchenko S. D., Oliinyk A. O., Subbotin S. A., Lytvyn V. A., Shkarupylo V. V. Modification and parallelization of genetic algorithm for synthesis of artificial neural networks, Radio Electronics, Computer Science, Control, 2019, No. 4, P. 68–82. DOI: 10.15588/1607-3274-2019-4-7

Elbrecht D., Schuman C. Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks, International Conference on Neuromorphic Systems 2020, ICONS 2020, Knoxville, 27–29 July 2020, proceedings. Knoxville, ACM Digital Library, 2020, pp. 1–5. DOI: 10.1145/3407197.3407198

HIGGS Data Set [Electronic resource], Access mode: https://archive.ics.uci.edu/ml/datasets/HIGGS

Leoshchenko S., Oliinyk A., Subbotin S., Zaiko T. A Using modern architectures of recurrent neural networks for technical diagnosis of complex systems, International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, 9–12 October 2018, proceedings. Kharkiv, IEEE, 2018, pp. 411–416. DOI: 10.1109/INFOCOMMST.2018.8632015

Published

2022-10-20

How to Cite

Leoshchenko, S. D., Oliinyk, A. O., Subbotin, S. A., Gofman, Y. O., & Ilyashenko, M. B. (2022). EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM. Radio Electronics, Computer Science, Control, (3), 77. https://doi.org/10.15588/1607-3274-2022-3-8

Issue

Section

Neuroinformatics and intelligent systems