Features of Simulation of a Biomorphic Neural Network on Electronic Device with Non-Volatile Memory and Low Power Consumption

Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy


2016, Vol. 2. №1

Features of Simulation of a Biomorphic Neural Network on Electronic Device with Non-Volatile Memory and Low Power Consumption

About the authors:

Alexander N. Busygin, Postgraduate Student, Department of Experimental Physics and Nanotechnology, University of Tyumen; daenur.al@gmail.com

Aleksey Yu. Kuzmenko, Postgraduate Student, Department of Micro- and Nanotechnologies, Tyumen State University; alekslock@yandex.ru

Alexander D. Pisarev, Cand. Sci. (Tech.), Associate Professor, Department of Applied and Technical Physics, Institute of Physics and Technology, Head of Laboratory of Beam-Plasma Technologies, SEC “Nanotechnologies”, University of Tyumen; spcb.doc@gmail.com

Vadim A. Filippov, Cand. Sci. (Soc.), Head of the Center for Advanced Studies “Artificial Cognitive Systems”, Deputy Rector for the Program “5-100”, Tyumen State University; filippov-vadim@yandex.ru


The article discusses the electronic device for the simulation of biomorphic neural networks which combines programmable microcontrollers and a non-volatile memristor memory, and it is compatible with a personal computer. The device is positioned as a research platform to develop the most effective architecture for a corticomorphic processor. A single-layer perceptron for the primary association of the input data and the biomorphic neural network are chosen as its initial architecture. The article discusses the features of biomorphic neural networks and their adaptation to the device.


  1. Bobylev A. N., Udovichenko S. Yu. 2015. “Sozdanie elektronnogo zapominayushhego ustroystva, podobnogo po svoystvam sinapsu mozga” [The Creation of an Electronic Memory Device with Properties Similar to Organic Synapse]. Proceedings of Tomsk State University of Control Systems and Radioelectronics, no 4 (38), pp. 68-71.
  2. Chelsia A. D., Dargham J. A., Chekima A., Omatu S. 2010. “Combining neural networks for skin detection. Signal and image processing.” Signal and Image Processing : An International Journal, vol. 1, no 2, pp. 1-11.
  3. Ding Y. R., Cai Y. J., Sun P. D., Chen B. 2014. “The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality.” Journal of Applied Research and Technology, vol. 12, pp. 493-499.
  4. Güler I., Übeyli E. D. 2005. “ECG beat classifier designed by combined neural network model.” Pattern Recognition, vol. 38, pp. 199-208.
  5. Kim K.-H., Gaba S., Wheeler D., et al. 2012. “A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications.” Nano Letters, vol. 12, pp. 389-395.
  6. Merolla Р. А., et al. 2014. “A million spiking-neuron integrated circuit with a scalable communication network and interface.” Science, vol. 345, pp. 668-672.
  7. Plahl C., Kozielski M., Schluter R., Ney H. 2013. “Feature combination and stacking of recurrent and non-recurent neural networks for LVCSR.” Proceedings of 38th IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6714-6718.
  8. Prezioso M., Merrikh-Bayat F., Hoskins B. D., Adam G. C., Likharev K. K., Strukov D. B.  2015. “Training and operation of an integrated neuromorphic network based on metal-oxide memristors.” Nature, vol. 521, pp. 61-64.
  9. Udovichenko S. Yu, Bobylev A. N., Busygin A. N., Pisarev A. D., Philippov V. A. 2016. “Prototip nejromorfnogo soprocessora na osnove memristorov iz smeshannogo oksida metallov” [Neuromorphic Coprocessor Prototype Based on Mixed Metal Oxide Memristors]. In: Proceedings of the 7th Annual Conference of Nanotechnology Society of Russia, pp. 29-32.
  10. Wen C., Rebelo A., Zhang J., Cardoso J. 2015. “A new optical music recognition system based on combined neural network.” Pattern Recognition Letters, vol. 58, pp. 1-7.
  11. Zhuravsky D. V., Bobylev A. N., Udovichenko S. Yu., Philippov V. A. 2015. “Ustanovlenie podobiya svoystv sinapsa i memristora, ispolzuemogo v elektronnom ustroystve” [The Similarity of Synapse Properties and Properties of Memristor Used in an Electronic Device Establishing]. Neurocomputers: Development and Application, no 11, pp. 95-101.