Neuromorphic model of associative memory in the formation of the working environment description of a robotic agent

Neuromorphic model of associative memory in the formation of the working environment description of a robotic agent

Anton M. Korsakov
Russian State Scientific Center for Robotics and Technical Cybernetics (RTC), Senior Research Scientist, 21, Tikhoretsky pr., Saint Petersburg, 194064, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-6132-7504

Received February 17, 2022

The article considers an approach to solving the problem of describing the workspace of a robotic agent based on the neuromorphic principle. A segmental spike model of a neuron with the possibility of structural adaptation was used as a basic element in the modeling. The main features of the neuron model used in terms of the possibilities of its structural reconfiguration are indicated. As the basis of the model for such a description, a scheme for the formation of a conditioned reflex in living organisms is chosen. The structural scheme of the neuromorphic model of conditioned reflex formation is given, as well as the general scheme of the model of associative memory formation. A step-by-step description of the algorithm for forming associative links in such a scheme is given. The principle of the formation of inhibitory connections when competing features appear in several objects is presented. The article presents the results of computer modeling on a model example. The conclusion is made about the applicability of the chosen neuron model and the scheme of organizing neurons into a network to solve the problem of describing the workspace of a robotic agent.

Key words
Compartmental spiking neuron model, neuromorphic systems, conditioned reflex, associative memory, reconfigurable growing neural networks.

This work was done as the part of the state task of the Ministry of Education and Science of Russia No. 075-01623-22-00 «Research and development of a biosimilar system for controlling the behavior of mobile robots based on energy-efficient software and hardware neuromorphic tools».


Bibliographic description
Korsakov, A.M., 2022. Neuromorphic model of associative memory in the formation of the working environment description of a robotic agent. Robotics and Technical Cybernetics, 10(3), pp.190-200.

UDC identifier:


  1. Anderson, J.R., 1976. Language, Memory, and Thought. Hillsdale, New Jersey : L. Erlbaum Associates Inc Publishers Publ., p.546.
  2. Pavlov, I.P., 2010. Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. Annals of Neurosciences, 17 (3). DOI: 10.5214/ans.0972-7531.1017309.
  3. Cooper, L.N., 2000. Memories and memory: a physicist’s approach to the brain. International journal of Modern Physics A. WORLD SCIENTIFIC PUBLISHING CO PTE LTD, 15(26), pp.4069-4082.
  4. Gurney K., 2018. An Introduction to Neural Networks. London: CRC press Publ., p.234. DOI: 10.1201/9781315273570.
  5. Hopfield, J.J., 1982. Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences. National Acad Sciences, 79(8), pp.2554-2558.
  6. Munakata, T., 2008. Fundamentals of the new Artificial Intelligence: Neural, Evolutionary, Fuzzy and More. Springer Science & Business Media Publ., p.267.
  7. Pershin, Y.V. and Di Ventra M., 2010. Experimental demonstration of associative memory with memristive neural networks. Neural Networks. Elsevier, 23(7), pp.881-886.
  8. Zhang, Y., Zeng, Z. and Wen, S., 2014. Implementation of memristive neural networks with spike-rate-dependent plasticity synapses. In: 2014 International Joint Conference on Neural Networks (IJCNN), 2226-2233.
  9. Liu, X., Zeng, Z. and Wen, S., 2016. Implementation of Memristive Neural Network With Full-Function Pavlov Associative Memory. In: IEEE Transactions on Circuits and Systems I: Regular Papers, 63(9), pp.1454-1463.
  10. An, H., An, Q. and Yi, Y., 2021. Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits. In: IEEE Transactions on Emerging Topics in Computational Intelligence, 5(4), pp.668-678.
  11. Bakhshiev, A.V., Gundelakh, F.V., 2015. Mathematical Model of the Impulses Transformation Processes in Natural Neurons for Bio-logically Inspired Control Systems Development. In: Supplementary Proceedings of the 4th International Conference on Analysis of Images, Social Networks and Texts (AIST-SUP 2015), Yekaterinburg, Russia, 1452, pp.1-12.
  12. Bakhshiev, A.V., Demcheva, A.A. and Stankevich, L.A., 2022. CSNM: The Compartmental Spiking Neuron Model for Developing Neuromorphic Information Processing Systems. In: Studies in Computational Intelligence, 1008 SCI, pp.327-333.
  13. Bakhshiev, A.V. et al., 2021. Strukturnaja adaptacija segmentnoj spajkovoj modeli nejrona [Structural adaptation of the segmental spike model of a neuron]. In: VII Vserossiyskaya Konferentsiya «Nelineynaya Dinamika v Kognitivnykh Issledovaniyakh – 2021»: trudy[VII All-Russian Conference «Nonlinear Dynamics in Cognitive Research – 2021»: Proceedings], 30-33. (in Russian).
  14. Asratjan, Je.A., 1956. Ucheniye akademika I.P. Pavlova o vysshey nervnoy deyatel'nosti [The teachings of academician I.P. Pavlova on higher nervous activity]. Moscow: Znanie Publ., 31. (in Russian).
  15. Korsakov, A. et al., 2021. Realizatsiya povedencheskikh funktsiy na spaykovykh neyronnykh setyakh [Behavioral functions implementation on spiking neural networks]. Informatika i Avtomatizatsiya [Informatics and Automation], 20(3), pp.591-622.
  16. Bakhshiev, A.V., 2010. Biblioteka sredstv razrabotki modelej nejronnyh setej so slozhnoj i dinamicheski menjajushhejsja arhitekturoj–NMSDK [Library of tools for developing models of neural networks with a complex and dynamically changing architecture – NMSDK]. In: Vserossiyskiy Seminar «Neyroinformatika, Yeye Prilozheniya i Analiz Dannykh»: Materialy [XVIII All-Russian seminar «Neuroinformatics, its applications and data analysis»: materials], 8-10. (in Russian).
  17. BakhshievбV. et al., 2020. Arkhitektura Programmnoy Platformy Dlya Razrabotki Sredstv Modelirovaniya Rastushchikh Spaykovykh Neyronnykh Setey [Architecture of the Software Platform for the Development of Modeling Tools for Growing Spiking Neural Networks]. In: Vserossiyskiy Seminar «Neyroinformatika, Yeye Prilozheniya i Analiz Dannykh»: Materialy [XVIII All-Russian Seminar «Neuroinformatics, its Applications and Data Analysis»: materials], pp.3-10. (in Russian).
  18. Bakhshiev, A.V. et al., 2020. The architecture of a software platform for growing spiking neural networks simulator developing. Journal of Physics: Conference Series, pp. 42001.