Abstract
The paper proposes the architecture of dynamically changing hierarchical memory based on compartmental spiking neuron model. The aim of the study is to create biologically-inspired memory models suitable for implementing the processes of features memorizing and high-level concepts. The presented architecture allows us to describe the bidirectional hierarchical structure of associative concepts related both in terms of generality and in terms of part-whole, with the ability to restore information both in the direction of generalization and in the direction of decomposition of the general concept into its component parts. A feature of the implementation is the use of a compartmental neuron model, which allows the use of a neuron to memorize objects by adding new sections of the dendritic tree. This opens the possibility of creating neural structures that are adaptive to significant changes in the environment.
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Acknowledgments
This work was done as the part of the state task of the Ministry of Education and Science of Russia No. 075-01195-20-00 “Development and study of new architectures of reconfigurable growing neural networks, methods and algorithms for their learning”.
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Bakhshiev, A., Korsakov, A., Stankevich, L. (2020). The Hierarchical Memory Based on Compartmental Spiking Neuron Model. In: Goertzel, B., Panov, A., Potapov, A., Yampolskiy, R. (eds) Artificial General Intelligence. AGI 2020. Lecture Notes in Computer Science(), vol 12177. Springer, Cham. https://doi.org/10.1007/978-3-030-52152-3_4
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DOI: https://doi.org/10.1007/978-3-030-52152-3_4
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