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Erschienen in:
2020 | OriginalPaper | Buchkapitel
The Hierarchical Memory Based on Compartmental Spiking Neuron Model
verfasst von : Aleksandr Bakhshiev, Anton Korsakov, Lev Stankevich
Erschienen in: Artificial General Intelligence
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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.