Aleksandr Bakhshiev · AGI-20
The hierarchical memory based on compartmental spiking neuron model
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.