Version 1
: Received: 26 January 2023 / Approved: 27 January 2023 / Online: 27 January 2023 (10:32:08 CET)
How to cite:
Kovaleva, N.S.; Matrosov, V.V.; Mishchenko, M. Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity. Preprints2023, 2023010502. https://doi.org/10.20944/preprints202301.0502.v1
Kovaleva, N.S.; Matrosov, V.V.; Mishchenko, M. Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity. Preprints 2023, 2023010502. https://doi.org/10.20944/preprints202301.0502.v1
Kovaleva, N.S.; Matrosov, V.V.; Mishchenko, M. Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity. Preprints2023, 2023010502. https://doi.org/10.20944/preprints202301.0502.v1
APA Style
Kovaleva, N.S., Matrosov, V.V., & Mishchenko, M. (2023). Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity. Preprints. https://doi.org/10.20944/preprints202301.0502.v1
Chicago/Turabian Style
Kovaleva, N.S., Valery V. Matrosov and Mikhail Mishchenko. 2023 "Flexible Working Memory Model in Spiking Neural Network With Two Types of Plasticity" Preprints. https://doi.org/10.20944/preprints202301.0502.v1
Abstract
Working memory (WM) is a brain system for short-term storage and manipulation of information and plays an important role in complex cognitive tasks. In the synaptic theory of WM memorized elements are stored in the form of short-term potentiated connections in a sample population of neurons. In this paper, we show that such populations can be formed due to the mechanisms of spike-timing-dependent plasticity (STDP) – the phase dependence associated with the ratio of the pulse times of the interacting neurons. We propose a WM model considering two types of plasticity: short-term plasticity and STDP. We have shown formation of neuronal clusters encoding items in the WM model, that can be formed by external stimulation of a group of neurons due to the mechanisms of STDP and hold and reactivated by short-term plasticity mechanisms. The dynamic formation of neuronal clusters instead of pre-formed clusters gives additional flexibility to the model.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
