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Matematicheskaya Biologiya i Bioinformatika, 2019, Volume 14, Issue 2, Pages 649–664
DOI: https://doi.org/10.17537/2019.14.649
(Mi mbb409)
 

This article is cited in 3 scientific papers (total in 3 papers)

Mathematical Modeling

Generalized memory of STDP-driven spiking neural network

S. A. Lobov

Lobachevski State University of Nizhni Novgorod, Russia
Full-text PDF (829 kB) Citations (3)
References:
Abstract: We propose a memory model based on the spiking neural network with Spike-Timing-Dependent Plasticity (STDP). In the model, information is recorded using local external stimulation. The memory decoding is a functional response in the form of population bursts of action potentials synchronized with the applied stimuli. In our model, STDP-mediated weights rearrangements are able to encode the localization of the applied stimulation, while the stimulation focus forms the source of the vector field of synaptic connections. Based on the characteristics of this field, we propose a measure of generalized network memory.
With repeated stimulations, we can observe a decrease in time until synchronous activity occurs. In this case, the obtained average learning curve and the dependence of the generalized memory on the stimulation number are characterized by a power-law. We show that the maximum time to reach a functional response is determined by the generalized memory remaining as a result of previous stimulations. Thus, the properly learning curves are due to the presence of incomplete forgetting of previous influences.
We study the reliability of generalized network memory, determined by the storage time of memory traces after the termination of external stimulation. The reliability depends on the level of neural noise, and this dependence is also power-law. We found that hubs – neurons that can initiate the generation of population bursts in the absence of noise – play a key role in maintaining generalized network memory. The inclusion of neural noise leads to the occurrence of random bursts initiated by neurons that are not hubs. This noise activity destroys memory traces and reduces the reliability of generalized network memory.
Key words: population bursts, network synchronization, synaptic plasticity, structural and functional rearrangements, learning curves, hub neurons, vector weights field, memory reliability, neural noise.
Funding agency Grant number
Russian Foundation for Basic Research 18-29-10068 мк
Received 04.09.2019, 11.12.2019, Published 23.12.2019
Document Type: Article
UDC: 51–76
Language: Russian
Citation: S. A. Lobov, “Generalized memory of STDP-driven spiking neural network”, Mat. Biolog. Bioinform., 14:2 (2019), 649–664
Citation in format AMSBIB
\Bibitem{Lob19}
\by S.~A.~Lobov
\paper Generalized memory of STDP-driven spiking neural network
\jour Mat. Biolog. Bioinform.
\yr 2019
\vol 14
\issue 2
\pages 649--664
\mathnet{http://mi.mathnet.ru/mbb409}
\crossref{https://doi.org/10.17537/2019.14.649}
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  • https://www.mathnet.ru/eng/mbb/v14/i2/p649
  • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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    Full-text PDF :251
    References:31
     
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