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Open access
Springer Nature
Scientific Reports
,  volume 13 ,  issue 1 ,  publication number 3997

Multitask computation through dynamics in recurrent spiking neural networks

Publication type: Journal Article
Publication date: 2023-03-10
Springer Nature
scimago Q1
wos Q1
SJR: 0.874
CiteScore: 6.7
Impact factor: 3.9
ISSN: 20452322
Multidisciplinary
Abstract

In this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks. These models are designed by considering neurocognitive activity as computational processes through dynamics. Trained by input–output examples, these spiking neural networks are reverse engineered to find the dynamic mechanisms that are fundamental to their performance. We show that considering multitasking and spiking within one system provides insightful ideas on the principles of neural computation.

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GOST Copy
Pugavko M. M., Maslennikov O. V., NEKORKIN V. I. Multitask computation through dynamics in recurrent spiking neural networks // Scientific Reports. 2023. Vol. 13. No. 1. 3997
GOST all authors (up to 50) Copy
Pugavko M. M., Maslennikov O. V., NEKORKIN V. I. Multitask computation through dynamics in recurrent spiking neural networks // Scientific Reports. 2023. Vol. 13. No. 1. 3997
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1038/s41598-023-31110-z
UR - https://doi.org/10.1038/s41598-023-31110-z
TI - Multitask computation through dynamics in recurrent spiking neural networks
T2 - Scientific Reports
AU - Pugavko, Mechislav M.
AU - Maslennikov, Oleg V
AU - NEKORKIN, VLADIMIR I.
PY - 2023
DA - 2023/03/10
PB - Springer Nature
IS - 1
VL - 13
PMID - 36899052
SN - 2045-2322
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2023_Pugavko,
author = {Mechislav M. Pugavko and Oleg V Maslennikov and VLADIMIR I. NEKORKIN},
title = {Multitask computation through dynamics in recurrent spiking neural networks},
journal = {Scientific Reports},
year = {2023},
volume = {13},
publisher = {Springer Nature},
month = {mar},
url = {https://doi.org/10.1038/s41598-023-31110-z},
number = {1},
pages = {3997},
doi = {10.1038/s41598-023-31110-z}
}