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How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study
In neuroscience, the Default Mode Network (DMN), also known as the default network or the default-state network, is a large-scale brain network known to have highly correlated activities that are distinct from other networks in the brain. Many studies have revealed that DMNs can influence other cogn...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911228/ https://www.ncbi.nlm.nih.gov/pubmed/33503833 http://dx.doi.org/10.3390/brainsci11020153 |
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author | Liu, Jing Yang, Xu Zhu, Yimeng Lei, Yunlin Cai, Jian Wang, Miao Huan, Ziyi Lin, Xialv |
author_facet | Liu, Jing Yang, Xu Zhu, Yimeng Lei, Yunlin Cai, Jian Wang, Miao Huan, Ziyi Lin, Xialv |
author_sort | Liu, Jing |
collection | PubMed |
description | In neuroscience, the Default Mode Network (DMN), also known as the default network or the default-state network, is a large-scale brain network known to have highly correlated activities that are distinct from other networks in the brain. Many studies have revealed that DMNs can influence other cognitive functions to some extent. This paper is motivated by this idea and intends to further explore on how DMNs could help Spiking Neural Networks (SNNs) on image classification problems through an experimental study. The approach emphasizes the bionic meaning on model selection and parameters settings. For modeling, we select Leaky Integrate-and-Fire (LIF) as the neuron model, Additive White Gaussian Noise (AWGN) as the input DMN, and design the learning algorithm based on Spike-Timing-Dependent Plasticity (STDP). Then, we experiment on a two-layer SNN to evaluate the influence of DMN on classification accuracy, and on a three-layer SNN to examine the influence of DMN on structure evolution, where the results both appear positive. Finally, we discuss possible directions for future works. |
format | Online Article Text |
id | pubmed-7911228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79112282021-02-28 How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study Liu, Jing Yang, Xu Zhu, Yimeng Lei, Yunlin Cai, Jian Wang, Miao Huan, Ziyi Lin, Xialv Brain Sci Article In neuroscience, the Default Mode Network (DMN), also known as the default network or the default-state network, is a large-scale brain network known to have highly correlated activities that are distinct from other networks in the brain. Many studies have revealed that DMNs can influence other cognitive functions to some extent. This paper is motivated by this idea and intends to further explore on how DMNs could help Spiking Neural Networks (SNNs) on image classification problems through an experimental study. The approach emphasizes the bionic meaning on model selection and parameters settings. For modeling, we select Leaky Integrate-and-Fire (LIF) as the neuron model, Additive White Gaussian Noise (AWGN) as the input DMN, and design the learning algorithm based on Spike-Timing-Dependent Plasticity (STDP). Then, we experiment on a two-layer SNN to evaluate the influence of DMN on classification accuracy, and on a three-layer SNN to examine the influence of DMN on structure evolution, where the results both appear positive. Finally, we discuss possible directions for future works. MDPI 2021-01-25 /pmc/articles/PMC7911228/ /pubmed/33503833 http://dx.doi.org/10.3390/brainsci11020153 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Jing Yang, Xu Zhu, Yimeng Lei, Yunlin Cai, Jian Wang, Miao Huan, Ziyi Lin, Xialv How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title | How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title_full | How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title_fullStr | How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title_full_unstemmed | How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title_short | How Neuronal Noises Influence the Spiking Neural Networks’s Cognitive Learning Process: A Preliminary Study |
title_sort | how neuronal noises influence the spiking neural networks’s cognitive learning process: a preliminary study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911228/ https://www.ncbi.nlm.nih.gov/pubmed/33503833 http://dx.doi.org/10.3390/brainsci11020153 |
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