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- Title
How Neuronal Noises Influence the Spiking Neural Networks's Cognitive Learning Process: A Preliminary Study.
- Authors
Liu, Jing; Yang, Xu; Zhu, Yimeng; Lei, Yunlin; Cai, Jian; Wang, Miao; Huan, Ziyi; Lin, Xialv; Höller, Yvonne
- Abstract
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.
- Subjects
COGNITIVE learning; ARTIFICIAL neural networks; ADDITIVE white Gaussian noise; COGNITIVE ability; MACHINE learning
- Publication
Brain Sciences (2076-3425), 2021, Vol 11, Issue 2, p153
- ISSN
2076-3425
- Publication type
Article
- DOI
10.3390/brainsci11020153