Liu Jing, Yang Xu, Zhu Yimeng, Lei Yunlin, Cai Jian, Wang Miao, Huan Ziyi, Lin Xialv
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Brain Sci. 2021 Jan 25;11(2):153. doi: 10.3390/brainsci11020153.
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.
在神经科学中，默认模式网络（DMN），也被称为默认网络或默认状态网络，是一个大规模的脑网络，已知其具有高度相关的活动，这些活动与大脑中的其他网络不同。许多研究表明，默认模式网络在一定程度上可以影响其他认知功能。本文受此想法启发，旨在通过实验研究进一步探索默认模式网络如何在图像分类问题上帮助脉冲神经网络（SNN）。该方法在模型选择和参数设置上强调仿生意义。对于建模，我们选择泄漏积分发放（LIF）作为神经元模型，加性高斯白噪声（AWGN）作为输入默认模式网络，并基于脉冲时间依赖可塑性（STDP）设计学习算法。然后，我们在一个两层脉冲神经网络上进行实验，以评估默认模式网络对分类准确率的影响，并在一个三层脉冲神经网络上进行实验，以检验默认模式网络对结构演化的影响，结果均呈积极。最后，我们讨论了未来工作的可能方向。
