Faghihi Faramarz, Alashwal Hany, Moustafa Ahmed A
Machine Listening Lab, University of Bremen, Bremen, Germany.
College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates.
Front Artif Intell. 2022 Feb 24;5:680165. doi: 10.3389/frai.2022.680165. eCollection 2022.
A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, "synaptic pruning" and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, "information channels" are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The "information channels" are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images.
我们开发并训练了一种受突触修剪启发的脉冲神经网络模型，用于提取手写数字的特征。该网络由三个脉冲神经层和一个输出神经元组成，其放电率用于分类。该模型从美国国家标准与技术研究院修改后的数据库（MNIST）中检测并收集图像的几何特征。在这项工作中，我们开发了一种新颖的学习规则来训练网络，以检测不同数字类别的特征。为此，使用突触前和突触后神经元的平均放电率更新第一层和第二层之间随机初始化的突触权重。然后，使用一种名为“突触修剪”的受神经科学启发的机制及其预定义的阈值，删除一些突触。因此，构建了这些名为“信息通道”的稀疏矩阵，使其作为第一层和第二层之间的连接矩阵，显示出每个数字类别的高度特定模式。在测试阶段，使用“信息通道”为每个测试图像分配一个数字类别。此外，还研究了反馈抑制的作用以及第二和第三神经层的连接率。与人类从小规模训练试验中学习的能力类似，与在MNIST数据集上表现出色的传统深度学习方法相比，所开发的脉冲神经网络在训练时需要非常小的数据集。这项工作引入了一类新型的受大脑启发的脉冲神经网络，以提取复杂数据图像的特征。
