论文标题
深度无监督的特征学习尖峰神经网络，具有二进制分类层，用于使用spykeflow的EMNIST分类
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
论文作者
论文摘要
最终用户AI经过大型服务器农场的培训，并从用户收集了数据。随着对物联网设备的需求不断增长，需要以节能的方式（在边缘）实施深度学习方法。在这项工作中，我们使用尖峰神经网络对此进行处理。使用二进制激活的峰值定时依赖性可塑性（STDP）的无监督学习技术用于从峰值输入数据中提取特征。梯度下降（反向传播）仅在输出层上使用以执行分类训练。平衡EMNIST数据集获得的精度与其他方法相比有利。还探讨了随机梯度下降（SGD）近似对我们网络学习能力的影响。
End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IOT devices, there is a need for deep learning approaches that can be implemented (at the edge) in an energy efficient manner. In this work we approach this using spiking neural networks. The unsupervised learning technique of spike timing dependent plasticity (STDP) using binary activations are used to extract features from spiking input data. Gradient descent (backpropagation) is used only on the output layer to perform the training for classification. The accuracies obtained for the balanced EMNIST data set compare favorably with other approaches. The effect of stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored.
