论文标题
具有近似反向传播的时间编码的尖峰神经网络中的监督学习
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
论文作者
论文摘要
在这项工作中，我们为时间编码的多层尖峰网络提出了一种新的监督学习方法，以执行分类。该方法采用了模仿反向传播的增强信号，但计算密集程度要少得多。除此信号外，每一层的重量更新计算仅需要本地数据。我们还采用了能够生产特定输出尖峰火车的规则；通过将目标尖峰时间设置为实际的尖峰时间，而关键高价值神经元的略有负偏移，实际的尖峰时间尽可能尽早。在模拟的MNIST手写数字分类中，经过此规则训练的两层网络与基于可比反向传播的非加速网络的性能相匹配。
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal that mimics backpropagation but is far less computationally intensive. The weight update calculation at each layer requires only local data apart from this signal. We also employ a rule capable of producing specific output spike trains; by setting the target spike time equal to the actual spike time with a slight negative offset for key high-value neurons the actual spike time becomes as early as possible. In simulated MNIST handwritten digit classification, two-layer networks trained with this rule matched the performance of a comparable backpropagation based non-spiking network.
