论文标题
片上学习的视觉模式识别：迈向完全神经形态的方法
Visual Pattern Recognition with on On-chip Learning: towards a Fully Neuromorphic Approach
论文作者
论文摘要
我们提出了一个尖峰神经网络（SNN），可通过对神经phichardware的芯片学习进行视觉模式识别。我们展示了该网络如何使用局部基于尖峰的可塑性规则来学习由动态视觉传感器感知的水平和垂直条组成的简单视觉模式。在识别过程中，网络对模式的身份进行了分类，同时估计其位置和规模。我们以先前的工作为基础，该工作将学习与循环中的神经形态硬件一起使用，并证明拟议的网络可以通过片上学习正确地运行，并展示了完整的神经形态模式学习和识别设置。我们的结果表明，该网络在输入上的噪声（添加130％的噪声时没有准确性下降）和神经元参数中多达20％的噪声。
We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphichardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sensor, using a local spike-based plasticity rule. During recognition, the network classifies the pattern's identity while at the same time estimating its location and scale. We build on previous work that used learning with neuromorphic hardware in the loop and demonstrate that the proposed network can properly operate with on-chip learning, demonstrating a complete neuromorphic pattern learning and recognition setup. Our results show that the network is robust against noise on the input (no accuracy drop when adding 130% noise) and against up to 20% noise in the neuron parameters.
