Rank-based Hebbian learning in a multilayered neural network
Recent work on biologically motivated networks have shown that the visual system can process a natural scene more quickly by encoding the order of neural firing rather than the frequency of firing. This `order of firing' encoding scheme has led to a rank-based approach which converts activation energy into a time-dependent pulse code. This paper focuses towards the contribution of unsupervised learning to the training of integrate and fire neurons within multi-layer networks. First, we propose an unsupervised learning algorithm and we test it on a simple recognition task. Then, we propose a multilayer architecture of integrate and fire neurons to solve a more complex vision task. This architecture is efficiently trained by an algorithm combining supervised and unsupervised rank-based hebbian learning. Further improvements are proposed in the final discussion.