A reservoir activation kernel for trees

We introduce an efficient tree kernel for reservoir computing models exploiting the recursive encoding of the structure in the state activations of the untrained recurrent layer. We discuss how the contractive property of the reservoir induces a topographic organization of the state space that can be used to compute structural matches in terms of pairwise distances between points in the state space. The experimental analysis shows that the proposed kernel is capable of achieving competitive classification results by relying on very small reservoirs comprising as little as 10 sparsely connected recurrent neurons.