Revisiting the memory capacity in reservoir computing of directed acyclic network

Reservoir computing (RC) is an attractive area of research by virtue of its potential for hardware implementation and low training cost. An intriguing research direction in this field is to interpret the underlying dynamics of an RC model by analyzing its short-term memory property, which can be quantified by the global index: Memory capacity (MC). In this paper, the global MC of the RC whose reservoir network is specified as a directed acyclic network (DAN) is examined, and first we give that its global MC is theoretically bounded by the length of the longest path of the reservoir DAN. Since the global MC is technically influenced by the model hyperparameters, the dependency of the MC on the hyperparameters of this RC is then explored in detail. In the further study, we employ the improved conventional network embedding method (i.e., struc2vec) to mine the underlying memory community in the reservoir DAN, which can be regarded as the cluster of reservoir nodes with the same memory profile. Experimental results demonstrate that such a memory community structure can provide a concrete interpretation of the global MC of this RC. Finally, the clustered RC is proposed by exploiting the detected memory community structure of DAN, where its prediction performance is verified to be enhanced with lower training cost compared with other RC models on several chaotic time series benchmarks.