Tackling the Trade-Off Between Information Processing Capacity and Rate in Delay-Based Reservoir Computers

Abstract

We study the role of the system response time in the computational capacity of delay-based reservoir computers. Photonic hardware implementation of these systems offers high processing speed. However, delay-based reservoir computers have a trade-off between computational capacity and processing speed due to the non-zero response time of the non-linear node. The reservoir state is obtained from the sampled output of the non-linear node. We show that the computational capacity is degraded when the sampling output rate is higher than the inverse of the system response time. We find that the computational capacity depends not only on the sampling output rate but also on the misalignment between the delay time of the non-linear node and the data injection time. We show that the capacity degradation due to the high sampling output rate can be reduced when the delay time is greater than the data injection time. We find that this mismatch gives an improvement of the performance of delay-based reservoir computers for several benchmarking tasks. Our results show that the processing speed of delay-based reservoir computers can be increased while keeping a good computational capacity by using a mismatch between delay and data injection times. It is also shown that computational capacity for high sampling output rates can be further increased by using an extra feedback line and delay times greater than the data injection time.