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Physical reservoir computing is a type of recurrent neural network thatapplies the dynamical response from physical systems to information processing.However, the relation between computation performance and physicalparameters/phenomena still remains unclear. This study reports our progressregarding the role of current-dependent magnetic damping in the computationalperformance of reservoir computing. The current-dependent relaxation dynamicsof a magnetic vortex core results in an asymmetric memory function with respectto binary inputs. A fast relaxation caused by a large input leads to a fastfading of the input memory, whereas a slow relaxation by a small input enablesthe reservoir to keep the input memory for a relatively long time. As a result,a step-like dependence is found for the short-term memory and parity-checkcapacities on the pulse width of input data, where the capacities remain at 1.5for a certain range of the pulse width, and drop to 1.0 for a long pulse-widthlimit. Both analytical and numerical analyses clarify that the step-likebehavior can be attributed to the current-dependent relaxation time of thevortex core to a limit-cycle state. }

Step-like dependence of memory function on pulse width in spintronics reservoir computing