An information theoretic parameter tuning for MEMS-based reservoir computing

抄録

With respect to the next frontier of neuromorphic sensing, we propose a parameter tuning method based on mutual information criteria for MEMS-based reservoir computing. It is required for MEMS reservoirs to tune the balance of the linear and nonlinear characteristics and to control their dynamical behaviors depending on driving forces, such as chaos and hysteresis. We focus on a pre-training method for machine learning called the intrinsic plasticity (IP) learning, and apply it to controlling the dynamical behaviors of MEMS reservoirs. First, we demonstrate simulation results for chaos suppression. Next, we applied our IP learning to parameter tuning of the MEMS-based reservoir Finally, we show that our approach can improve prediction accuracy in nonlinear transformation tasks.