Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin-based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short-term plasticity with two strength-dependent modes of response are studied: facilitation and facilitation-then-depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model-based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks.
CITATION STYLE
Maraj, J. J., Haughn, K. P. T., Inman, D. J., & Sarles, S. A. (2023). Sensory Adaptation in Biomolecular Memristors Improves Reservoir Computing Performance. Advanced Intelligent Systems, 5(8). https://doi.org/10.1002/aisy.202300049
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