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Neuromorphic Artificial Touch for Categorization of Naturalistic Textures.

, , and . IEEE Trans. Neural Networks Learn. Syst., 28 (4): 819-829 (2017)

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Neuromorphic Artificial Sense of Touch: Bridging Robotics and Neuroscience., , , , and . ISRR (2), volume 3 of Springer Proceedings in Advanced Robotics, page 617-630. Springer, (2015)Piezoelectric Energy Harvesting Solutions., , , , , , and . Sensors, 14 (3): 4755-4790 (2014)Neuromorphic Artificial Touch for Categorization of Naturalistic Textures., , and . IEEE Trans. Neural Networks Learn. Syst., 28 (4): 819-829 (2017)Dynamic neuronal networks efficiently achieve classification in robotic interactions with real-world objects., , , , and . CoRR, (2022)A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks., , , , and . Frontiers Comput. Neurosci., (2021)Rich dynamics caused by known biological brain network features resulting in stateful networks., and . CoRR, (2021)Tactile Decoding of Edge Orientation With Artificial Cuneate Neurons in Dynamic Conditions., , , , , , , , , and . Frontiers Neurorobotics, (2019)Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions., , , , and . Neural Networks, (2020)