Neurocomputational Modelling of Tactile Perception for the Development of Artificial Sense of Touch.

In this thesis, we developed and validated an artificial tactile system by adopting a bio-robotic approach to systematically merge engineering of artificial touch and neurophysiology of the human sense. This objective was achieved in three steps. First, we proved that a biomimetic fingertip implementing neuromorphic representation of tactile stimuli is able to achieve excellent performance in decoding daily life tactile stimuli under varying sensing conditions. Second, we developed a neuronal model emulating the learning processes of the Cuneate Nucleus, the second stage of the human somatosensory pathways. Finally, we integrated these two elements to create a functional biomimetic system towards artificial touch. By means of this approach we achieved three results. We contributed to the development of more efficient upper limb neuroprostheses by characterizing the properties of the biomimetic fingertip. We increased our knowledge about the biological system under investigation: in neuroscience, the mechanisms of different regions are often studied in isolation, and therefore their functions are not fully captured. Our integrated biorobotic system merging low-level tactile sensations with high-level learning, sheds new light on the overall mechanism of sensory processing. Finally, our methodology indicates a novel mode of assumption-free information representation by the brain, which can be exploited to develop robust and effective autonomous sensing systems able to learn feature extraction in the field of biorobotics.