A closed-loop neurobotic system for fine touch sensing

• Published in: Journal of neural engineering Vol. 10; no. 4; p. 046019
• Main Authors: Bologna, L L, Pinoteau, J, Passot, J-B, Garrido, J A, Vogel, J, Vidal, E Ros, Arleo, A
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2013
• Subjects: active sensing; Biofeedback, Psychology - instrumentation; Biofeedback, Psychology - physiology; Biomimetics - instrumentation; Braille; Computer Simulation; Detection; Discrimination; Equipment Design; Equipment Failure Analysis; Feedback, Physiological - physiology; fine touch; Human; Humans; information theory; Mechanoreceptors - physiology; Mechanotransduction, Cellular - physiology; Models, Biological; neural models; neurobotics; Probabilistic methods; Reproducibility of Results; Robotics; Robotics - instrumentation; Sensitivity and Specificity; Signal Processing, Computer-Assisted - instrumentation; Tactile; Touch; Touch - physiology
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2560/10/4/046019

Objective. Fine touch sensing relies on peripheral-to-central neurotransmission of somesthetic percepts, as well as on active motion policies shaping tactile exploration. This paper presents a novel neuroengineering framework for robotic applications based on the multistage processing of fine tactile information in the closed action-perception loop. Approach. The integrated system modules focus on (i) neural coding principles of spatiotemporal spiking patterns at the periphery of the somatosensory pathway, (ii) probabilistic decoding mechanisms mediating cortical-like tactile recognition and (iii) decision-making and low-level motor adaptation underlying active touch sensing. We probed the resulting neural architecture through a Braille reading task. Main results. Our results on the peripheral encoding of primary contact features are consistent with experimental data on human slow-adapting type I mechanoreceptors. They also suggest second-order processing by cuneate neurons may resolve perceptual ambiguities, contributing to a fast and highly performing online discrimination of Braille inputs by a downstream probabilistic decoder. The implemented multilevel adaptive control provides robustness to motion inaccuracy, while making the number of finger accelerations covariate with Braille character complexity. The resulting modulation of fingertip kinematics is coherent with that observed in human Braille readers. Significance. This work provides a basis for the design and implementation of modular neuromimetic systems for fine touch discrimination in robotics.