Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees

Abstract

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

Sensitivity to temporal parameters of intraneural tactile sensory feedback

“…Interestingly, the temporal parameters of neural stimulation are crucial to optimally exploit this kind of technology. Indeed, the modulation of neural stimulation at temporal, spatial, and intensity levels allowed to control grasping force [ 6 , 7 , 10 ], to perceive more natural sensations [ 11 ], to discriminate textures [ 18 , 19 ], and to identify the physical properties of objects such as compliance and shape [ 7 , 10 , 20 ]. In particular, in order to recognize three objects with different compliances and shapes, the subject exploited the intra-digit and inter-digit temporal differences of the neural stimulation, respectively [ 7 ].…”

Fiber Bragg Gratings for Medical Applications and Future Challenges: A Review

“…Such a study, combined with other ones focused on robotic manipulators equipped with FBGs for tactile sensing [74], [75] opens up to future applications of FBGs in bionics. Indeed, FBG-based sensory feedback could enable closedloop control of the prosthesis, improving its embodiment, amputee's recovery functions, and engagement with the surroundings going beyond the results achieved with MEMS sensors [76], [77].…”

A modular strategy for next-generation upper-limb sensory-motor neuroprostheses

“…136,137 One complimentary key addition to algorithms (performed offline or during the training phase) is the simulation of the neural structures receiving the stimulations, usually in combination with finite-element modeling of the electrode structure to optimize the stimulation location/pattern for feedback (see Romeni et al 138 for the PNS and Kumaravelu et al 139 for the CNS). Different sensations have been reported with different encoding strategies, such as force-and-stiffness feedback when using a linear algorithm modulating impulse amplitude, 49 roughness, 140 and texture irregularity, 141 when combined with a biomimetic algorithm generating the pulses with a model of single mechanoreceptor behavior. 136 One study focused on the advantages of using different algorithms, 142 concluding that the best option was to generate pulse times according to a model of mechanoreceptor populations 137 and to modulate pulse amplitude according to skin indentation.…”

“…69,87,168 LIFEs were also used for both motor decoding and to convey sensory input. 169,170 TIMEs are another interesting example because they have been extensively tested in the PNS for encoding sensory feedback, 49,[140][141][142]150,171 as well as for recording, 163,172 and recently, even as an actuation module. 88 Indeed, a study with nonhuman primate upper-limb paralysis showed the restoration of grasping function using intrafascicular stimulation.…”