ESR7 - A Spiking Model of Realistic Human Tactile Interactions

This project aims to improve current population spiking models of peripheral tactile responses such that they can be directly coupled to an appropriate touch sensor and deliver realistic tactile feedback in real-time.

Overview

Objectives

This project aims to improve current population spiking models of peripheral tactile responses such that they can be directly coupled to an appropriate touch sensor and deliver realistic tactile feedback in real-time. To this end, we will develop and validate a robust computational method that translates sensor input into spiking output, as well as modifying existing spiking models to deliver real-time output. Additionally, the touch sensors will be integrated into a setup that tracks human hand movements and contact forces, and we will collect a data set of human touch interactions during object grasping and manipulation. This data set will provide novel insights into how humans exploit their sense of touch and which tactile features underlie manipulation and grasping skills.

Expected Results

On the technical side, this project will deliver a method to convert touch sensor activations into realistic tactile population responses. These results will feed into neuroprosthetics and robotics, specifically through the secondment at UNIBI. On the scientific side, this project will enable a better understanding of how human use touch, and specifically which features are important for grasping and manipulation: these results will therefore guide future development of neuroprosthetic feedback algorithms and novel tactile sensors.

Secondments

UGOT

study transduction in tactile afferents
IIT-CNCS

learn data analysis, feature extraction and information theory
UNIBI

implement neural representation of tactile stimuli

Supervisors

H. Saal
J. Wessberg
S. Panzeri
E. Chicca

Giulia Corniani

I have always been fascinated by the complexity and mysteriousness of the human brain. I first got closer to neuroscience during my master thesis traineeship working with Brain-Computer Interfaces. After that experience, I knew Neuroscience was definitely my topic

Neutouch for me

The thing I like the most about this project is that we are a melting pot of backgrounds, cultures, and experiences. The best characteristic of NeuTouch is its multidisciplinarity: we face the same issue from different perspectives and merging our knowledge we might be able to reach innovative and outstanding solutions.

Info

Research Topics

Biology & Computational Neuroscience
Institution

University of Sheffield
Background

B.Sc. Information engineering (Università degli Studi di Padova, 2013- 2016)
M.Sc. Bioengineering (Università degli Studi di Padova, 2016-2018)

Thesis

Mechanotransduction and information coding in the human peripheral tactile system. University of Sheffield, 2023.

Abstract

The human peripheral tactile system is responsible for the initial processing of tactile stimuli and is composed of the skin and various embedded mechanoreceptors innervated by afferents. Spiking models are widely used to characterize this system and infer how populations of afferents shape tactile perception. Leveraging existing models of tactile afferents and moved by their limitations, we present three studies designed to advance these essential tools in the investigation of the human peripheral tactile system.

Firstly, reconciling existing evidence, we quantitatively characterize the population of peripheral tactile afferents. We estimate that approximately 230,000 afferents cover the human body, provide innervation densities in different skin areas, and show the relation of these numbers with tactile acuity, hair follicle density, and somatosensory cortical representation.

Secondly, we ask how tactile afferents work together to encode information in complex ways. We find that information is spread across classes, and combining information from multiple classes improves transmission. We test the importance of temporal and spatial resolution in the population code, probing that destroying temporal information is more destructive than removing spatial information.

Finally, we use Optical Coherence Tomography to image the skin subsurface in vivo and dynamically and quantify the deformation of individual fingerprint ridges down to the type-1 mechanoreceptors' location. When scanning the skin with a flat surface, the ridge deforms as a single unit. Higher strains emerge from the stick-to-slip transition compared to plate movement reversal. When scanning the skin with small features, different ridge sub-units experience different strain patterns. Higher strains occur in the deepest layer imaged.

Overall, this research provides a better understanding of coding strategies of tactile afferents on a population level and of the link between skin mechanics and transduction mechanisms underlying tactile perception. Our findings will have implications for developing novel spiking models of the human peripheral tactile system.

Publications

Corniani, G., & Saal, H. P. (2020). Tactile innervation densities across the whole body. Journal of Neurophysiology, 124(4), 1229-1240.

Corniani, G., Casal, M. A., Panzeri, S., & Saal, H. P. (2022). Population coding strategies in human tactile afferents. PLOS Computational Biology, 18(12), e1010763.

Corniani, G., Lee, Z. S., Carré, M. J., Lewis, R., Delhaye, B. P., & Saal, H. P. (2024). Sub-surface deformation of individual fingerprint ridges during tactile interactions. eLife, 13:RP93554.

Corniani, G. (2022, May). Imaging Sub-surface Skin Strain Patterns During Fingertip Sliding. In Haptics: Science, Technology, Applications: 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, Hamburg, Germany, May 22–25, 2022, Proceedings (Vol. 13235, p. 358). Springer Nature.

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