Work

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Year: 2025

Type: article

Abstract: Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.

Source: APL Bioengineering

DOI: 10.1063/5.0249434

Authors: Franklin Leong, Silvestro Micera, Solaiman Shokur

Institutions: École Polytechnique Fédérale de Lausanne, Vita-Salute San Raffaele University, Scuola Superiore Sant'Anna, Centre Hospitalier Universitaire Vaudois, University of Lausanne

Language: English

Topic: Neuroscience and Neural Engineering

Subfield: Cellular and Molecular Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,200

Funders: National Science Foundation, Fondation Bertarelli, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, HORIZON EUROPE Framework Programme