# Optimization frameworks for bespoke sensory encoding in neuroprosthetics

## TL;DR

This paper explores methods to optimize sensory feedback in neuroprosthetics by using three different frameworks to efficiently find effective stimulation parameters.

## Contribution

The novelty lies in proposing three optimization frameworks—explicit, physiological, and self-optimized—to enhance sensory encoding in neuroprosthetics.

## Key findings

- Three optimization frameworks are introduced to improve sensory encoding in neuroprosthetics.
- These frameworks are applicable to various sensory systems beyond somatosensation.
- The frameworks aim to reduce the need for impractical brute-force approaches in parameter selection.

## 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.