Optimization frameworks for bespoke sensory encoding in neuroprosthetics
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 neu...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | APL Bioengineering |
| Online Access: | http://dx.doi.org/10.1063/5.0249434 |
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| author | Franklin Leong Silvestro Micera Solaiman Shokur |
| author_facet | Franklin Leong Silvestro Micera Solaiman Shokur |
| author_sort | Franklin Leong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-77eef45290ac484a96fb7f8788652275 |
| institution | DOAJ |
| issn | 2473-2877 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Bioengineering |
| spelling | doaj-art-77eef45290ac484a96fb7f87886522752025-08-20T03:14:57ZengAIP Publishing LLCAPL Bioengineering2473-28772025-06-0192020901020901-910.1063/5.0249434Optimization frameworks for bespoke sensory encoding in neuroprostheticsFranklin Leong0Silvestro Micera1Solaiman Shokur2Translational Neural Engineering Laboratory (TNE Lab), Neuro-X Institute, EPFL, Geneva, SwitzerlandTranslational Neural Engineering Laboratory (TNE Lab), Neuro-X Institute, EPFL, Geneva, SwitzerlandTranslational Neural Engineering Laboratory (TNE Lab), Neuro-X Institute, EPFL, Geneva, SwitzerlandRestoring 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.http://dx.doi.org/10.1063/5.0249434 |
| spellingShingle | Franklin Leong Silvestro Micera Solaiman Shokur Optimization frameworks for bespoke sensory encoding in neuroprosthetics APL Bioengineering |
| title | Optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| title_full | Optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| title_fullStr | Optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| title_full_unstemmed | Optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| title_short | Optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| title_sort | optimization frameworks for bespoke sensory encoding in neuroprosthetics |
| url | http://dx.doi.org/10.1063/5.0249434 |
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