A redundant encoding algorithm for artificial sensory information speeds learning and improves multisensory-guided navigation
- 1Purdue University
Abstract
Intracortical microstimulation (ICMS) directly modulates cortical activity, providing an artificial sensory stream to guide accurate control of prosthetic limbs. Yet, only a fraction of reported sensations evoked by ICMS are proprioceptive (i.e., describing the position and movement of the body). Taking a learning-based approach to encoding an artificial proprioceptive signal bypasses this limitation. For example, animals trained with multi-channel ICMS in association with natural vision learn to both decode the ICMS signal to localize an invisible target and learn to integrate artificial sensation with natural sensation. The limitation of a learning-based approach, however, is that it requires training. We hypothesized that changing the algorithm used to encode artificial sensory information could both reduce learning time and improve plateau performance on ICMS-guided navigation. To test this idea, we trained eight mice on a sensory-guided navigation task: mice were required to locate a target in a training cage, the position of which was encoded by a red circle (vision), by multi-channel ICMS (ICMS trials), or by both vision and ICMS (multimodal). ICMS was encoded using one of two algorithms: sparse (fewer simultaneously stimulating electrodes) or redundant (more simultaneously stimulating electrodes). We found that redundant encoding sped learning of the ICMS signal relative to sparse encoding. Further, mice guided by redundant ICMS ran faster and completed trials in less time than when guided by sparse ICMS. Having redundant encoding also facilitated multisensory integration of ICMS with natural vision, improving success rates, path efficiency, movement speed, and movement time. We conclude that optimized ICMS encoding algorithms could overcome the current limitations of learning-based sensory encoding, facilitating learning and integration of artificial sensory information into existing sensorimotor neural circuits, paving the way to restoring both the sensory and motor streams of information flow in damaged sensorimotor systems.
Related
- Redundant information across functionally coupled cortical networks supports rapid perceptual decisions in the ferret
- Compression of redundant visual information improves feature discrimination in human vision
- Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training
- Navigation driven by bidirectional information transmission between sensing and actuation
- An optimization-based waypoint generation method for overcoming local minima in artificial potential field navigation
BibTeX
@misc{dadarlat2026redundant,
title = {{A redundant encoding algorithm for artificial sensory information speeds learning and improves multisensory-guided navigation}},
author = {M. C. Dadarlat and S. Senneka},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.17.752429},
url = {https://www.biorxiv.org/content/10.64898/2026.09.17.752429v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A redundant encoding algorithm for artificial sensory information speeds learning and improves multisensory-guided navigation
AU - M. C. Dadarlat
AU - S. Senneka
PY - 2026
DA - 2026/09/17
PB - bioRxiv
DO - 10.64898/2026.09.17.752429
UR - https://www.biorxiv.org/content/10.64898/2026.09.17.752429v1
AB - Intracortical microstimulation (ICMS) directly modulates cortical activity, providing an artificial sensory stream to guide accurate control of prosthetic limbs. Yet, only a fraction of reported sensations evoked by ICMS are proprioceptive (i.e., describing the position and movement of the body). Taking a learning-based approach to encoding an artificial proprioceptive signal bypasses this limitation. For example, animals trained with multi-channel ICMS in association with natural vision learn to both decode the ICMS signal to localize an invisible target and learn to integrate artificial sensation with natural sensation. The limitation of a learning-based approach, however, is that it requires training. We hypothesized that changing the algorithm used to encode artificial sensory information could both reduce learning time and improve plateau performance on ICMS-guided navigation. To test this idea, we trained eight mice on a sensory-guided navigation task: mice were required to locate a target in a training cage, the position of which was encoded by a red circle (vision), by multi-channel ICMS (ICMS trials), or by both vision and ICMS (multimodal). ICMS was encoded using one of two algorithms: sparse (fewer simultaneously stimulating electrodes) or redundant (more simultaneously stimulating electrodes). We found that redundant encoding sped learning of the ICMS signal relative to sparse encoding. Further, mice guided by redundant ICMS ran faster and completed trials in less time than when guided by sparse ICMS. Having redundant encoding also facilitated multisensory integration of ICMS with natural vision, improving success rates, path efficiency, movement speed, and movement time. We conclude that optimized ICMS encoding algorithms could overcome the current limitations of learning-based sensory encoding, facilitating learning and integration of artificial sensory information into existing sensorimotor neural circuits, paving the way to restoring both the sensory and motor streams of information flow in damaged sensorimotor systems.
ER -