ArticleProceedings of the National Academy of Sciences of the United States of America2019
Learning active sensing strategies using a sensory brain-machine interface.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integration of learned artificial sensation with vision during freely moving navigation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Intracortical Microstimulation in Brain-Computer Interfaces: Evoking Perception and Plasticity.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Article
- Neural Plasticity in Sensorimotor Brain-Machine Interfaces.Annual review of biomedical engineering · 2023Review
- Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes.Journal of neural engineering · 2021Article
- Backward spatial perception can be augmented through a novel visual-to-auditory sensory substitution algorithm.Scientific reports · 2021Article
- Improving scalability in systems neuroscience.Neuron · 2021Review
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Authors and funding
12 authors.
Funding
Abstract
Diverse organisms, from insects to humans, actively seek out sensory information that best informs goal-directed actions. Efficient active sensing requires congruity between sensor properties and motor strategies, as typically honed through evolution. However, it has been difficult to study whether active sensing strategies are also modified with experience. Here, we used a sensory brain-machine interface paradigm, permitting both free behavior and experimental manipulation of sensory feedback, to study learning of active sensing strategies. Rats performed a searching task in a water maze in which the only task-relevant sensory feedback was provided by intracortical microstimulation (ICMS) encoding egocentric bearing to the hidden goal location. The rats learned to use the artificial goal direction sense to find the platform with the same proficiency as natural vision. Manipulation of the acuity of the ICMS feedback revealed distinct search strategy adaptations. Using an optimization model, the different strategies were found to minimize the effort required to extract the most salient task-relevant information. The results demonstrate that animals can adjust motor strategies to match novel sensor properties for efficient goal-directed behavior.
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Registered trials
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