Open AccessA neural surveyor to map touch on the body
Author(s) -
Luke Miller,
Cécile Fabio,
Malika Azaroual,
Dollyane Muret,
Robert J. van Beers,
Alessandro Farnè,
W. Pieter Medendorp
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2102233118
Subject(s) - computer science , somatosensory system , multilateration , artificial intelligence , bayes' theorem , computation , artificial neural network , representation (politics) , receptive field , convolutional neural network , sensory system , computer vision , bayesian probability , neuroscience , algorithm , node (physics) , physics , acoustics , psychology , politics , political science , law
Significance Perhaps the most recognizable “sensory map” in neuroscience is the somatosensory homunculus. Although the homunculus suggests a direct link between cortical territory and body part, the relationship is actually ambiguous without a decoder that knows this mapping. How the somatosensory system derives a spatial code from an activation in the homunculus is a longstanding mystery we aimed to solve. We propose that touch location is disambiguated using multilateration, a computation used by surveying and global positioning systems to localize objects. We develop a Bayesian formulation of multilateration, which we implement in a neural network to identify its computational signature. We then detect this signature in psychophysical experiments. Our results suggest that multilateration provides the homunculus-to-body mapping necessary for localizing touch.