A neural surveyor to map touch on the body
Résumé
Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory ho-munculus. Though it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its posi-tion within the entire map, but how this is computed by neural networks is unknown. We pro-pose that the somatosensory system implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neu-ral network, which captures several fundamental receptive field properties of cortical soma-tosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate com-putational mechanism underlying tactile localization.
Domaines
Sciences du Vivant [q-bio]
Licence : CC BY NC ND - Paternité - Pas d'utilisation commerciale - Pas de modification