A neural surveyor to map touch on the body
| Autor: | Luke E. Miller, Cécile Fabio, Malika Azaroual, Dollyane Muret, Robert J. van Beers, Alessandro Farnè, W. Pieter Medendorp |
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| Přispěvatelé: | Sensorimotor Control, IBBA, AMS - Sports |
| Rok vydání: | 2022 |
| Předmět: |
computation
Adult neural network Models Neurological Somatosensory somatosensory Mice Young Adult Physical Stimulation Tactile localization Animals Humans Neurons Brain Mapping Multidisciplinary Action intention and motor control Bayes Theorem Somatosensory Cortex Biological Sciences Neural network Touch Perception Touch Psychological and Cognitive Sciences Computation Female Neural Networks Computer tactile localization |
| Zdroj: | Proceedings of the National Academy of Sciences USA, 119, 1 Miller, L E, Fabio, C, Azaroual, M, Muret, D, van Beers, R J, Farne, A & Pieter Medendorp, W 2022, ' A neural surveyor to map touch on the body ', Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 1, e2102233118, pp. 1-12 . https://doi.org/10.1073/pnas.2102233118 Proceedings of the National Academy of Sciences of the United States of America Proceedings of the National Academy of Sciences of the United States of America, 119(1):e2102233118, 1-12. National Acad Sciences Proceedings of the National Academy of Sciences USA, 119 |
| ISSN: | 0027-8424 |
| DOI: | 10.1073/pnas.2102233118 |
| Popis: | 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. Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although 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 position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning 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 neural network, which captures several fundamental receptive field properties of cortical somatosensory 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 computational mechanism underlying tactile localization. |
| Databáze: | OpenAIRE |
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