English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/171298
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

A neural population mechanism for rapid learning

AuthorsPerich, Matthew G.; Gallego, Juan Álvaro ; Miller, Lee E.
Keywordsmotor cortex
premotor cortex
motor control
motor learning
movement planning
neural populations
single neurons
neural manifolds
null space
computational neuroscience
Issue Date17-May-2017
AbstractLong-term learning of language, mathematics, and motor skills likely requires plastic changes in the cortex, but behavior often requires faster changes, sometimes based even on single errors. Here, we show evidence of one mechanism by which the brain can rapidly develop new motor output, seemingly without altering the functional connectivity between or within cortical areas. We recorded simultaneously from hundreds of neurons in the premotor (PMd) and primary motor (M1) cortices, and computed models relating these neural populations throughout adaptation to reaching movement perturbations. We found a signature of learning in the ″null subspace″ of PMd with respect to M1. Earlier experiments have shown that null subspace activity allows the motor cortex to alter preparatory activity without directly influencing M1. In our experiments, the null subspace planning activity evolved with the adaptation, yet the ″potent mapping″ that captures information sent to M1 was preserved. Our results illustrate a population-level mechanism within the motor cortices to adjust the output from one brain area to its downstream structures that could be exploited throughout the brain for rapid, on-line behavioral adaptation. [ENG]
DescriptionThis article is a preprint and has not been peer-reviewed
Publisher version (URL)https://doi.org/10.1016/ j.neuron.2018.09.030
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
Perich_Neural_Neuron_2018_preprint.pdfPre-print2,56 MBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.