title: Structural constraints on learning in the neural network.
abstract: Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues (Nature 512: 423-426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.
authors: Division of Biokinesiology and Physical Therapy, University of Southern California. Los Angeles, California; and clarisa@usc.edu.
authors: Clarisa A
authors: http://orcid.org/0000-0001-8529-0016
authors: CA
authors: Martinez
authors: Neuroscience Graduate Program, University of Southern California, Los Angeles, California.
authors: Chunji
authors: C
authors: Wang
mesh_terms: D001921:Brain; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D014584:User-Computer Interface
keywords: brain-computer interface; learning/physiology; motor cortex; stroke; structural learning