title: Inference from populations: going beyond models.
abstract: How are abstract signals, like intent, represented in neural populations? By creating a direct link between neural activity and behavior, brain-computer interfaces (BCIs) can help answer this question. Early instantiations of these devices sought mainly to mimic arm movements: by building models of arm tuning for the neurons, desired arm movements could be read out and used to control various prosthetic devices. However, as the functionality of these devices increases, a more general approach that relies less on endogenous control signals may be required. Here we review some of the current, model-based approaches for finding volitional control signals for spiking-based BCIs, and present some new approaches for finding control signals without resorting to parametric models of neural activity.
authors.affiliation: Department of Neurobiology and the Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Chase
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Schwartz
mesh_terms: D001132:Arm / Q000502:physiology; D001519:Behavior / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009068:Movement / Q000502:physiology; D019736:Prostheses and Implants; D014584:User-Computer Interface