title: Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.
abstract: Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.
authors.affiliation: Department of Biomedical Engineering, Florida University, Gainesville, FL, USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: J C
authors.initials: JC
authors.lastname: Principe
authors.forename: J M
authors.initials: JM
authors.lastname: Carmena
authors.forename: Mikhail A
authors.initials: MA
authors.lastname: Lebedev
authors.forename: M A L
authors.initials: MA
authors.lastname: Nicolelis