title: Latent state-space models for neural decoding.
abstract: Ensembles of single-neurons in motor cortex can show strong low-dimensional collective dynamics. In this study, we explore an approach where neural decoding is applied to estimated low-dimensional dynamics instead of to the full recorded neuronal population. A latent state-space model (SSM) approach is used to estimate the low-dimensional neural dynamics from the measured spiking activity in population of neurons. A second state-space model representation is then used to decode kinematics, via a Kalman filter, from the estimated low-dimensional dynamics. The latent SSM-based decoding approach is illustrated on neuronal activity recorded from primary motor cortex in a monkey performing naturalistic 3-D reach and grasp movements. Our analysis show that 3-D reach decoding performance based on estimated low-dimensional dynamics is comparable to the decoding performance based on the full recorded neuronal population.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Aghagolzadeh
authors.forename: Wilson
authors.initials: W
authors.lastname: Truccolo
mesh_terms: D000818:Animals; D001696:Biomechanical Phenomena; D018737:Hand Strength / Q000502:physiology; D000882:Haplorhini; D008959:Models, Neurological; D008962:Models, Theoretical; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology