title: A new look at state-space models for neural data.
abstract: State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.
authors: Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA. liam@stat.columbia.edu.
authors: Liam
authors: L
authors: Paninski
authors: Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
authors: Yashar
authors: Y
authors: Ahmadian
authors: Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
authors: Daniel Gil
authors: DG
authors: Ferreira
authors: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA.
authors: Shinsuke
authors: S
authors: Koyama
authors: Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
authors: Kamiar
authors: K
authors: Rahnama Rad
authors: Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
authors: Michael
authors: M
authors: Vidne
authors: Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
authors: Joshua
authors: J
authors: Vogelstein
authors: Department of Statistics, Florida State University, Tallahassee, FL, USA.
authors: Wei
authors: W
authors: Wu
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D012165:Retinal Ganglion Cells / Q000502:physiology; D013569:Synapses / Q000502:physiology