CUED Publications database

A new look at state-space models for neural data.

Paninski, L and Ahmadian, Y and Ferreira, DG and Koyama, S and Rahnama Rad, K and Vidne, M and Vogelstein, J and Wu, W (2009) A new look at state-space models for neural data. J Comput Neurosci, 29. pp. 107-126.

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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.

Item Type: Article
Uncontrolled Keywords: Action Potentials Animals Computer Simulation Models, Neurological Models, Statistical Neurons Retinal Ganglion Cells Synapses
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 14 Aug 2020 21:27
Last Modified: 04 Mar 2021 04:00
DOI: 10.1007/s10827-009-0179-x