Title |
A new look at state-space models for neural data
|
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Published in |
Journal of Computational Neuroscience, August 2009
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DOI | 10.1007/s10827-009-0179-x |
Pubmed ID | |
Authors |
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreira, Shinsuke Koyama, Kamiar Rahnama Rad, Michael Vidne, Joshua Vogelstein, Wei Wu |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 25 | 6% |
Germany | 6 | 1% |
Japan | 3 | <1% |
United Kingdom | 3 | <1% |
Switzerland | 2 | <1% |
Hungary | 2 | <1% |
France | 2 | <1% |
Finland | 2 | <1% |
Malaysia | 1 | <1% |
Other | 6 | 1% |
Unknown | 362 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 135 | 33% |
Researcher | 93 | 22% |
Student > Master | 26 | 6% |
Student > Doctoral Student | 24 | 6% |
Professor | 24 | 6% |
Other | 73 | 18% |
Unknown | 39 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 92 | 22% |
Neuroscience | 78 | 19% |
Engineering | 69 | 17% |
Computer Science | 36 | 9% |
Mathematics | 32 | 8% |
Other | 54 | 13% |
Unknown | 53 | 13% |