Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

State-Space Models for the Analysis of Neural Spike Train and Behavioral Data

  • Zhe Chen
  • Emery N. Brown
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_410-1

Definition

An adaptation of the state-space paradigm to the analysis of neuroscience data in which the observation model is either a point process or a time series of binary observations and the state model is typically a linear Gaussian process. The paradigm has been applied to a number of problems including neural spike train decoding, analysis of receptive field dynamics, analyses of learning, neural prosthetic control, and control of brain states under anesthesia.

The state-space paradigm for analyses of point processes and time series of discrete binary observations has been developed for the analysis of neural spike train and behavioral data (Brown et al. 1998; Smith and Brown 2003). The state-space point process (SSPP) paradigm has two standard components. The state equation defines the system dynamics. The observation equation defines how the system is measured. For the SSPP system the observations can be point processes or time series of discrete binary responses. Point...

Keywords

Point Process Model Predictive Control Brain Machine Interface Model Predictive Control Algorithm Sequential Monte Carlo Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute for Medical Engineering and ScienceMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of Anesthesia, Critical Care and Pain MedicineMassachusetts General Hospital, Harvard Medical SchoolBostonUSA