State-Space Models for the Analysis of Neural Spike Train and Behavioral Data
- 215 Downloads
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...
KeywordsPoint Process Model Predictive Control Brain Machine Interface Model Predictive Control Algorithm Sequential Monte Carlo Method
- Chen Z, Barbieri R, EN Brown (2010) State-space modeling of neural spike train and neural behavioral data. In: Statistical signal processing for neuroscience: Oweiss. Oxford Press, Amsterdam, pp 161–200Google Scholar
- Ching S, Liberman MY, Chemali JJ et al (2013) Real-time closed-loop control in a rodent model of medically induced coma using burst suppression. Anesthesiology 119(4):848–860Google Scholar
- Coleman TP, Yanike M, Suzuki WA, Brown EN (eds) (2010) A mixed filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures. In: Glanzman DL, Ding M (eds) Neuronal variability and its functional significance. Oxford University Press, New York, pp 3–28Google Scholar
- Shanechi M, Chemali JJ, Liberman M, Solt K, Brown EN (2013) A brain-machine interface for control of medically-induced coma. PLoS Comput Biol: e1003284 Google Scholar
- Shanechi MM, Orsborn A, Gowda S, Carmena JM (2013) Proficient BMI control enabled by closed-loop adaptation of an optimal feedback-controlled point process decoder. In: Translational and computational motor control meeting, San Diego, 8 Nov 2013Google Scholar
- Wong KF, Smith AC, Pierce ET et al (2014) Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness. J Neurosci Methods 227c: 65–74Google Scholar