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...
This is a preview of subscription content, log in via an institution.
References
Ahmadian Y, Pillow JW, Paninski L (2011) Efficient Markov chain Monte Carlo methods for decoding neural spike trains. Neural Comput 23:46–96
Barbieri R, Brown EN (2006) Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng 53:4–12
Barbieri R, Frank LM, Nguyen DP et al (2004) Dynamic analyses of information encoding in neural ensembles. Neural Comput 16:277–307
Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA (1998) A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J Neurosci 18:7411–7425
Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V (2001) An analysis of neural receptive field plasticity by point process adaptive filtering. Proc Natl Acad Sci U S A 98:12261–12266
Chemali J, Ching S, Purdon PL, Solt K, Brown EN (2013) Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression. J Neural Eng 10:056017
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 161–200
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):848–860
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 3–28
Eden UT, Frank LM, Barbieri R, Solo V, Brown EN (2004) Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput 16:971–998
Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN (2007) Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Trans Biomed Eng 54:419–428
Frank LM, Stanley GB, Brown EN (2004) Hippocampal plasticity across multiple days of exposure to novel environments. J Neurosci 24:7681–7689
Prerau MJ, Smith AC, Eden UT, Yanike M, Suzuki WA, Brown EN (2008) A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance. Biol Cybern 99:1–14
Prerau MJ, Smith AC, Eden UT et al (2009) Characterizing learning by simultaneous analysis of continuous and binary measures of performance. J Neurophysiol 102:3060–3072
Purdon PL, Pierce ET, Mukamel EA et al (2013) Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc Natl Acad Sci U S A 110:E1142–E1151
Shanechi MM, Hu RC, Powers M, Wornell GW, Brown EN, Williams ZM (2012) Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nat Neurosci 15:1715–1722
Shanechi MM, Wornell GW, Williams ZM, Brown EN (2013a) Feedback-controlled parallel point process filter for estimation of goal-directed movements from neural signals. IEEE Trans Neural Syst Rehabil Eng 21:129–140
Shanechi MM, Williams ZM, Wornell GW, Hu RC, Powers M, Brown EN (2013b) A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design. PLoS One 8:e59049
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
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 2013
Shimazaki H, Amari S, Brown EN, Grun S (2012) State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLoS Comput Biol 8:e1002385
Smith AC, Brown EN (2003) Estimating a state-space model from point process observations. Neural Comput 15:965–991
Smith AC, Frank LM, Wirth S et al (2004) Dynamic analysis of learning in behavioral experiments. J Neurosci 24:447–461
Truccolo W, Eden UT, Fellows MR, Donoghue JP, Brown EN (2005) A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J Neurophysiol 93:1074–1089
Wirth S, Yanike M, Frank LM, Smith AC, Brown EN, Suzuki WA (2003) Single neurons in the monkey hippocampus and learning of new associations. Science 300:1578–1581
Wong KF, Smith AC, Pierce ET et al (2014) Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness. J Neurosci Methods 227c: 65–74
Yuan K, Girolami M, Niranjan M (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Comput 24:1462–1486
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Chen, Z., Brown, E.N. (2014). State-Space Models for the Analysis of Neural Spike Train and Behavioral Data. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_410-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_410-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences