An EM algorithm for estimation in Markov-modulated Poisson processes
Computational Statistics & Data Analysis
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient greedy learning of Gaussian mixture models
Neural Computation
Estimating a state-space model from point process observations
Neural Computation
Dynamic Programming
Variational Learning for Switching State-Space Models
Neural Computation
IEEE Transactions on Information Theory
The computational structure of spike trains
Neural Computation
Detection of bursts in extracellular spike trains using hidden semi-Markov point process models
Journal of Computational Neuroscience
Detection of hidden structures in nonstationary spike trains
Neural Computation
Uncovering spatial topology represented by rat hippocampal population neuronal codes
Journal of Computational Neuroscience
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UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete-and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi-unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.